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# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
PATH_TO_DIFFUSERS = "src/diffusers"
# Matches is_xxx_available()
_re_backend = re.compile(r"is\_([a-z_]*)_available\(\)")
# Matches from xxx import bla
_re_single_line_import = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
DUMMY_CONSTANT = """
{0} = None
"""
DUMMY_CLASS = """
class {0}(metaclass=DummyObject):
_backends = {1}
def __init__(self, *args, **kwargs):
requires_backends(self, {1})
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, {1})
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, {1})
"""
DUMMY_FUNCTION = """
def {0}(*args, **kwargs):
requires_backends({0}, {1})
"""
def find_backend(line):
"""Find one (or multiple) backend in a code line of the init."""
backends = _re_backend.findall(line)
if len(backends) == 0:
return None
return "_and_".join(backends)
def read_init():
"""Read the init and extracts PyTorch, TensorFlow, SentencePiece and Tokenizers objects."""
with open(os.path.join(PATH_TO_DIFFUSERS, "__init__.py"), "r", encoding="utf-8", newline="\n") as f:
lines = f.readlines()
# Get to the point we do the actual imports for type checking
line_index = 0
backend_specific_objects = {}
# Go through the end of the file
while line_index < len(lines):
# If the line is an if is_backend_available, we grab all objects associated.
backend = find_backend(lines[line_index])
if backend is not None:
objects = []
line_index += 1
# Until we unindent, add backend objects to the list
while not lines[line_index].startswith("else:"):
line = lines[line_index]
single_line_import_search = _re_single_line_import.search(line)
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(", "))
elif line.startswith(" " * 8):
objects.append(line[8:-2])
line_index += 1
backend_specific_objects[backend] = objects
else:
line_index += 1
return backend_specific_objects
def create_dummy_object(name, backend_name):
"""Create the code for the dummy object corresponding to `name`."""
if name.isupper():
return DUMMY_CONSTANT.format(name)
elif name.islower():
return DUMMY_FUNCTION.format(name, backend_name)
else:
return DUMMY_CLASS.format(name, backend_name)
def create_dummy_files(backend_specific_objects=None):
"""Create the content of the dummy files."""
if backend_specific_objects is None:
backend_specific_objects = read_init()
# For special correspondence backend to module name as used in the function requires_modulename
dummy_files = {}
for backend, objects in backend_specific_objects.items():
backend_name = "[" + ", ".join(f'"{b}"' for b in backend.split("_and_")) + "]"
dummy_file = "# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
dummy_file += "# flake8: noqa\n\n"
dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
dummy_file += "\n".join([create_dummy_object(o, backend_name) for o in objects])
dummy_files[backend] = dummy_file
return dummy_files
def check_dummies(overwrite=False):
"""Check if the dummy files are up to date and maybe `overwrite` with the right content."""
dummy_files = create_dummy_files()
# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
short_names = {"torch": "pt"}
# Locate actual dummy modules and read their content.
path = os.path.join(PATH_TO_DIFFUSERS, "utils")
dummy_file_paths = {
backend: os.path.join(path, f"dummy_{short_names.get(backend, backend)}_objects.py")
for backend in dummy_files.keys()
}
actual_dummies = {}
for backend, file_path in dummy_file_paths.items():
if os.path.isfile(file_path):
with open(file_path, "r", encoding="utf-8", newline="\n") as f:
actual_dummies[backend] = f.read()
else:
actual_dummies[backend] = ""
for backend in dummy_files.keys():
if dummy_files[backend] != actual_dummies[backend]:
if overwrite:
print(
f"Updating diffusers.utils.dummy_{short_names.get(backend, backend)}_objects.py as the main "
"__init__ has new objects."
)
with open(dummy_file_paths[backend], "w", encoding="utf-8", newline="\n") as f:
f.write(dummy_files[backend])
else:
raise ValueError(
"The main __init__ has objects that are not present in "
f"diffusers.utils.dummy_{short_names.get(backend, backend)}_objects.py. Run `make fix-copies` "
"to fix this."
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
args = parser.parse_args()
check_dummies(args.fix_and_overwrite)
| diffusers-ft-main | utils/check_dummies.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
PATH_TO_TRANSFORMERS = "src/transformers"
# This is to make sure the transformers module imported is the one in the repo.
spec = importlib.util.spec_from_file_location(
"transformers",
os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
transformers = spec.loader.load_module()
CONFIG_MAPPING = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
_re_checkpoint = re.compile("\[(.+?)\]\((https://huggingface\.co/.+?)\)")
CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK = {
"CLIPConfigMixin",
"DecisionTransformerConfigMixin",
"EncoderDecoderConfigMixin",
"RagConfigMixin",
"SpeechEncoderDecoderConfigMixin",
"VisionEncoderDecoderConfigMixin",
"VisionTextDualEncoderConfigMixin",
}
def check_config_docstrings_have_checkpoints():
configs_without_checkpoint = []
for config_class in list(CONFIG_MAPPING.values()):
checkpoint_found = False
# source code of `config_class`
config_source = inspect.getsource(config_class)
checkpoints = _re_checkpoint.findall(config_source)
for checkpoint in checkpoints:
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
ckpt_name, ckpt_link = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
ckpt_link_from_name = f"https://huggingface.co/{ckpt_name}"
if ckpt_link == ckpt_link_from_name:
checkpoint_found = True
break
name = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(name)
if len(configs_without_checkpoint) > 0:
message = "\n".join(sorted(configs_without_checkpoint))
raise ValueError(f"The following configurations don't contain any valid checkpoint:\n{message}")
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| diffusers-ft-main | utils/check_config_docstrings.py |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import importlib.util
import os
import re
from pathlib import Path
PATH_TO_TRANSFORMERS = "src/transformers"
# Matches is_xxx_available()
_re_backend = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
_re_one_line_import_struct = re.compile(r"^_import_structure\s+=\s+\{([^\}]+)\}")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
_re_import_struct_key_value = re.compile(r'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
_re_test_backend = re.compile(r"^\s*if\s+not\s+is\_[a-z_]*\_available\(\)")
# Catches a line _import_struct["bla"].append("foo")
_re_import_struct_add_one = re.compile(r'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
_re_import_struct_add_many = re.compile(r"^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]")
# Catches a line with an object between quotes and a comma: "MyModel",
_re_quote_object = re.compile('^\s+"([^"]+)",')
# Catches a line with objects between brackets only: ["foo", "bar"],
_re_between_brackets = re.compile("^\s+\[([^\]]+)\]")
# Catches a line with from foo import bar, bla, boo
_re_import = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
# Catches a line with try:
_re_try = re.compile(r"^\s*try:")
# Catches a line with else:
_re_else = re.compile(r"^\s*else:")
def find_backend(line):
"""Find one (or multiple) backend in a code line of the init."""
if _re_test_backend.search(line) is None:
return None
backends = [b[0] for b in _re_backend.findall(line)]
backends.sort()
return "_and_".join(backends)
def parse_init(init_file):
"""
Read an init_file and parse (per backend) the _import_structure objects defined and the TYPE_CHECKING objects
defined
"""
with open(init_file, "r", encoding="utf-8", newline="\n") as f:
lines = f.readlines()
line_index = 0
while line_index < len(lines) and not lines[line_index].startswith("_import_structure = {"):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(lines):
return None
# First grab the objects without a specific backend in _import_structure
objects = []
while not lines[line_index].startswith("if TYPE_CHECKING") and find_backend(lines[line_index]) is None:
line = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(line):
content = _re_one_line_import_struct.search(line).groups()[0]
imports = re.findall("\[([^\]]+)\]", content)
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(", ")])
line_index += 1
continue
single_line_import_search = _re_import_struct_key_value.search(line)
if single_line_import_search is not None:
imports = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(", ") if len(obj) > 0]
objects.extend(imports)
elif line.startswith(" " * 8 + '"'):
objects.append(line[9:-3])
line_index += 1
import_dict_objects = {"none": objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith("if TYPE_CHECKING"):
# If the line is an if not is_backend_available, we grab all objects associated.
backend = find_backend(lines[line_index])
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1]) is None:
backend = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index]) is None:
line_index += 1
line_index += 1
objects = []
# Until we unindent, add backend objects to the list
while len(lines[line_index]) <= 1 or lines[line_index].startswith(" " * 4):
line = lines[line_index]
if _re_import_struct_add_one.search(line) is not None:
objects.append(_re_import_struct_add_one.search(line).groups()[0])
elif _re_import_struct_add_many.search(line) is not None:
imports = _re_import_struct_add_many.search(line).groups()[0].split(", ")
imports = [obj[1:-1] for obj in imports if len(obj) > 0]
objects.extend(imports)
elif _re_between_brackets.search(line) is not None:
imports = _re_between_brackets.search(line).groups()[0].split(", ")
imports = [obj[1:-1] for obj in imports if len(obj) > 0]
objects.extend(imports)
elif _re_quote_object.search(line) is not None:
objects.append(_re_quote_object.search(line).groups()[0])
elif line.startswith(" " * 8 + '"'):
objects.append(line[9:-3])
elif line.startswith(" " * 12 + '"'):
objects.append(line[13:-3])
line_index += 1
import_dict_objects[backend] = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
objects = []
while (
line_index < len(lines)
and find_backend(lines[line_index]) is None
and not lines[line_index].startswith("else")
):
line = lines[line_index]
single_line_import_search = _re_import.search(line)
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(", "))
elif line.startswith(" " * 8):
objects.append(line[8:-2])
line_index += 1
type_hint_objects = {"none": objects}
# Let's continue with backend-specific objects
while line_index < len(lines):
# If the line is an if is_backend_available, we grab all objects associated.
backend = find_backend(lines[line_index])
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1]) is None:
backend = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index]) is None:
line_index += 1
line_index += 1
objects = []
# Until we unindent, add backend objects to the list
while len(lines[line_index]) <= 1 or lines[line_index].startswith(" " * 8):
line = lines[line_index]
single_line_import_search = _re_import.search(line)
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(", "))
elif line.startswith(" " * 12):
objects.append(line[12:-2])
line_index += 1
type_hint_objects[backend] = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def analyze_results(import_dict_objects, type_hint_objects):
"""
Analyze the differences between _import_structure objects and TYPE_CHECKING objects found in an init.
"""
def find_duplicates(seq):
return [k for k, v in collections.Counter(seq).items() if v > 1]
if list(import_dict_objects.keys()) != list(type_hint_objects.keys()):
return ["Both sides of the init do not have the same backends!"]
errors = []
for key in import_dict_objects.keys():
duplicate_imports = find_duplicates(import_dict_objects[key])
if duplicate_imports:
errors.append(f"Duplicate _import_structure definitions for: {duplicate_imports}")
duplicate_type_hints = find_duplicates(type_hint_objects[key])
if duplicate_type_hints:
errors.append(f"Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}")
if sorted(set(import_dict_objects[key])) != sorted(set(type_hint_objects[key])):
name = "base imports" if key == "none" else f"{key} backend"
errors.append(f"Differences for {name}:")
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(f" {a} in TYPE_HINT but not in _import_structure.")
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(f" {a} in _import_structure but not in TYPE_HINT.")
return errors
def check_all_inits():
"""
Check all inits in the transformers repo and raise an error if at least one does not define the same objects in
both halves.
"""
failures = []
for root, _, files in os.walk(PATH_TO_TRANSFORMERS):
if "__init__.py" in files:
fname = os.path.join(root, "__init__.py")
objects = parse_init(fname)
if objects is not None:
errors = analyze_results(*objects)
if len(errors) > 0:
errors[0] = f"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"
failures.append("\n".join(errors))
if len(failures) > 0:
raise ValueError("\n\n".join(failures))
def get_transformers_submodules():
"""
Returns the list of Transformers submodules.
"""
submodules = []
for path, directories, files in os.walk(PATH_TO_TRANSFORMERS):
for folder in directories:
# Ignore private modules
if folder.startswith("_"):
directories.remove(folder)
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(path) / folder).glob("*.py"))) == 0:
continue
short_path = str((Path(path) / folder).relative_to(PATH_TO_TRANSFORMERS))
submodule = short_path.replace(os.path.sep, ".")
submodules.append(submodule)
for fname in files:
if fname == "__init__.py":
continue
short_path = str((Path(path) / fname).relative_to(PATH_TO_TRANSFORMERS))
submodule = short_path.replace(".py", "").replace(os.path.sep, ".")
if len(submodule.split(".")) == 1:
submodules.append(submodule)
return submodules
IGNORE_SUBMODULES = [
"convert_pytorch_checkpoint_to_tf2",
"modeling_flax_pytorch_utils",
]
def check_submodules():
# This is to make sure the transformers module imported is the one in the repo.
spec = importlib.util.spec_from_file_location(
"transformers",
os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
transformers = spec.loader.load_module()
module_not_registered = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
]
if len(module_not_registered) > 0:
list_of_modules = "\n".join(f"- {module}" for module in module_not_registered)
raise ValueError(
"The following submodules are not properly registered in the main init of Transformers:\n"
f"{list_of_modules}\n"
"Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."
)
if __name__ == "__main__":
check_all_inits()
check_submodules()
| diffusers-ft-main | utils/check_inits.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
DIFFUSERS_PATH = "src/diffusers"
REPO_PATH = "."
# This is to make sure the diffusers module imported is the one in the repo.
spec = importlib.util.spec_from_file_location(
"diffusers",
os.path.join(DIFFUSERS_PATH, "__init__.py"),
submodule_search_locations=[DIFFUSERS_PATH],
)
diffusers_module = spec.loader.load_module()
def _should_continue(line, indent):
return line.startswith(indent) or len(line) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$", line) is not None
def find_code_in_diffusers(object_name):
"""Find and return the code source code of `object_name`."""
parts = object_name.split(".")
i = 0
# First let's find the module where our object lives.
module = parts[i]
while i < len(parts) and not os.path.isfile(os.path.join(DIFFUSERS_PATH, f"{module}.py")):
i += 1
if i < len(parts):
module = os.path.join(module, parts[i])
if i >= len(parts):
raise ValueError(f"`object_name` should begin with the name of a module of diffusers but got {object_name}.")
with open(os.path.join(DIFFUSERS_PATH, f"{module}.py"), "r", encoding="utf-8", newline="\n") as f:
lines = f.readlines()
# Now let's find the class / func in the code!
indent = ""
line_index = 0
for name in parts[i + 1 :]:
while (
line_index < len(lines) and re.search(rf"^{indent}(class|def)\s+{name}(\(|\:)", lines[line_index]) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(lines):
raise ValueError(f" {object_name} does not match any function or class in {module}.")
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
start_index = line_index
while line_index < len(lines) and _should_continue(lines[line_index], indent):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1]) <= 1:
line_index -= 1
code_lines = lines[start_index:line_index]
return "".join(code_lines)
_re_copy_warning = re.compile(r"^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)")
_re_replace_pattern = re.compile(r"^\s*(\S+)->(\S+)(\s+.*|$)")
_re_fill_pattern = re.compile(r"<FILL\s+[^>]*>")
def get_indent(code):
lines = code.split("\n")
idx = 0
while idx < len(lines) and len(lines[idx]) == 0:
idx += 1
if idx < len(lines):
return re.search(r"^(\s*)\S", lines[idx]).groups()[0]
return ""
def blackify(code):
"""
Applies the black part of our `make style` command to `code`.
"""
has_indent = len(get_indent(code)) > 0
if has_indent:
code = f"class Bla:\n{code}"
mode = black.Mode(target_versions={black.TargetVersion.PY37}, line_length=119, preview=True)
result = black.format_str(code, mode=mode)
result, _ = style_docstrings_in_code(result)
return result[len("class Bla:\n") :] if has_indent else result
def is_copy_consistent(filename, overwrite=False):
"""
Check if the code commented as a copy in `filename` matches the original.
Return the differences or overwrites the content depending on `overwrite`.
"""
with open(filename, "r", encoding="utf-8", newline="\n") as f:
lines = f.readlines()
diffs = []
line_index = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(lines):
search = _re_copy_warning.search(lines[line_index])
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
indent, object_name, replace_pattern = search.groups()
theoretical_code = find_code_in_diffusers(object_name)
theoretical_indent = get_indent(theoretical_code)
start_index = line_index + 1 if indent == theoretical_indent else line_index + 2
indent = theoretical_indent
line_index = start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
should_continue = True
while line_index < len(lines) and should_continue:
line_index += 1
if line_index >= len(lines):
break
line = lines[line_index]
should_continue = _should_continue(line, indent) and re.search(f"^{indent}# End copy", line) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1]) <= 1:
line_index -= 1
observed_code_lines = lines[start_index:line_index]
observed_code = "".join(observed_code_lines)
# Remove any nested `Copied from` comments to avoid circular copies
theoretical_code = [line for line in theoretical_code.split("\n") if _re_copy_warning.search(line) is None]
theoretical_code = "\n".join(theoretical_code)
# Before comparing, use the `replace_pattern` on the original code.
if len(replace_pattern) > 0:
patterns = replace_pattern.replace("with", "").split(",")
patterns = [_re_replace_pattern.search(p) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
obj1, obj2, option = pattern.groups()
theoretical_code = re.sub(obj1, obj2, theoretical_code)
if option.strip() == "all-casing":
theoretical_code = re.sub(obj1.lower(), obj2.lower(), theoretical_code)
theoretical_code = re.sub(obj1.upper(), obj2.upper(), theoretical_code)
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
theoretical_code = blackify(lines[start_index - 1] + theoretical_code)
theoretical_code = theoretical_code[len(lines[start_index - 1]) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index])
if overwrite:
lines = lines[:start_index] + [theoretical_code] + lines[line_index:]
line_index = start_index + 1
if overwrite and len(diffs) > 0:
# Warn the user a file has been modified.
print(f"Detected changes, rewriting {filename}.")
with open(filename, "w", encoding="utf-8", newline="\n") as f:
f.writelines(lines)
return diffs
def check_copies(overwrite: bool = False):
all_files = glob.glob(os.path.join(DIFFUSERS_PATH, "**/*.py"), recursive=True)
diffs = []
for filename in all_files:
new_diffs = is_copy_consistent(filename, overwrite)
diffs += [f"- {filename}: copy does not match {d[0]} at line {d[1]}" for d in new_diffs]
if not overwrite and len(diffs) > 0:
diff = "\n".join(diffs)
raise Exception(
"Found the following copy inconsistencies:\n"
+ diff
+ "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
args = parser.parse_args()
check_copies(args.fix_and_overwrite)
| diffusers-ft-main | utils/check_copies.py |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import re
PATH_TO_TRANSFORMERS = "src/diffusers"
# Pattern that looks at the indentation in a line.
_re_indent = re.compile(r"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
_re_direct_key = re.compile(r'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
_re_indirect_key = re.compile(r'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
_re_strip_line = re.compile(r'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
_re_bracket_content = re.compile(r"\[([^\]]+)\]")
def get_indent(line):
"""Returns the indent in `line`."""
search = _re_indent.search(line)
return "" if search is None else search.groups()[0]
def split_code_in_indented_blocks(code, indent_level="", start_prompt=None, end_prompt=None):
"""
Split `code` into its indented blocks, starting at `indent_level`. If provided, begins splitting after
`start_prompt` and stops at `end_prompt` (but returns what's before `start_prompt` as a first block and what's
after `end_prompt` as a last block, so `code` is always the same as joining the result of this function).
"""
# Let's split the code into lines and move to start_index.
index = 0
lines = code.split("\n")
if start_prompt is not None:
while not lines[index].startswith(start_prompt):
index += 1
blocks = ["\n".join(lines[:index])]
else:
blocks = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
current_block = [lines[index]]
index += 1
while index < len(lines) and (end_prompt is None or not lines[index].startswith(end_prompt)):
if len(lines[index]) > 0 and get_indent(lines[index]) == indent_level:
if len(current_block) > 0 and get_indent(current_block[-1]).startswith(indent_level + " "):
current_block.append(lines[index])
blocks.append("\n".join(current_block))
if index < len(lines) - 1:
current_block = [lines[index + 1]]
index += 1
else:
current_block = []
else:
blocks.append("\n".join(current_block))
current_block = [lines[index]]
else:
current_block.append(lines[index])
index += 1
# Adds current block if it's nonempty.
if len(current_block) > 0:
blocks.append("\n".join(current_block))
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(lines):
blocks.append("\n".join(lines[index:]))
return blocks
def ignore_underscore(key):
"Wraps a `key` (that maps an object to string) to lower case and remove underscores."
def _inner(x):
return key(x).lower().replace("_", "")
return _inner
def sort_objects(objects, key=None):
"Sort a list of `objects` following the rules of isort. `key` optionally maps an object to a str."
# If no key is provided, we use a noop.
def noop(x):
return x
if key is None:
key = noop
# Constants are all uppercase, they go first.
constants = [obj for obj in objects if key(obj).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
classes = [obj for obj in objects if key(obj)[0].isupper() and not key(obj).isupper()]
# Functions begin with a lowercase, they go last.
functions = [obj for obj in objects if not key(obj)[0].isupper()]
key1 = ignore_underscore(key)
return sorted(constants, key=key1) + sorted(classes, key=key1) + sorted(functions, key=key1)
def sort_objects_in_import(import_statement):
"""
Return the same `import_statement` but with objects properly sorted.
"""
# This inner function sort imports between [ ].
def _replace(match):
imports = match.groups()[0]
if "," not in imports:
return f"[{imports}]"
keys = [part.strip().replace('"', "") for part in imports.split(",")]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1]) == 0:
keys = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(keys)]) + "]"
lines = import_statement.split("\n")
if len(lines) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
idx = 2 if lines[1].strip() == "[" else 1
keys_to_sort = [(i, _re_strip_line.search(line).groups()[0]) for i, line in enumerate(lines[idx:-idx])]
sorted_indices = sort_objects(keys_to_sort, key=lambda x: x[1])
sorted_lines = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:])
elif len(lines) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1]) is not None:
lines[1] = _re_bracket_content.sub(_replace, lines[1])
else:
keys = [part.strip().replace('"', "") for part in lines[1].split(",")]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1]) == 0:
keys = keys[:-1]
lines[1] = get_indent(lines[1]) + ", ".join([f'"{k}"' for k in sort_objects(keys)])
return "\n".join(lines)
else:
# Finally we have to deal with imports fitting on one line
import_statement = _re_bracket_content.sub(_replace, import_statement)
return import_statement
def sort_imports(file, check_only=True):
"""
Sort `_import_structure` imports in `file`, `check_only` determines if we only check or overwrite.
"""
with open(file, "r") as f:
code = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
main_blocks = split_code_in_indented_blocks(
code, start_prompt="_import_structure = {", end_prompt="if TYPE_CHECKING:"
)
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1, len(main_blocks) - 1):
# Check if the block contains some `_import_structure`s thingy to sort.
block = main_blocks[block_idx]
block_lines = block.split("\n")
# Get to the start of the imports.
line_idx = 0
while line_idx < len(block_lines) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
line_idx = len(block_lines)
else:
line_idx += 1
if line_idx >= len(block_lines):
continue
# Ignore beginning and last line: they don't contain anything.
internal_block_code = "\n".join(block_lines[line_idx:-1])
indent = get_indent(block_lines[1])
# Slit the internal block into blocks of indent level 1.
internal_blocks = split_code_in_indented_blocks(internal_block_code, indent_level=indent)
# We have two categories of import key: list or _import_structure[key].append/extend
pattern = _re_direct_key if "_import_structure" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
keys = [(pattern.search(b).groups()[0] if pattern.search(b) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
keys_to_sort = [(i, key) for i, key in enumerate(keys) if key is not None]
sorted_indices = [x[0] for x in sorted(keys_to_sort, key=lambda x: x[1])]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
count = 0
reordered_blocks = []
for i in range(len(internal_blocks)):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i])
else:
block = sort_objects_in_import(internal_blocks[sorted_indices[count]])
reordered_blocks.append(block)
count += 1
# And we put our main block back together with its first and last line.
main_blocks[block_idx] = "\n".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]])
if code != "\n".join(main_blocks):
if check_only:
return True
else:
print(f"Overwriting {file}.")
with open(file, "w") as f:
f.write("\n".join(main_blocks))
def sort_imports_in_all_inits(check_only=True):
failures = []
for root, _, files in os.walk(PATH_TO_TRANSFORMERS):
if "__init__.py" in files:
result = sort_imports(os.path.join(root, "__init__.py"), check_only=check_only)
if result:
failures = [os.path.join(root, "__init__.py")]
if len(failures) > 0:
raise ValueError(f"Would overwrite {len(failures)} files, run `make style`.")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
args = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| diffusers-ft-main | utils/custom_init_isort.py |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
import inspect
import os
import re
import warnings
from collections import OrderedDict
from difflib import get_close_matches
from pathlib import Path
from diffusers.models.auto import get_values
from diffusers.utils import ENV_VARS_TRUE_VALUES, is_flax_available, is_tf_available, is_torch_available
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_repo.py
PATH_TO_DIFFUSERS = "src/diffusers"
PATH_TO_TESTS = "tests"
PATH_TO_DOC = "docs/source/en"
# Update this list with models that are supposed to be private.
PRIVATE_MODELS = [
"DPRSpanPredictor",
"RealmBertModel",
"T5Stack",
"TFDPRSpanPredictor",
]
# Update this list for models that are not tested with a comment explaining the reason it should not be.
# Being in this list is an exception and should **not** be the rule.
IGNORE_NON_TESTED = PRIVATE_MODELS.copy() + [
# models to ignore for not tested
"OPTDecoder", # Building part of bigger (tested) model.
"DecisionTransformerGPT2Model", # Building part of bigger (tested) model.
"SegformerDecodeHead", # Building part of bigger (tested) model.
"PLBartEncoder", # Building part of bigger (tested) model.
"PLBartDecoder", # Building part of bigger (tested) model.
"PLBartDecoderWrapper", # Building part of bigger (tested) model.
"BigBirdPegasusEncoder", # Building part of bigger (tested) model.
"BigBirdPegasusDecoder", # Building part of bigger (tested) model.
"BigBirdPegasusDecoderWrapper", # Building part of bigger (tested) model.
"DetrEncoder", # Building part of bigger (tested) model.
"DetrDecoder", # Building part of bigger (tested) model.
"DetrDecoderWrapper", # Building part of bigger (tested) model.
"M2M100Encoder", # Building part of bigger (tested) model.
"M2M100Decoder", # Building part of bigger (tested) model.
"Speech2TextEncoder", # Building part of bigger (tested) model.
"Speech2TextDecoder", # Building part of bigger (tested) model.
"LEDEncoder", # Building part of bigger (tested) model.
"LEDDecoder", # Building part of bigger (tested) model.
"BartDecoderWrapper", # Building part of bigger (tested) model.
"BartEncoder", # Building part of bigger (tested) model.
"BertLMHeadModel", # Needs to be setup as decoder.
"BlenderbotSmallEncoder", # Building part of bigger (tested) model.
"BlenderbotSmallDecoderWrapper", # Building part of bigger (tested) model.
"BlenderbotEncoder", # Building part of bigger (tested) model.
"BlenderbotDecoderWrapper", # Building part of bigger (tested) model.
"MBartEncoder", # Building part of bigger (tested) model.
"MBartDecoderWrapper", # Building part of bigger (tested) model.
"MegatronBertLMHeadModel", # Building part of bigger (tested) model.
"MegatronBertEncoder", # Building part of bigger (tested) model.
"MegatronBertDecoder", # Building part of bigger (tested) model.
"MegatronBertDecoderWrapper", # Building part of bigger (tested) model.
"PegasusEncoder", # Building part of bigger (tested) model.
"PegasusDecoderWrapper", # Building part of bigger (tested) model.
"DPREncoder", # Building part of bigger (tested) model.
"ProphetNetDecoderWrapper", # Building part of bigger (tested) model.
"RealmBertModel", # Building part of bigger (tested) model.
"RealmReader", # Not regular model.
"RealmScorer", # Not regular model.
"RealmForOpenQA", # Not regular model.
"ReformerForMaskedLM", # Needs to be setup as decoder.
"Speech2Text2DecoderWrapper", # Building part of bigger (tested) model.
"TFDPREncoder", # Building part of bigger (tested) model.
"TFElectraMainLayer", # Building part of bigger (tested) model (should it be a TFModelMixin ?)
"TFRobertaForMultipleChoice", # TODO: fix
"TrOCRDecoderWrapper", # Building part of bigger (tested) model.
"SeparableConv1D", # Building part of bigger (tested) model.
"FlaxBartForCausalLM", # Building part of bigger (tested) model.
"FlaxBertForCausalLM", # Building part of bigger (tested) model. Tested implicitly through FlaxRobertaForCausalLM.
"OPTDecoderWrapper",
]
# Update this list with test files that don't have a tester with a `all_model_classes` variable and which don't
# trigger the common tests.
TEST_FILES_WITH_NO_COMMON_TESTS = [
"models/decision_transformer/test_modeling_decision_transformer.py",
"models/camembert/test_modeling_camembert.py",
"models/mt5/test_modeling_flax_mt5.py",
"models/mbart/test_modeling_mbart.py",
"models/mt5/test_modeling_mt5.py",
"models/pegasus/test_modeling_pegasus.py",
"models/camembert/test_modeling_tf_camembert.py",
"models/mt5/test_modeling_tf_mt5.py",
"models/xlm_roberta/test_modeling_tf_xlm_roberta.py",
"models/xlm_roberta/test_modeling_flax_xlm_roberta.py",
"models/xlm_prophetnet/test_modeling_xlm_prophetnet.py",
"models/xlm_roberta/test_modeling_xlm_roberta.py",
"models/vision_text_dual_encoder/test_modeling_vision_text_dual_encoder.py",
"models/vision_text_dual_encoder/test_modeling_flax_vision_text_dual_encoder.py",
"models/decision_transformer/test_modeling_decision_transformer.py",
]
# Update this list for models that are not in any of the auto MODEL_XXX_MAPPING. Being in this list is an exception and
# should **not** be the rule.
IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [
# models to ignore for model xxx mapping
"DPTForDepthEstimation",
"DecisionTransformerGPT2Model",
"GLPNForDepthEstimation",
"ViltForQuestionAnswering",
"ViltForImagesAndTextClassification",
"ViltForImageAndTextRetrieval",
"ViltForMaskedLM",
"XGLMEncoder",
"XGLMDecoder",
"XGLMDecoderWrapper",
"PerceiverForMultimodalAutoencoding",
"PerceiverForOpticalFlow",
"SegformerDecodeHead",
"FlaxBeitForMaskedImageModeling",
"PLBartEncoder",
"PLBartDecoder",
"PLBartDecoderWrapper",
"BeitForMaskedImageModeling",
"CLIPTextModel",
"CLIPVisionModel",
"TFCLIPTextModel",
"TFCLIPVisionModel",
"FlaxCLIPTextModel",
"FlaxCLIPVisionModel",
"FlaxWav2Vec2ForCTC",
"DetrForSegmentation",
"DPRReader",
"FlaubertForQuestionAnswering",
"FlavaImageCodebook",
"FlavaTextModel",
"FlavaImageModel",
"FlavaMultimodalModel",
"GPT2DoubleHeadsModel",
"LukeForMaskedLM",
"LukeForEntityClassification",
"LukeForEntityPairClassification",
"LukeForEntitySpanClassification",
"OpenAIGPTDoubleHeadsModel",
"RagModel",
"RagSequenceForGeneration",
"RagTokenForGeneration",
"RealmEmbedder",
"RealmForOpenQA",
"RealmScorer",
"RealmReader",
"TFDPRReader",
"TFGPT2DoubleHeadsModel",
"TFOpenAIGPTDoubleHeadsModel",
"TFRagModel",
"TFRagSequenceForGeneration",
"TFRagTokenForGeneration",
"Wav2Vec2ForCTC",
"HubertForCTC",
"SEWForCTC",
"SEWDForCTC",
"XLMForQuestionAnswering",
"XLNetForQuestionAnswering",
"SeparableConv1D",
"VisualBertForRegionToPhraseAlignment",
"VisualBertForVisualReasoning",
"VisualBertForQuestionAnswering",
"VisualBertForMultipleChoice",
"TFWav2Vec2ForCTC",
"TFHubertForCTC",
"MaskFormerForInstanceSegmentation",
]
# Update this list for models that have multiple model types for the same
# model doc
MODEL_TYPE_TO_DOC_MAPPING = OrderedDict(
[
("data2vec-text", "data2vec"),
("data2vec-audio", "data2vec"),
("data2vec-vision", "data2vec"),
]
)
# This is to make sure the transformers module imported is the one in the repo.
spec = importlib.util.spec_from_file_location(
"diffusers",
os.path.join(PATH_TO_DIFFUSERS, "__init__.py"),
submodule_search_locations=[PATH_TO_DIFFUSERS],
)
diffusers = spec.loader.load_module()
def check_model_list():
"""Check the model list inside the transformers library."""
# Get the models from the directory structure of `src/diffusers/models/`
models_dir = os.path.join(PATH_TO_DIFFUSERS, "models")
_models = []
for model in os.listdir(models_dir):
model_dir = os.path.join(models_dir, model)
if os.path.isdir(model_dir) and "__init__.py" in os.listdir(model_dir):
_models.append(model)
# Get the models from the directory structure of `src/transformers/models/`
models = [model for model in dir(diffusers.models) if not model.startswith("__")]
missing_models = sorted(list(set(_models).difference(models)))
if missing_models:
raise Exception(
f"The following models should be included in {models_dir}/__init__.py: {','.join(missing_models)}."
)
# If some modeling modules should be ignored for all checks, they should be added in the nested list
# _ignore_modules of this function.
def get_model_modules():
"""Get the model modules inside the transformers library."""
_ignore_modules = [
"modeling_auto",
"modeling_encoder_decoder",
"modeling_marian",
"modeling_mmbt",
"modeling_outputs",
"modeling_retribert",
"modeling_utils",
"modeling_flax_auto",
"modeling_flax_encoder_decoder",
"modeling_flax_utils",
"modeling_speech_encoder_decoder",
"modeling_flax_speech_encoder_decoder",
"modeling_flax_vision_encoder_decoder",
"modeling_transfo_xl_utilities",
"modeling_tf_auto",
"modeling_tf_encoder_decoder",
"modeling_tf_outputs",
"modeling_tf_pytorch_utils",
"modeling_tf_utils",
"modeling_tf_transfo_xl_utilities",
"modeling_tf_vision_encoder_decoder",
"modeling_vision_encoder_decoder",
]
modules = []
for model in dir(diffusers.models):
# There are some magic dunder attributes in the dir, we ignore them
if not model.startswith("__"):
model_module = getattr(diffusers.models, model)
for submodule in dir(model_module):
if submodule.startswith("modeling") and submodule not in _ignore_modules:
modeling_module = getattr(model_module, submodule)
if inspect.ismodule(modeling_module):
modules.append(modeling_module)
return modules
def get_models(module, include_pretrained=False):
"""Get the objects in module that are models."""
models = []
model_classes = (diffusers.ModelMixin, diffusers.TFModelMixin, diffusers.FlaxModelMixin)
for attr_name in dir(module):
if not include_pretrained and ("Pretrained" in attr_name or "PreTrained" in attr_name):
continue
attr = getattr(module, attr_name)
if isinstance(attr, type) and issubclass(attr, model_classes) and attr.__module__ == module.__name__:
models.append((attr_name, attr))
return models
def is_a_private_model(model):
"""Returns True if the model should not be in the main init."""
if model in PRIVATE_MODELS:
return True
# Wrapper, Encoder and Decoder are all privates
if model.endswith("Wrapper"):
return True
if model.endswith("Encoder"):
return True
if model.endswith("Decoder"):
return True
return False
def check_models_are_in_init():
"""Checks all models defined in the library are in the main init."""
models_not_in_init = []
dir_transformers = dir(diffusers)
for module in get_model_modules():
models_not_in_init += [
model[0] for model in get_models(module, include_pretrained=True) if model[0] not in dir_transformers
]
# Remove private models
models_not_in_init = [model for model in models_not_in_init if not is_a_private_model(model)]
if len(models_not_in_init) > 0:
raise Exception(f"The following models should be in the main init: {','.join(models_not_in_init)}.")
# If some test_modeling files should be ignored when checking models are all tested, they should be added in the
# nested list _ignore_files of this function.
def get_model_test_files():
"""Get the model test files.
The returned files should NOT contain the `tests` (i.e. `PATH_TO_TESTS` defined in this script). They will be
considered as paths relative to `tests`. A caller has to use `os.path.join(PATH_TO_TESTS, ...)` to access the files.
"""
_ignore_files = [
"test_modeling_common",
"test_modeling_encoder_decoder",
"test_modeling_flax_encoder_decoder",
"test_modeling_flax_speech_encoder_decoder",
"test_modeling_marian",
"test_modeling_tf_common",
"test_modeling_tf_encoder_decoder",
]
test_files = []
# Check both `PATH_TO_TESTS` and `PATH_TO_TESTS/models`
model_test_root = os.path.join(PATH_TO_TESTS, "models")
model_test_dirs = []
for x in os.listdir(model_test_root):
x = os.path.join(model_test_root, x)
if os.path.isdir(x):
model_test_dirs.append(x)
for target_dir in [PATH_TO_TESTS] + model_test_dirs:
for file_or_dir in os.listdir(target_dir):
path = os.path.join(target_dir, file_or_dir)
if os.path.isfile(path):
filename = os.path.split(path)[-1]
if "test_modeling" in filename and not os.path.splitext(filename)[0] in _ignore_files:
file = os.path.join(*path.split(os.sep)[1:])
test_files.append(file)
return test_files
# This is a bit hacky but I didn't find a way to import the test_file as a module and read inside the tester class
# for the all_model_classes variable.
def find_tested_models(test_file):
"""Parse the content of test_file to detect what's in all_model_classes"""
# This is a bit hacky but I didn't find a way to import the test_file as a module and read inside the class
with open(os.path.join(PATH_TO_TESTS, test_file), "r", encoding="utf-8", newline="\n") as f:
content = f.read()
all_models = re.findall(r"all_model_classes\s+=\s+\(\s*\(([^\)]*)\)", content)
# Check with one less parenthesis as well
all_models += re.findall(r"all_model_classes\s+=\s+\(([^\)]*)\)", content)
if len(all_models) > 0:
model_tested = []
for entry in all_models:
for line in entry.split(","):
name = line.strip()
if len(name) > 0:
model_tested.append(name)
return model_tested
def check_models_are_tested(module, test_file):
"""Check models defined in module are tested in test_file."""
# XxxModelMixin are not tested
defined_models = get_models(module)
tested_models = find_tested_models(test_file)
if tested_models is None:
if test_file.replace(os.path.sep, "/") in TEST_FILES_WITH_NO_COMMON_TESTS:
return
return [
f"{test_file} should define `all_model_classes` to apply common tests to the models it tests. "
+ "If this intentional, add the test filename to `TEST_FILES_WITH_NO_COMMON_TESTS` in the file "
+ "`utils/check_repo.py`."
]
failures = []
for model_name, _ in defined_models:
if model_name not in tested_models and model_name not in IGNORE_NON_TESTED:
failures.append(
f"{model_name} is defined in {module.__name__} but is not tested in "
+ f"{os.path.join(PATH_TO_TESTS, test_file)}. Add it to the all_model_classes in that file."
+ "If common tests should not applied to that model, add its name to `IGNORE_NON_TESTED`"
+ "in the file `utils/check_repo.py`."
)
return failures
def check_all_models_are_tested():
"""Check all models are properly tested."""
modules = get_model_modules()
test_files = get_model_test_files()
failures = []
for module in modules:
test_file = [file for file in test_files if f"test_{module.__name__.split('.')[-1]}.py" in file]
if len(test_file) == 0:
failures.append(f"{module.__name__} does not have its corresponding test file {test_file}.")
elif len(test_file) > 1:
failures.append(f"{module.__name__} has several test files: {test_file}.")
else:
test_file = test_file[0]
new_failures = check_models_are_tested(module, test_file)
if new_failures is not None:
failures += new_failures
if len(failures) > 0:
raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures))
def get_all_auto_configured_models():
"""Return the list of all models in at least one auto class."""
result = set() # To avoid duplicates we concatenate all model classes in a set.
if is_torch_available():
for attr_name in dir(diffusers.models.auto.modeling_auto):
if attr_name.startswith("MODEL_") and attr_name.endswith("MAPPING_NAMES"):
result = result | set(get_values(getattr(diffusers.models.auto.modeling_auto, attr_name)))
if is_tf_available():
for attr_name in dir(diffusers.models.auto.modeling_tf_auto):
if attr_name.startswith("TF_MODEL_") and attr_name.endswith("MAPPING_NAMES"):
result = result | set(get_values(getattr(diffusers.models.auto.modeling_tf_auto, attr_name)))
if is_flax_available():
for attr_name in dir(diffusers.models.auto.modeling_flax_auto):
if attr_name.startswith("FLAX_MODEL_") and attr_name.endswith("MAPPING_NAMES"):
result = result | set(get_values(getattr(diffusers.models.auto.modeling_flax_auto, attr_name)))
return [cls for cls in result]
def ignore_unautoclassed(model_name):
"""Rules to determine if `name` should be in an auto class."""
# Special white list
if model_name in IGNORE_NON_AUTO_CONFIGURED:
return True
# Encoder and Decoder should be ignored
if "Encoder" in model_name or "Decoder" in model_name:
return True
return False
def check_models_are_auto_configured(module, all_auto_models):
"""Check models defined in module are each in an auto class."""
defined_models = get_models(module)
failures = []
for model_name, _ in defined_models:
if model_name not in all_auto_models and not ignore_unautoclassed(model_name):
failures.append(
f"{model_name} is defined in {module.__name__} but is not present in any of the auto mapping. "
"If that is intended behavior, add its name to `IGNORE_NON_AUTO_CONFIGURED` in the file "
"`utils/check_repo.py`."
)
return failures
def check_all_models_are_auto_configured():
"""Check all models are each in an auto class."""
missing_backends = []
if not is_torch_available():
missing_backends.append("PyTorch")
if not is_tf_available():
missing_backends.append("TensorFlow")
if not is_flax_available():
missing_backends.append("Flax")
if len(missing_backends) > 0:
missing = ", ".join(missing_backends)
if os.getenv("TRANSFORMERS_IS_CI", "").upper() in ENV_VARS_TRUE_VALUES:
raise Exception(
"Full quality checks require all backends to be installed (with `pip install -e .[dev]` in the "
f"Transformers repo, the following are missing: {missing}."
)
else:
warnings.warn(
"Full quality checks require all backends to be installed (with `pip install -e .[dev]` in the "
f"Transformers repo, the following are missing: {missing}. While it's probably fine as long as you "
"didn't make any change in one of those backends modeling files, you should probably execute the "
"command above to be on the safe side."
)
modules = get_model_modules()
all_auto_models = get_all_auto_configured_models()
failures = []
for module in modules:
new_failures = check_models_are_auto_configured(module, all_auto_models)
if new_failures is not None:
failures += new_failures
if len(failures) > 0:
raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures))
_re_decorator = re.compile(r"^\s*@(\S+)\s+$")
def check_decorator_order(filename):
"""Check that in the test file `filename` the slow decorator is always last."""
with open(filename, "r", encoding="utf-8", newline="\n") as f:
lines = f.readlines()
decorator_before = None
errors = []
for i, line in enumerate(lines):
search = _re_decorator.search(line)
if search is not None:
decorator_name = search.groups()[0]
if decorator_before is not None and decorator_name.startswith("parameterized"):
errors.append(i)
decorator_before = decorator_name
elif decorator_before is not None:
decorator_before = None
return errors
def check_all_decorator_order():
"""Check that in all test files, the slow decorator is always last."""
errors = []
for fname in os.listdir(PATH_TO_TESTS):
if fname.endswith(".py"):
filename = os.path.join(PATH_TO_TESTS, fname)
new_errors = check_decorator_order(filename)
errors += [f"- {filename}, line {i}" for i in new_errors]
if len(errors) > 0:
msg = "\n".join(errors)
raise ValueError(
"The parameterized decorator (and its variants) should always be first, but this is not the case in the"
f" following files:\n{msg}"
)
def find_all_documented_objects():
"""Parse the content of all doc files to detect which classes and functions it documents"""
documented_obj = []
for doc_file in Path(PATH_TO_DOC).glob("**/*.rst"):
with open(doc_file, "r", encoding="utf-8", newline="\n") as f:
content = f.read()
raw_doc_objs = re.findall(r"(?:autoclass|autofunction):: transformers.(\S+)\s+", content)
documented_obj += [obj.split(".")[-1] for obj in raw_doc_objs]
for doc_file in Path(PATH_TO_DOC).glob("**/*.mdx"):
with open(doc_file, "r", encoding="utf-8", newline="\n") as f:
content = f.read()
raw_doc_objs = re.findall("\[\[autodoc\]\]\s+(\S+)\s+", content)
documented_obj += [obj.split(".")[-1] for obj in raw_doc_objs]
return documented_obj
# One good reason for not being documented is to be deprecated. Put in this list deprecated objects.
DEPRECATED_OBJECTS = [
"AutoModelWithLMHead",
"BartPretrainedModel",
"DataCollator",
"DataCollatorForSOP",
"GlueDataset",
"GlueDataTrainingArguments",
"LineByLineTextDataset",
"LineByLineWithRefDataset",
"LineByLineWithSOPTextDataset",
"PretrainedBartModel",
"PretrainedFSMTModel",
"SingleSentenceClassificationProcessor",
"SquadDataTrainingArguments",
"SquadDataset",
"SquadExample",
"SquadFeatures",
"SquadV1Processor",
"SquadV2Processor",
"TFAutoModelWithLMHead",
"TFBartPretrainedModel",
"TextDataset",
"TextDatasetForNextSentencePrediction",
"Wav2Vec2ForMaskedLM",
"Wav2Vec2Tokenizer",
"glue_compute_metrics",
"glue_convert_examples_to_features",
"glue_output_modes",
"glue_processors",
"glue_tasks_num_labels",
"squad_convert_examples_to_features",
"xnli_compute_metrics",
"xnli_output_modes",
"xnli_processors",
"xnli_tasks_num_labels",
"TFTrainer",
"TFTrainingArguments",
]
# Exceptionally, some objects should not be documented after all rules passed.
# ONLY PUT SOMETHING IN THIS LIST AS A LAST RESORT!
UNDOCUMENTED_OBJECTS = [
"AddedToken", # This is a tokenizers class.
"BasicTokenizer", # Internal, should never have been in the main init.
"CharacterTokenizer", # Internal, should never have been in the main init.
"DPRPretrainedReader", # Like an Encoder.
"DummyObject", # Just picked by mistake sometimes.
"MecabTokenizer", # Internal, should never have been in the main init.
"ModelCard", # Internal type.
"SqueezeBertModule", # Internal building block (should have been called SqueezeBertLayer)
"TFDPRPretrainedReader", # Like an Encoder.
"TransfoXLCorpus", # Internal type.
"WordpieceTokenizer", # Internal, should never have been in the main init.
"absl", # External module
"add_end_docstrings", # Internal, should never have been in the main init.
"add_start_docstrings", # Internal, should never have been in the main init.
"cached_path", # Internal used for downloading models.
"convert_tf_weight_name_to_pt_weight_name", # Internal used to convert model weights
"logger", # Internal logger
"logging", # External module
"requires_backends", # Internal function
]
# This list should be empty. Objects in it should get their own doc page.
SHOULD_HAVE_THEIR_OWN_PAGE = [
# Benchmarks
"PyTorchBenchmark",
"PyTorchBenchmarkArguments",
"TensorFlowBenchmark",
"TensorFlowBenchmarkArguments",
]
def ignore_undocumented(name):
"""Rules to determine if `name` should be undocumented."""
# NOT DOCUMENTED ON PURPOSE.
# Constants uppercase are not documented.
if name.isupper():
return True
# ModelMixins / Encoders / Decoders / Layers / Embeddings / Attention are not documented.
if (
name.endswith("ModelMixin")
or name.endswith("Decoder")
or name.endswith("Encoder")
or name.endswith("Layer")
or name.endswith("Embeddings")
or name.endswith("Attention")
):
return True
# Submodules are not documented.
if os.path.isdir(os.path.join(PATH_TO_DIFFUSERS, name)) or os.path.isfile(
os.path.join(PATH_TO_DIFFUSERS, f"{name}.py")
):
return True
# All load functions are not documented.
if name.startswith("load_tf") or name.startswith("load_pytorch"):
return True
# is_xxx_available functions are not documented.
if name.startswith("is_") and name.endswith("_available"):
return True
# Deprecated objects are not documented.
if name in DEPRECATED_OBJECTS or name in UNDOCUMENTED_OBJECTS:
return True
# MMBT model does not really work.
if name.startswith("MMBT"):
return True
if name in SHOULD_HAVE_THEIR_OWN_PAGE:
return True
return False
def check_all_objects_are_documented():
"""Check all models are properly documented."""
documented_objs = find_all_documented_objects()
modules = diffusers._modules
objects = [c for c in dir(diffusers) if c not in modules and not c.startswith("_")]
undocumented_objs = [c for c in objects if c not in documented_objs and not ignore_undocumented(c)]
if len(undocumented_objs) > 0:
raise Exception(
"The following objects are in the public init so should be documented:\n - "
+ "\n - ".join(undocumented_objs)
)
check_docstrings_are_in_md()
check_model_type_doc_match()
def check_model_type_doc_match():
"""Check all doc pages have a corresponding model type."""
model_doc_folder = Path(PATH_TO_DOC) / "model_doc"
model_docs = [m.stem for m in model_doc_folder.glob("*.mdx")]
model_types = list(diffusers.models.auto.configuration_auto.MODEL_NAMES_MAPPING.keys())
model_types = [MODEL_TYPE_TO_DOC_MAPPING[m] if m in MODEL_TYPE_TO_DOC_MAPPING else m for m in model_types]
errors = []
for m in model_docs:
if m not in model_types and m != "auto":
close_matches = get_close_matches(m, model_types)
error_message = f"{m} is not a proper model identifier."
if len(close_matches) > 0:
close_matches = "/".join(close_matches)
error_message += f" Did you mean {close_matches}?"
errors.append(error_message)
if len(errors) > 0:
raise ValueError(
"Some model doc pages do not match any existing model type:\n"
+ "\n".join(errors)
+ "\nYou can add any missing model type to the `MODEL_NAMES_MAPPING` constant in "
"models/auto/configuration_auto.py."
)
# Re pattern to catch :obj:`xx`, :class:`xx`, :func:`xx` or :meth:`xx`.
_re_rst_special_words = re.compile(r":(?:obj|func|class|meth):`([^`]+)`")
# Re pattern to catch things between double backquotes.
_re_double_backquotes = re.compile(r"(^|[^`])``([^`]+)``([^`]|$)")
# Re pattern to catch example introduction.
_re_rst_example = re.compile(r"^\s*Example.*::\s*$", flags=re.MULTILINE)
def is_rst_docstring(docstring):
"""
Returns `True` if `docstring` is written in rst.
"""
if _re_rst_special_words.search(docstring) is not None:
return True
if _re_double_backquotes.search(docstring) is not None:
return True
if _re_rst_example.search(docstring) is not None:
return True
return False
def check_docstrings_are_in_md():
"""Check all docstrings are in md"""
files_with_rst = []
for file in Path(PATH_TO_DIFFUSERS).glob("**/*.py"):
with open(file, "r") as f:
code = f.read()
docstrings = code.split('"""')
for idx, docstring in enumerate(docstrings):
if idx % 2 == 0 or not is_rst_docstring(docstring):
continue
files_with_rst.append(file)
break
if len(files_with_rst) > 0:
raise ValueError(
"The following files have docstrings written in rst:\n"
+ "\n".join([f"- {f}" for f in files_with_rst])
+ "\nTo fix this run `doc-builder convert path_to_py_file` after installing `doc-builder`\n"
"(`pip install git+https://github.com/huggingface/doc-builder`)"
)
def check_repo_quality():
"""Check all models are properly tested and documented."""
print("Checking all models are included.")
check_model_list()
print("Checking all models are public.")
check_models_are_in_init()
print("Checking all models are properly tested.")
check_all_decorator_order()
check_all_models_are_tested()
print("Checking all objects are properly documented.")
check_all_objects_are_documented()
print("Checking all models are in at least one auto class.")
check_all_models_are_auto_configured()
if __name__ == "__main__":
check_repo_quality()
| diffusers-ft-main | utils/check_repo.py |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import collections
import importlib.util
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
TRANSFORMERS_PATH = "src/diffusers"
PATH_TO_DOCS = "docs/source/en"
REPO_PATH = "."
def _find_text_in_file(filename, start_prompt, end_prompt):
"""
Find the text in `filename` between a line beginning with `start_prompt` and before `end_prompt`, removing empty
lines.
"""
with open(filename, "r", encoding="utf-8", newline="\n") as f:
lines = f.readlines()
# Find the start prompt.
start_index = 0
while not lines[start_index].startswith(start_prompt):
start_index += 1
start_index += 1
end_index = start_index
while not lines[end_index].startswith(end_prompt):
end_index += 1
end_index -= 1
while len(lines[start_index]) <= 1:
start_index += 1
while len(lines[end_index]) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index]), start_index, end_index, lines
# Add here suffixes that are used to identify models, separated by |
ALLOWED_MODEL_SUFFIXES = "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
_re_tf_models = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_re_flax_models = re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_re_pt_models = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# This is to make sure the diffusers module imported is the one in the repo.
spec = importlib.util.spec_from_file_location(
"diffusers",
os.path.join(TRANSFORMERS_PATH, "__init__.py"),
submodule_search_locations=[TRANSFORMERS_PATH],
)
diffusers_module = spec.loader.load_module()
# Thanks to https://stackoverflow.com/questions/29916065/how-to-do-camelcase-split-in-python
def camel_case_split(identifier):
"Split a camelcased `identifier` into words."
matches = re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)", identifier)
return [m.group(0) for m in matches]
def _center_text(text, width):
text_length = 2 if text == "✅" or text == "❌" else len(text)
left_indent = (width - text_length) // 2
right_indent = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def get_model_table_from_auto_modules():
"""Generates an up-to-date model table from the content of the auto modules."""
# Dictionary model names to config.
config_mapping_names = diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
model_name_to_config = {
name: config_mapping_names[code]
for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
if code in config_mapping_names
}
model_name_to_prefix = {name: config.replace("ConfigMixin", "") for name, config in model_name_to_config.items()}
# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
slow_tokenizers = collections.defaultdict(bool)
fast_tokenizers = collections.defaultdict(bool)
pt_models = collections.defaultdict(bool)
tf_models = collections.defaultdict(bool)
flax_models = collections.defaultdict(bool)
# Let's lookup through all diffusers object (once).
for attr_name in dir(diffusers_module):
lookup_dict = None
if attr_name.endswith("Tokenizer"):
lookup_dict = slow_tokenizers
attr_name = attr_name[:-9]
elif attr_name.endswith("TokenizerFast"):
lookup_dict = fast_tokenizers
attr_name = attr_name[:-13]
elif _re_tf_models.match(attr_name) is not None:
lookup_dict = tf_models
attr_name = _re_tf_models.match(attr_name).groups()[0]
elif _re_flax_models.match(attr_name) is not None:
lookup_dict = flax_models
attr_name = _re_flax_models.match(attr_name).groups()[0]
elif _re_pt_models.match(attr_name) is not None:
lookup_dict = pt_models
attr_name = _re_pt_models.match(attr_name).groups()[0]
if lookup_dict is not None:
while len(attr_name) > 0:
if attr_name in model_name_to_prefix.values():
lookup_dict[attr_name] = True
break
# Try again after removing the last word in the name
attr_name = "".join(camel_case_split(attr_name)[:-1])
# Let's build that table!
model_names = list(model_name_to_config.keys())
model_names.sort(key=str.lower)
columns = ["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
widths = [len(c) + 2 for c in columns]
widths[0] = max([len(name) for name in model_names]) + 2
# Build the table per se
table = "|" + "|".join([_center_text(c, w) for c, w in zip(columns, widths)]) + "|\n"
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]) + "|\n"
check = {True: "✅", False: "❌"}
for name in model_names:
prefix = model_name_to_prefix[name]
line = [
name,
check[slow_tokenizers[prefix]],
check[fast_tokenizers[prefix]],
check[pt_models[prefix]],
check[tf_models[prefix]],
check[flax_models[prefix]],
]
table += "|" + "|".join([_center_text(l, w) for l, w in zip(line, widths)]) + "|\n"
return table
def check_model_table(overwrite=False):
"""Check the model table in the index.rst is consistent with the state of the lib and maybe `overwrite`."""
current_table, start_index, end_index, lines = _find_text_in_file(
filename=os.path.join(PATH_TO_DOCS, "index.mdx"),
start_prompt="<!--This table is updated automatically from the auto modules",
end_prompt="<!-- End table-->",
)
new_table = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(PATH_TO_DOCS, "index.mdx"), "w", encoding="utf-8", newline="\n") as f:
f.writelines(lines[:start_index] + [new_table] + lines[end_index:])
else:
raise ValueError(
"The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this."
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
args = parser.parse_args()
check_model_table(args.fix_and_overwrite)
| diffusers-ft-main | utils/check_table.py |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
fork_point_sha = subprocess.check_output("git merge-base main HEAD".split()).decode("utf-8")
modified_files = subprocess.check_output(f"git diff --name-only {fork_point_sha}".split()).decode("utf-8").split()
joined_dirs = "|".join(sys.argv[1:])
regex = re.compile(rf"^({joined_dirs}).*?\.py$")
relevant_modified_files = [x for x in modified_files if regex.match(x)]
print(" ".join(relevant_modified_files), end="")
| diffusers-ft-main | utils/get_modified_files.py |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
git_repo_path = abspath(join(dirname(dirname(dirname(__file__))), "src"))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)
def pytest_addoption(parser):
from diffusers.utils.testing_utils import pytest_addoption_shared
pytest_addoption_shared(parser)
def pytest_terminal_summary(terminalreporter):
from diffusers.utils.testing_utils import pytest_terminal_summary_main
make_reports = terminalreporter.config.getoption("--make-reports")
if make_reports:
pytest_terminal_summary_main(terminalreporter, id=make_reports)
| diffusers-ft-main | examples/conftest.py |
# coding=utf-8
# Copyright 2022 HuggingFace Inc..
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from typing import List
from accelerate.utils import write_basic_config
from diffusers.utils import slow
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger()
# These utils relate to ensuring the right error message is received when running scripts
class SubprocessCallException(Exception):
pass
def run_command(command: List[str], return_stdout=False):
"""
Runs `command` with `subprocess.check_output` and will potentially return the `stdout`. Will also properly capture
if an error occurred while running `command`
"""
try:
output = subprocess.check_output(command, stderr=subprocess.STDOUT)
if return_stdout:
if hasattr(output, "decode"):
output = output.decode("utf-8")
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
f"Command `{' '.join(command)}` failed with the following error:\n\n{e.output.decode()}"
) from e
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class ExamplesTestsAccelerate(unittest.TestCase):
@classmethod
def setUpClass(cls):
super().setUpClass()
cls._tmpdir = tempfile.mkdtemp()
cls.configPath = os.path.join(cls._tmpdir, "default_config.yml")
write_basic_config(save_location=cls.configPath)
cls._launch_args = ["accelerate", "launch", "--config_file", cls.configPath]
@classmethod
def tearDownClass(cls):
super().tearDownClass()
shutil.rmtree(cls._tmpdir)
@slow
def test_train_unconditional(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/unconditional_image_generation/train_unconditional.py
--dataset_name huggan/few-shot-aurora
--resolution 64
--output_dir {tmpdir}
--train_batch_size 4
--num_epochs 1
--gradient_accumulation_steps 1
--learning_rate 1e-3
--lr_warmup_steps 5
--mixed_precision fp16
""".split()
run_command(self._launch_args + test_args, return_stdout=True)
# save_pretrained smoke test
self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.bin")))
self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))
# logging test
self.assertTrue(len(os.listdir(os.path.join(tmpdir, "logs", "train_unconditional"))) > 0)
@slow
def test_textual_inversion(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/textual_inversion/textual_inversion.py
--pretrained_model_name_or_path runwayml/stable-diffusion-v1-5
--train_data_dir docs/source/imgs
--learnable_property object
--placeholder_token <cat-toy>
--initializer_token toy
--resolution 64
--train_batch_size 1
--gradient_accumulation_steps 2
--max_train_steps 10
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--mixed_precision fp16
""".split()
run_command(self._launch_args + test_args)
# save_pretrained smoke test
self.assertTrue(os.path.isfile(os.path.join(tmpdir, "learned_embeds.bin")))
| diffusers-ft-main | examples/test_examples.py |
import argparse
import inspect
import math
import os
from pathlib import Path
from typing import Optional
import torch
import torch.nn.functional as F
from accelerate import Accelerator
from accelerate.logging import get_logger
from datasets import load_dataset
from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel, __version__
from diffusers.optimization import get_scheduler
from diffusers.training_utils import EMAModel
from diffusers.utils import deprecate
from huggingface_hub import HfFolder, Repository, whoami
from packaging import version
from torchvision.transforms import (
CenterCrop,
Compose,
InterpolationMode,
Normalize,
RandomHorizontalFlip,
Resize,
ToTensor,
)
from tqdm.auto import tqdm
logger = get_logger(__name__)
diffusers_version = version.parse(version.parse(__version__).base_version)
def _extract_into_tensor(arr, timesteps, broadcast_shape):
"""
Extract values from a 1-D numpy array for a batch of indices.
:param arr: the 1-D numpy array.
:param timesteps: a tensor of indices into the array to extract.
:param broadcast_shape: a larger shape of K dimensions with the batch
dimension equal to the length of timesteps.
:return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
"""
if not isinstance(arr, torch.Tensor):
arr = torch.from_numpy(arr)
res = arr[timesteps].float().to(timesteps.device)
while len(res.shape) < len(broadcast_shape):
res = res[..., None]
return res.expand(broadcast_shape)
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that HF Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--train_data_dir",
type=str,
default=None,
help=(
"A folder containing the training data. Folder contents must follow the structure described in"
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="ddpm-model-64",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--overwrite_output_dir", action="store_true")
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument(
"--resolution",
type=int,
default=64,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
)
parser.add_argument(
"--eval_batch_size", type=int, default=16, help="The number of images to generate for evaluation."
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"The number of subprocesses to use for data loading. 0 means that the data will be loaded in the main"
" process."
),
)
parser.add_argument("--num_epochs", type=int, default=100)
parser.add_argument("--save_images_epochs", type=int, default=10, help="How often to save images during training.")
parser.add_argument(
"--save_model_epochs", type=int, default=10, help="How often to save the model during training."
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="cosine",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument("--adam_beta1", type=float, default=0.95, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument(
"--adam_weight_decay", type=float, default=1e-6, help="Weight decay magnitude for the Adam optimizer."
)
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer.")
parser.add_argument(
"--use_ema",
action="store_true",
default=True,
help="Whether to use Exponential Moving Average for the final model weights.",
)
parser.add_argument("--ema_inv_gamma", type=float, default=1.0, help="The inverse gamma value for the EMA decay.")
parser.add_argument("--ema_power", type=float, default=3 / 4, help="The power value for the EMA decay.")
parser.add_argument("--ema_max_decay", type=float, default=0.9999, help="The maximum decay magnitude for EMA.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--hub_private_repo", action="store_true", help="Whether or not to create a private repository."
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--mixed_precision",
type=str,
default="no",
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU."
),
)
parser.add_argument(
"--prediction_type",
type=str,
default="epsilon",
choices=["epsilon", "sample"],
help="Whether the model should predict the 'epsilon'/noise error or directly the reconstructed image 'x0'.",
)
parser.add_argument("--ddpm_num_steps", type=int, default=1000)
parser.add_argument("--ddpm_beta_schedule", type=str, default="linear")
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.dataset_name is None and args.train_data_dir is None:
raise ValueError("You must specify either a dataset name from the hub or a train data directory.")
return args
def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
if token is None:
token = HfFolder.get_token()
if organization is None:
username = whoami(token)["name"]
return f"{username}/{model_id}"
else:
return f"{organization}/{model_id}"
def main(args):
logging_dir = os.path.join(args.output_dir, args.logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with="tensorboard",
logging_dir=logging_dir,
)
model = UNet2DModel(
sample_size=args.resolution,
in_channels=3,
out_channels=3,
layers_per_block=2,
block_out_channels=(128, 128, 256, 256, 512, 512),
down_block_types=(
"DownBlock2D",
"DownBlock2D",
"DownBlock2D",
"DownBlock2D",
"AttnDownBlock2D",
"DownBlock2D",
),
up_block_types=(
"UpBlock2D",
"AttnUpBlock2D",
"UpBlock2D",
"UpBlock2D",
"UpBlock2D",
"UpBlock2D",
),
)
accepts_prediction_type = "prediction_type" in set(inspect.signature(DDPMScheduler.__init__).parameters.keys())
if accepts_prediction_type:
noise_scheduler = DDPMScheduler(
num_train_timesteps=args.ddpm_num_steps,
beta_schedule=args.ddpm_beta_schedule,
prediction_type=args.prediction_type,
)
else:
noise_scheduler = DDPMScheduler(num_train_timesteps=args.ddpm_num_steps, beta_schedule=args.ddpm_beta_schedule)
optimizer = torch.optim.AdamW(
model.parameters(),
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
augmentations = Compose(
[
Resize(args.resolution, interpolation=InterpolationMode.BILINEAR),
CenterCrop(args.resolution),
RandomHorizontalFlip(),
ToTensor(),
Normalize([0.5], [0.5]),
]
)
if args.dataset_name is not None:
dataset = load_dataset(
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
split="train",
)
else:
dataset = load_dataset("imagefolder", data_dir=args.train_data_dir, cache_dir=args.cache_dir, split="train")
def transforms(examples):
images = [augmentations(image.convert("RGB")) for image in examples["image"]]
return {"input": images}
logger.info(f"Dataset size: {len(dataset)}")
dataset.set_transform(transforms)
train_dataloader = torch.utils.data.DataLoader(
dataset, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers
)
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps,
num_training_steps=(len(train_dataloader) * args.num_epochs) // args.gradient_accumulation_steps,
)
model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
model, optimizer, train_dataloader, lr_scheduler
)
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
ema_model = EMAModel(
accelerator.unwrap_model(model),
inv_gamma=args.ema_inv_gamma,
power=args.ema_power,
max_value=args.ema_max_decay,
)
# Handle the repository creation
if accelerator.is_main_process:
if args.push_to_hub:
if args.hub_model_id is None:
repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
else:
repo_name = args.hub_model_id
repo = Repository(args.output_dir, clone_from=repo_name)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:
gitignore.write("step_*\n")
if "epoch_*" not in gitignore:
gitignore.write("epoch_*\n")
elif args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if accelerator.is_main_process:
run = os.path.split(__file__)[-1].split(".")[0]
accelerator.init_trackers(run)
global_step = 0
for epoch in range(args.num_epochs):
model.train()
progress_bar = tqdm(total=num_update_steps_per_epoch, disable=not accelerator.is_local_main_process)
progress_bar.set_description(f"Epoch {epoch}")
for step, batch in enumerate(train_dataloader):
clean_images = batch["input"]
# Sample noise that we'll add to the images
noise = torch.randn(clean_images.shape).to(clean_images.device)
bsz = clean_images.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(
0, noise_scheduler.config.num_train_timesteps, (bsz,), device=clean_images.device
).long()
# Add noise to the clean images according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)
with accelerator.accumulate(model):
# Predict the noise residual
model_output = model(noisy_images, timesteps).sample
if args.prediction_type == "epsilon":
loss = F.mse_loss(model_output, noise) # this could have different weights!
elif args.prediction_type == "sample":
alpha_t = _extract_into_tensor(
noise_scheduler.alphas_cumprod, timesteps, (clean_images.shape[0], 1, 1, 1)
)
snr_weights = alpha_t / (1 - alpha_t)
loss = snr_weights * F.mse_loss(
model_output, clean_images, reduction="none"
) # use SNR weighting from distillation paper
loss = loss.mean()
else:
raise ValueError(f"Unsupported prediction type: {args.prediction_type}")
accelerator.backward(loss)
if accelerator.sync_gradients:
accelerator.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
lr_scheduler.step()
if args.use_ema:
ema_model.step(model)
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0], "step": global_step}
if args.use_ema:
logs["ema_decay"] = ema_model.decay
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
progress_bar.close()
accelerator.wait_for_everyone()
# Generate sample images for visual inspection
if accelerator.is_main_process:
if epoch % args.save_images_epochs == 0 or epoch == args.num_epochs - 1:
pipeline = DDPMPipeline(
unet=accelerator.unwrap_model(ema_model.averaged_model if args.use_ema else model),
scheduler=noise_scheduler,
)
deprecate("todo: remove this check", "0.10.0", "when the most used version is >= 0.8.0")
if diffusers_version < version.parse("0.8.0"):
generator = torch.manual_seed(0)
else:
generator = torch.Generator(device=pipeline.device).manual_seed(0)
# run pipeline in inference (sample random noise and denoise)
images = pipeline(
generator=generator,
batch_size=args.eval_batch_size,
output_type="numpy",
).images
# denormalize the images and save to tensorboard
images_processed = (images * 255).round().astype("uint8")
accelerator.trackers[0].writer.add_images(
"test_samples", images_processed.transpose(0, 3, 1, 2), epoch
)
if epoch % args.save_model_epochs == 0 or epoch == args.num_epochs - 1:
# save the model
pipeline.save_pretrained(args.output_dir)
if args.push_to_hub:
repo.push_to_hub(commit_message=f"Epoch {epoch}", blocking=False)
accelerator.wait_for_everyone()
accelerator.end_training()
if __name__ == "__main__":
args = parse_args()
main(args)
| diffusers-ft-main | examples/unconditional_image_generation/train_unconditional.py |
import argparse
import math
import os
import torch
import torch.nn.functional as F
from accelerate import Accelerator
from accelerate.logging import get_logger
from datasets import load_dataset
from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
from diffusers.hub_utils import init_git_repo, push_to_hub
from diffusers.optimization import get_scheduler
from diffusers.training_utils import EMAModel
from onnxruntime.training.ortmodule import ORTModule
from torchvision.transforms import (
CenterCrop,
Compose,
InterpolationMode,
Normalize,
RandomHorizontalFlip,
Resize,
ToTensor,
)
from tqdm.auto import tqdm
logger = get_logger(__name__)
def main(args):
logging_dir = os.path.join(args.output_dir, args.logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with="tensorboard",
logging_dir=logging_dir,
)
model = UNet2DModel(
sample_size=args.resolution,
in_channels=3,
out_channels=3,
layers_per_block=2,
block_out_channels=(128, 128, 256, 256, 512, 512),
down_block_types=(
"DownBlock2D",
"DownBlock2D",
"DownBlock2D",
"DownBlock2D",
"AttnDownBlock2D",
"DownBlock2D",
),
up_block_types=(
"UpBlock2D",
"AttnUpBlock2D",
"UpBlock2D",
"UpBlock2D",
"UpBlock2D",
"UpBlock2D",
),
)
model = ORTModule(model)
noise_scheduler = DDPMScheduler(num_train_timesteps=1000, tensor_format="pt")
optimizer = torch.optim.AdamW(
model.parameters(),
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
augmentations = Compose(
[
Resize(args.resolution, interpolation=InterpolationMode.BILINEAR),
CenterCrop(args.resolution),
RandomHorizontalFlip(),
ToTensor(),
Normalize([0.5], [0.5]),
]
)
if args.dataset_name is not None:
dataset = load_dataset(
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
use_auth_token=True if args.use_auth_token else None,
split="train",
)
else:
dataset = load_dataset("imagefolder", data_dir=args.train_data_dir, cache_dir=args.cache_dir, split="train")
def transforms(examples):
images = [augmentations(image.convert("RGB")) for image in examples["image"]]
return {"input": images}
dataset.set_transform(transforms)
train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.train_batch_size, shuffle=True)
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps,
num_training_steps=(len(train_dataloader) * args.num_epochs) // args.gradient_accumulation_steps,
)
model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
model, optimizer, train_dataloader, lr_scheduler
)
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
ema_model = EMAModel(model, inv_gamma=args.ema_inv_gamma, power=args.ema_power, max_value=args.ema_max_decay)
if args.push_to_hub:
repo = init_git_repo(args, at_init=True)
if accelerator.is_main_process:
run = os.path.split(__file__)[-1].split(".")[0]
accelerator.init_trackers(run)
global_step = 0
for epoch in range(args.num_epochs):
model.train()
progress_bar = tqdm(total=num_update_steps_per_epoch, disable=not accelerator.is_local_main_process)
progress_bar.set_description(f"Epoch {epoch}")
for step, batch in enumerate(train_dataloader):
clean_images = batch["input"]
# Sample noise that we'll add to the images
noise = torch.randn(clean_images.shape).to(clean_images.device)
bsz = clean_images.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(
0, noise_scheduler.config.num_train_timesteps, (bsz,), device=clean_images.device
).long()
# Add noise to the clean images according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)
with accelerator.accumulate(model):
# Predict the noise residual
noise_pred = model(noisy_images, timesteps, return_dict=True)[0]
loss = F.mse_loss(noise_pred, noise)
accelerator.backward(loss)
accelerator.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
lr_scheduler.step()
if args.use_ema:
ema_model.step(model)
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0], "step": global_step}
if args.use_ema:
logs["ema_decay"] = ema_model.decay
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
progress_bar.close()
accelerator.wait_for_everyone()
# Generate sample images for visual inspection
if accelerator.is_main_process:
if epoch % args.save_images_epochs == 0 or epoch == args.num_epochs - 1:
pipeline = DDPMPipeline(
unet=accelerator.unwrap_model(ema_model.averaged_model if args.use_ema else model),
scheduler=noise_scheduler,
)
generator = torch.manual_seed(0)
# run pipeline in inference (sample random noise and denoise)
images = pipeline(generator=generator, batch_size=args.eval_batch_size, output_type="numpy").images
# denormalize the images and save to tensorboard
images_processed = (images * 255).round().astype("uint8")
accelerator.trackers[0].writer.add_images(
"test_samples", images_processed.transpose(0, 3, 1, 2), epoch
)
if epoch % args.save_model_epochs == 0 or epoch == args.num_epochs - 1:
# save the model
if args.push_to_hub:
push_to_hub(args, pipeline, repo, commit_message=f"Epoch {epoch}", blocking=False)
else:
pipeline.save_pretrained(args.output_dir)
accelerator.wait_for_everyone()
accelerator.end_training()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument("--local_rank", type=int, default=-1)
parser.add_argument("--dataset_name", type=str, default=None)
parser.add_argument("--dataset_config_name", type=str, default=None)
parser.add_argument("--train_data_dir", type=str, default=None, help="A folder containing the training data.")
parser.add_argument("--output_dir", type=str, default="ddpm-model-64")
parser.add_argument("--overwrite_output_dir", action="store_true")
parser.add_argument("--cache_dir", type=str, default=None)
parser.add_argument("--resolution", type=int, default=64)
parser.add_argument("--train_batch_size", type=int, default=16)
parser.add_argument("--eval_batch_size", type=int, default=16)
parser.add_argument("--num_epochs", type=int, default=100)
parser.add_argument("--save_images_epochs", type=int, default=10)
parser.add_argument("--save_model_epochs", type=int, default=10)
parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
parser.add_argument("--learning_rate", type=float, default=1e-4)
parser.add_argument("--lr_scheduler", type=str, default="cosine")
parser.add_argument("--lr_warmup_steps", type=int, default=500)
parser.add_argument("--adam_beta1", type=float, default=0.95)
parser.add_argument("--adam_beta2", type=float, default=0.999)
parser.add_argument("--adam_weight_decay", type=float, default=1e-6)
parser.add_argument("--adam_epsilon", type=float, default=1e-08)
parser.add_argument("--use_ema", action="store_true", default=True)
parser.add_argument("--ema_inv_gamma", type=float, default=1.0)
parser.add_argument("--ema_power", type=float, default=3 / 4)
parser.add_argument("--ema_max_decay", type=float, default=0.9999)
parser.add_argument("--push_to_hub", action="store_true")
parser.add_argument("--use_auth_token", action="store_true")
parser.add_argument("--hub_token", type=str, default=None)
parser.add_argument("--hub_model_id", type=str, default=None)
parser.add_argument("--hub_private_repo", action="store_true")
parser.add_argument("--logging_dir", type=str, default="logs")
parser.add_argument(
"--mixed_precision",
type=str,
default="no",
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU."
),
)
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.dataset_name is None and args.train_data_dir is None:
raise ValueError("You must specify either a dataset name from the hub or a train data directory.")
main(args)
| diffusers-ft-main | examples/unconditional_image_generation/train_unconditional_ort.py |
import argparse
import itertools
import math
import os
import random
from pathlib import Path
from typing import Optional
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.utils.data import Dataset
import PIL
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
from diffusers.optimization import get_scheduler
from huggingface_hub import HfFolder, Repository, whoami
# TODO: remove and import from diffusers.utils when the new version of diffusers is released
from packaging import version
from PIL import Image
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
PIL_INTERPOLATION = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling.BILINEAR,
"bicubic": PIL.Image.Resampling.BICUBIC,
"lanczos": PIL.Image.Resampling.LANCZOS,
"nearest": PIL.Image.Resampling.NEAREST,
}
else:
PIL_INTERPOLATION = {
"linear": PIL.Image.LINEAR,
"bilinear": PIL.Image.BILINEAR,
"bicubic": PIL.Image.BICUBIC,
"lanczos": PIL.Image.LANCZOS,
"nearest": PIL.Image.NEAREST,
}
# ------------------------------------------------------------------------------
logger = get_logger(__name__)
def save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path):
logger.info("Saving embeddings")
learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_id]
learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()}
torch.save(learned_embeds_dict, save_path)
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--save_steps",
type=int,
default=500,
help="Save learned_embeds.bin every X updates steps.",
)
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--train_data_dir", type=str, default=None, required=True, help="A folder containing the training data."
)
parser.add_argument(
"--placeholder_token",
type=str,
default=None,
required=True,
help="A token to use as a placeholder for the concept.",
)
parser.add_argument(
"--initializer_token", type=str, default=None, required=True, help="A token to use as initializer word."
)
parser.add_argument("--learnable_property", type=str, default="object", help="Choose between 'object' and 'style'")
parser.add_argument("--repeats", type=int, default=100, help="How many times to repeat the training data.")
parser.add_argument(
"--output_dir",
type=str,
default="text-inversion-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
)
parser.add_argument(
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument(
"--max_train_steps",
type=int,
default=5000,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=True,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default="no",
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.train_data_dir is None:
raise ValueError("You must specify a train data directory.")
return args
imagenet_templates_small = [
"a photo of a {}",
"a rendering of a {}",
"a cropped photo of the {}",
"the photo of a {}",
"a photo of a clean {}",
"a photo of a dirty {}",
"a dark photo of the {}",
"a photo of my {}",
"a photo of the cool {}",
"a close-up photo of a {}",
"a bright photo of the {}",
"a cropped photo of a {}",
"a photo of the {}",
"a good photo of the {}",
"a photo of one {}",
"a close-up photo of the {}",
"a rendition of the {}",
"a photo of the clean {}",
"a rendition of a {}",
"a photo of a nice {}",
"a good photo of a {}",
"a photo of the nice {}",
"a photo of the small {}",
"a photo of the weird {}",
"a photo of the large {}",
"a photo of a cool {}",
"a photo of a small {}",
]
imagenet_style_templates_small = [
"a painting in the style of {}",
"a rendering in the style of {}",
"a cropped painting in the style of {}",
"the painting in the style of {}",
"a clean painting in the style of {}",
"a dirty painting in the style of {}",
"a dark painting in the style of {}",
"a picture in the style of {}",
"a cool painting in the style of {}",
"a close-up painting in the style of {}",
"a bright painting in the style of {}",
"a cropped painting in the style of {}",
"a good painting in the style of {}",
"a close-up painting in the style of {}",
"a rendition in the style of {}",
"a nice painting in the style of {}",
"a small painting in the style of {}",
"a weird painting in the style of {}",
"a large painting in the style of {}",
]
class TextualInversionDataset(Dataset):
def __init__(
self,
data_root,
tokenizer,
learnable_property="object", # [object, style]
size=512,
repeats=100,
interpolation="bicubic",
flip_p=0.5,
set="train",
placeholder_token="*",
center_crop=False,
):
self.data_root = data_root
self.tokenizer = tokenizer
self.learnable_property = learnable_property
self.size = size
self.placeholder_token = placeholder_token
self.center_crop = center_crop
self.flip_p = flip_p
self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]
self.num_images = len(self.image_paths)
self._length = self.num_images
if set == "train":
self._length = self.num_images * repeats
self.interpolation = {
"linear": PIL_INTERPOLATION["linear"],
"bilinear": PIL_INTERPOLATION["bilinear"],
"bicubic": PIL_INTERPOLATION["bicubic"],
"lanczos": PIL_INTERPOLATION["lanczos"],
}[interpolation]
self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small
self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)
def __len__(self):
return self._length
def __getitem__(self, i):
example = {}
image = Image.open(self.image_paths[i % self.num_images])
if not image.mode == "RGB":
image = image.convert("RGB")
placeholder_string = self.placeholder_token
text = random.choice(self.templates).format(placeholder_string)
example["input_ids"] = self.tokenizer(
text,
padding="max_length",
truncation=True,
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids[0]
# default to score-sde preprocessing
img = np.array(image).astype(np.uint8)
if self.center_crop:
crop = min(img.shape[0], img.shape[1])
h, w, = (
img.shape[0],
img.shape[1],
)
img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]
image = Image.fromarray(img)
image = image.resize((self.size, self.size), resample=self.interpolation)
image = self.flip_transform(image)
image = np.array(image).astype(np.uint8)
image = (image / 127.5 - 1.0).astype(np.float32)
example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
return example
def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
if token is None:
token = HfFolder.get_token()
if organization is None:
username = whoami(token)["name"]
return f"{username}/{model_id}"
else:
return f"{organization}/{model_id}"
def freeze_params(params):
for param in params:
param.requires_grad = False
def main():
args = parse_args()
logging_dir = os.path.join(args.output_dir, args.logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with="tensorboard",
logging_dir=logging_dir,
)
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.push_to_hub:
if args.hub_model_id is None:
repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
else:
repo_name = args.hub_model_id
repo = Repository(args.output_dir, clone_from=repo_name)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:
gitignore.write("step_*\n")
if "epoch_*" not in gitignore:
gitignore.write("epoch_*\n")
elif args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
# Load the tokenizer and add the placeholder token as a additional special token
if args.tokenizer_name:
tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
elif args.pretrained_model_name_or_path:
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
# Add the placeholder token in tokenizer
num_added_tokens = tokenizer.add_tokens(args.placeholder_token)
if num_added_tokens == 0:
raise ValueError(
f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different"
" `placeholder_token` that is not already in the tokenizer."
)
# Convert the initializer_token, placeholder_token to ids
token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
# Check if initializer_token is a single token or a sequence of tokens
if len(token_ids) > 1:
raise ValueError("The initializer token must be a single token.")
initializer_token_id = token_ids[0]
placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)
# Load models and create wrapper for stable diffusion
text_encoder = CLIPTextModel.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="text_encoder",
revision=args.revision,
)
vae = AutoencoderKL.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="vae",
revision=args.revision,
)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="unet",
revision=args.revision,
)
# Resize the token embeddings as we are adding new special tokens to the tokenizer
text_encoder.resize_token_embeddings(len(tokenizer))
# Initialise the newly added placeholder token with the embeddings of the initializer token
token_embeds = text_encoder.get_input_embeddings().weight.data
token_embeds[placeholder_token_id] = token_embeds[initializer_token_id]
# Freeze vae and unet
freeze_params(vae.parameters())
freeze_params(unet.parameters())
# Freeze all parameters except for the token embeddings in text encoder
params_to_freeze = itertools.chain(
text_encoder.text_model.encoder.parameters(),
text_encoder.text_model.final_layer_norm.parameters(),
text_encoder.text_model.embeddings.position_embedding.parameters(),
)
freeze_params(params_to_freeze)
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Initialize the optimizer
optimizer = torch.optim.AdamW(
text_encoder.get_input_embeddings().parameters(), # only optimize the embeddings
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
noise_scheduler = DDPMScheduler.from_config(args.pretrained_model_name_or_path, subfolder="scheduler")
train_dataset = TextualInversionDataset(
data_root=args.train_data_dir,
tokenizer=tokenizer,
size=args.resolution,
placeholder_token=args.placeholder_token,
repeats=args.repeats,
learnable_property=args.learnable_property,
center_crop=args.center_crop,
set="train",
)
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.train_batch_size, shuffle=True)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
)
text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
text_encoder, optimizer, train_dataloader, lr_scheduler
)
# Move vae and unet to device
vae.to(accelerator.device)
unet.to(accelerator.device)
# Keep vae and unet in eval model as we don't train these
vae.eval()
unet.eval()
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("textual_inversion", config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
global_step = 0
for epoch in range(args.num_train_epochs):
text_encoder.train()
for step, batch in enumerate(train_dataloader):
with accelerator.accumulate(text_encoder):
# Convert images to latent space
latents = vae.encode(batch["pixel_values"]).latent_dist.sample().detach()
latents = latents * 0.18215
# Sample noise that we'll add to the latents
noise = torch.randn(latents.shape).to(latents.device)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(
0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device
).long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Get the text embedding for conditioning
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
# Predict the noise residual
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
loss = F.mse_loss(model_pred, target, reduction="none").mean([1, 2, 3]).mean()
accelerator.backward(loss)
# Zero out the gradients for all token embeddings except the newly added
# embeddings for the concept, as we only want to optimize the concept embeddings
if accelerator.num_processes > 1:
grads = text_encoder.module.get_input_embeddings().weight.grad
else:
grads = text_encoder.get_input_embeddings().weight.grad
# Get the index for tokens that we want to zero the grads for
index_grads_to_zero = torch.arange(len(tokenizer)) != placeholder_token_id
grads.data[index_grads_to_zero, :] = grads.data[index_grads_to_zero, :].fill_(0)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if global_step % args.save_steps == 0:
save_path = os.path.join(args.output_dir, f"learned_embeds-steps-{global_step}.bin")
save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break
accelerator.wait_for_everyone()
# Create the pipeline using using the trained modules and save it.
if accelerator.is_main_process:
pipeline = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
text_encoder=accelerator.unwrap_model(text_encoder),
tokenizer=tokenizer,
vae=vae,
unet=unet,
revision=args.revision,
)
pipeline.save_pretrained(args.output_dir)
# Also save the newly trained embeddings
save_path = os.path.join(args.output_dir, "learned_embeds.bin")
save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)
if args.push_to_hub:
repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
accelerator.end_training()
if __name__ == "__main__":
main()
| diffusers-ft-main | examples/textual_inversion/textual_inversion.py |
import argparse
import logging
import math
import os
import random
from pathlib import Path
from typing import Optional
import numpy as np
import torch
import torch.utils.checkpoint
from torch.utils.data import Dataset
import jax
import jax.numpy as jnp
import optax
import PIL
import transformers
from diffusers import (
FlaxAutoencoderKL,
FlaxDDPMScheduler,
FlaxPNDMScheduler,
FlaxStableDiffusionPipeline,
FlaxUNet2DConditionModel,
)
from diffusers.pipelines.stable_diffusion import FlaxStableDiffusionSafetyChecker
from flax import jax_utils
from flax.training import train_state
from flax.training.common_utils import shard
from huggingface_hub import HfFolder, Repository, whoami
# TODO: remove and import from diffusers.utils when the new version of diffusers is released
from packaging import version
from PIL import Image
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
PIL_INTERPOLATION = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling.BILINEAR,
"bicubic": PIL.Image.Resampling.BICUBIC,
"lanczos": PIL.Image.Resampling.LANCZOS,
"nearest": PIL.Image.Resampling.NEAREST,
}
else:
PIL_INTERPOLATION = {
"linear": PIL.Image.LINEAR,
"bilinear": PIL.Image.BILINEAR,
"bicubic": PIL.Image.BICUBIC,
"lanczos": PIL.Image.LANCZOS,
"nearest": PIL.Image.NEAREST,
}
# ------------------------------------------------------------------------------
logger = logging.getLogger(__name__)
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--train_data_dir", type=str, default=None, required=True, help="A folder containing the training data."
)
parser.add_argument(
"--placeholder_token",
type=str,
default=None,
required=True,
help="A token to use as a placeholder for the concept.",
)
parser.add_argument(
"--initializer_token", type=str, default=None, required=True, help="A token to use as initializer word."
)
parser.add_argument("--learnable_property", type=str, default="object", help="Choose between 'object' and 'style'")
parser.add_argument("--repeats", type=int, default=100, help="How many times to repeat the training data.")
parser.add_argument(
"--output_dir",
type=str,
default="text-inversion-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--seed", type=int, default=42, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
)
parser.add_argument(
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument(
"--max_train_steps",
type=int,
default=5000,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=True,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument(
"--use_auth_token",
action="store_true",
help=(
"Will use the token generated when running `huggingface-cli login` (necessary to use this script with"
" private models)."
),
)
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.train_data_dir is None:
raise ValueError("You must specify a train data directory.")
return args
imagenet_templates_small = [
"a photo of a {}",
"a rendering of a {}",
"a cropped photo of the {}",
"the photo of a {}",
"a photo of a clean {}",
"a photo of a dirty {}",
"a dark photo of the {}",
"a photo of my {}",
"a photo of the cool {}",
"a close-up photo of a {}",
"a bright photo of the {}",
"a cropped photo of a {}",
"a photo of the {}",
"a good photo of the {}",
"a photo of one {}",
"a close-up photo of the {}",
"a rendition of the {}",
"a photo of the clean {}",
"a rendition of a {}",
"a photo of a nice {}",
"a good photo of a {}",
"a photo of the nice {}",
"a photo of the small {}",
"a photo of the weird {}",
"a photo of the large {}",
"a photo of a cool {}",
"a photo of a small {}",
]
imagenet_style_templates_small = [
"a painting in the style of {}",
"a rendering in the style of {}",
"a cropped painting in the style of {}",
"the painting in the style of {}",
"a clean painting in the style of {}",
"a dirty painting in the style of {}",
"a dark painting in the style of {}",
"a picture in the style of {}",
"a cool painting in the style of {}",
"a close-up painting in the style of {}",
"a bright painting in the style of {}",
"a cropped painting in the style of {}",
"a good painting in the style of {}",
"a close-up painting in the style of {}",
"a rendition in the style of {}",
"a nice painting in the style of {}",
"a small painting in the style of {}",
"a weird painting in the style of {}",
"a large painting in the style of {}",
]
class TextualInversionDataset(Dataset):
def __init__(
self,
data_root,
tokenizer,
learnable_property="object", # [object, style]
size=512,
repeats=100,
interpolation="bicubic",
flip_p=0.5,
set="train",
placeholder_token="*",
center_crop=False,
):
self.data_root = data_root
self.tokenizer = tokenizer
self.learnable_property = learnable_property
self.size = size
self.placeholder_token = placeholder_token
self.center_crop = center_crop
self.flip_p = flip_p
self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]
self.num_images = len(self.image_paths)
self._length = self.num_images
if set == "train":
self._length = self.num_images * repeats
self.interpolation = {
"linear": PIL_INTERPOLATION["linear"],
"bilinear": PIL_INTERPOLATION["bilinear"],
"bicubic": PIL_INTERPOLATION["bicubic"],
"lanczos": PIL_INTERPOLATION["lanczos"],
}[interpolation]
self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small
self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)
def __len__(self):
return self._length
def __getitem__(self, i):
example = {}
image = Image.open(self.image_paths[i % self.num_images])
if not image.mode == "RGB":
image = image.convert("RGB")
placeholder_string = self.placeholder_token
text = random.choice(self.templates).format(placeholder_string)
example["input_ids"] = self.tokenizer(
text,
padding="max_length",
truncation=True,
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids[0]
# default to score-sde preprocessing
img = np.array(image).astype(np.uint8)
if self.center_crop:
crop = min(img.shape[0], img.shape[1])
h, w, = (
img.shape[0],
img.shape[1],
)
img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]
image = Image.fromarray(img)
image = image.resize((self.size, self.size), resample=self.interpolation)
image = self.flip_transform(image)
image = np.array(image).astype(np.uint8)
image = (image / 127.5 - 1.0).astype(np.float32)
example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
return example
def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
if token is None:
token = HfFolder.get_token()
if organization is None:
username = whoami(token)["name"]
return f"{username}/{model_id}"
else:
return f"{organization}/{model_id}"
def resize_token_embeddings(model, new_num_tokens, initializer_token_id, placeholder_token_id, rng):
if model.config.vocab_size == new_num_tokens or new_num_tokens is None:
return
model.config.vocab_size = new_num_tokens
params = model.params
old_embeddings = params["text_model"]["embeddings"]["token_embedding"]["embedding"]
old_num_tokens, emb_dim = old_embeddings.shape
initializer = jax.nn.initializers.normal()
new_embeddings = initializer(rng, (new_num_tokens, emb_dim))
new_embeddings = new_embeddings.at[:old_num_tokens].set(old_embeddings)
new_embeddings = new_embeddings.at[placeholder_token_id].set(new_embeddings[initializer_token_id])
params["text_model"]["embeddings"]["token_embedding"]["embedding"] = new_embeddings
model.params = params
return model
def get_params_to_save(params):
return jax.device_get(jax.tree_util.tree_map(lambda x: x[0], params))
def main():
args = parse_args()
if args.seed is not None:
set_seed(args.seed)
if jax.process_index() == 0:
if args.push_to_hub:
if args.hub_model_id is None:
repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
else:
repo_name = args.hub_model_id
repo = Repository(args.output_dir, clone_from=repo_name)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:
gitignore.write("step_*\n")
if "epoch_*" not in gitignore:
gitignore.write("epoch_*\n")
elif args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
if jax.process_index() == 0:
transformers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
# Load the tokenizer and add the placeholder token as a additional special token
if args.tokenizer_name:
tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
elif args.pretrained_model_name_or_path:
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
# Add the placeholder token in tokenizer
num_added_tokens = tokenizer.add_tokens(args.placeholder_token)
if num_added_tokens == 0:
raise ValueError(
f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different"
" `placeholder_token` that is not already in the tokenizer."
)
# Convert the initializer_token, placeholder_token to ids
token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
# Check if initializer_token is a single token or a sequence of tokens
if len(token_ids) > 1:
raise ValueError("The initializer token must be a single token.")
initializer_token_id = token_ids[0]
placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)
# Load models and create wrapper for stable diffusion
text_encoder = FlaxCLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
vae, vae_params = FlaxAutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
# Create sampling rng
rng = jax.random.PRNGKey(args.seed)
rng, _ = jax.random.split(rng)
# Resize the token embeddings as we are adding new special tokens to the tokenizer
text_encoder = resize_token_embeddings(
text_encoder, len(tokenizer), initializer_token_id, placeholder_token_id, rng
)
original_token_embeds = text_encoder.params["text_model"]["embeddings"]["token_embedding"]["embedding"]
train_dataset = TextualInversionDataset(
data_root=args.train_data_dir,
tokenizer=tokenizer,
size=args.resolution,
placeholder_token=args.placeholder_token,
repeats=args.repeats,
learnable_property=args.learnable_property,
center_crop=args.center_crop,
set="train",
)
def collate_fn(examples):
pixel_values = torch.stack([example["pixel_values"] for example in examples])
input_ids = torch.stack([example["input_ids"] for example in examples])
batch = {"pixel_values": pixel_values, "input_ids": input_ids}
batch = {k: v.numpy() for k, v in batch.items()}
return batch
total_train_batch_size = args.train_batch_size * jax.local_device_count()
train_dataloader = torch.utils.data.DataLoader(
train_dataset, batch_size=total_train_batch_size, shuffle=True, drop_last=True, collate_fn=collate_fn
)
# Optimization
if args.scale_lr:
args.learning_rate = args.learning_rate * total_train_batch_size
constant_scheduler = optax.constant_schedule(args.learning_rate)
optimizer = optax.adamw(
learning_rate=constant_scheduler,
b1=args.adam_beta1,
b2=args.adam_beta2,
eps=args.adam_epsilon,
weight_decay=args.adam_weight_decay,
)
def create_mask(params, label_fn):
def _map(params, mask, label_fn):
for k in params:
if label_fn(k):
mask[k] = "token_embedding"
else:
if isinstance(params[k], dict):
mask[k] = {}
_map(params[k], mask[k], label_fn)
else:
mask[k] = "zero"
mask = {}
_map(params, mask, label_fn)
return mask
def zero_grads():
# from https://github.com/deepmind/optax/issues/159#issuecomment-896459491
def init_fn(_):
return ()
def update_fn(updates, state, params=None):
return jax.tree_util.tree_map(jnp.zeros_like, updates), ()
return optax.GradientTransformation(init_fn, update_fn)
# Zero out gradients of layers other than the token embedding layer
tx = optax.multi_transform(
{"token_embedding": optimizer, "zero": zero_grads()},
create_mask(text_encoder.params, lambda s: s == "token_embedding"),
)
state = train_state.TrainState.create(apply_fn=text_encoder.__call__, params=text_encoder.params, tx=tx)
noise_scheduler = FlaxDDPMScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000
)
# Initialize our training
train_rngs = jax.random.split(rng, jax.local_device_count())
# Define gradient train step fn
def train_step(state, vae_params, unet_params, batch, train_rng):
dropout_rng, sample_rng, new_train_rng = jax.random.split(train_rng, 3)
def compute_loss(params):
vae_outputs = vae.apply(
{"params": vae_params}, batch["pixel_values"], deterministic=True, method=vae.encode
)
latents = vae_outputs.latent_dist.sample(sample_rng)
# (NHWC) -> (NCHW)
latents = jnp.transpose(latents, (0, 3, 1, 2))
latents = latents * 0.18215
noise_rng, timestep_rng = jax.random.split(sample_rng)
noise = jax.random.normal(noise_rng, latents.shape)
bsz = latents.shape[0]
timesteps = jax.random.randint(
timestep_rng,
(bsz,),
0,
noise_scheduler.config.num_train_timesteps,
)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
encoder_hidden_states = state.apply_fn(
batch["input_ids"], params=params, dropout_rng=dropout_rng, train=True
)[0]
unet_outputs = unet.apply(
{"params": unet_params}, noisy_latents, timesteps, encoder_hidden_states, train=False
)
noise_pred = unet_outputs.sample
loss = (noise - noise_pred) ** 2
loss = loss.mean()
return loss
grad_fn = jax.value_and_grad(compute_loss)
loss, grad = grad_fn(state.params)
grad = jax.lax.pmean(grad, "batch")
new_state = state.apply_gradients(grads=grad)
# Keep the token embeddings fixed except the newly added embeddings for the concept,
# as we only want to optimize the concept embeddings
token_embeds = original_token_embeds.at[placeholder_token_id].set(
new_state.params["text_model"]["embeddings"]["token_embedding"]["embedding"][placeholder_token_id]
)
new_state.params["text_model"]["embeddings"]["token_embedding"]["embedding"] = token_embeds
metrics = {"loss": loss}
metrics = jax.lax.pmean(metrics, axis_name="batch")
return new_state, metrics, new_train_rng
# Create parallel version of the train and eval step
p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
# Replicate the train state on each device
state = jax_utils.replicate(state)
vae_params = jax_utils.replicate(vae_params)
unet_params = jax_utils.replicate(unet_params)
# Train!
num_update_steps_per_epoch = math.ceil(len(train_dataloader))
# Scheduler and math around the number of training steps.
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel & distributed) = {total_train_batch_size}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
epochs = tqdm(range(args.num_train_epochs), desc=f"Epoch ... (1/{args.num_train_epochs})", position=0)
for epoch in epochs:
# ======================== Training ================================
train_metrics = []
steps_per_epoch = len(train_dataset) // total_train_batch_size
train_step_progress_bar = tqdm(total=steps_per_epoch, desc="Training...", position=1, leave=False)
# train
for batch in train_dataloader:
batch = shard(batch)
state, train_metric, train_rngs = p_train_step(state, vae_params, unet_params, batch, train_rngs)
train_metrics.append(train_metric)
train_step_progress_bar.update(1)
global_step += 1
if global_step >= args.max_train_steps:
break
train_metric = jax_utils.unreplicate(train_metric)
train_step_progress_bar.close()
epochs.write(f"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})")
# Create the pipeline using using the trained modules and save it.
if jax.process_index() == 0:
scheduler = FlaxPNDMScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True
)
safety_checker = FlaxStableDiffusionSafetyChecker.from_pretrained(
"CompVis/stable-diffusion-safety-checker", from_pt=True
)
pipeline = FlaxStableDiffusionPipeline(
text_encoder=text_encoder,
vae=vae,
unet=unet,
tokenizer=tokenizer,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"),
)
pipeline.save_pretrained(
args.output_dir,
params={
"text_encoder": get_params_to_save(state.params),
"vae": get_params_to_save(vae_params),
"unet": get_params_to_save(unet_params),
"safety_checker": safety_checker.params,
},
)
# Also save the newly trained embeddings
learned_embeds = get_params_to_save(state.params)["text_model"]["embeddings"]["token_embedding"]["embedding"][
placeholder_token_id
]
learned_embeds_dict = {args.placeholder_token: learned_embeds}
jnp.save(os.path.join(args.output_dir, "learned_embeds.npy"), learned_embeds_dict)
if args.push_to_hub:
repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
if __name__ == "__main__":
main()
| diffusers-ft-main | examples/textual_inversion/textual_inversion_flax.py |
import argparse
import logging
import math
import os
import random
from pathlib import Path
from typing import Iterable, Optional
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from datasets import load_dataset
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
from diffusers.optimization import get_scheduler
from huggingface_hub import HfFolder, Repository, whoami
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer
logger = get_logger(__name__)
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--train_data_dir",
type=str,
default=None,
help=(
"A folder containing the training data. Folder contents must follow the structure described in"
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
),
)
parser.add_argument(
"--image_column", type=str, default="image", help="The column of the dataset containing an image."
)
parser.add_argument(
"--caption_column",
type=str,
default="text",
help="The column of the dataset containing a caption or a list of captions.",
)
parser.add_argument(
"--max_train_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="sd-model-finetuned",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop",
action="store_true",
help="Whether to center crop images before resizing to resolution (if not set, random crop will be used)",
)
parser.add_argument(
"--random_flip",
action="store_true",
help="whether to randomly flip images horizontally",
)
parser.add_argument(
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
' `"wandb"` and `"comet_ml"`. Use `"all"` (default) to report to all integrations.'
"Only applicable when `--with_tracking` is passed."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
# Sanity checks
if args.dataset_name is None and args.train_data_dir is None:
raise ValueError("Need either a dataset name or a training folder.")
return args
def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
if token is None:
token = HfFolder.get_token()
if organization is None:
username = whoami(token)["name"]
return f"{username}/{model_id}"
else:
return f"{organization}/{model_id}"
dataset_name_mapping = {
"lambdalabs/pokemon-blip-captions": ("image", "text"),
}
# Adapted from torch-ema https://github.com/fadel/pytorch_ema/blob/master/torch_ema/ema.py#L14
class EMAModel:
"""
Exponential Moving Average of models weights
"""
def __init__(self, parameters: Iterable[torch.nn.Parameter], decay=0.9999):
parameters = list(parameters)
self.shadow_params = [p.clone().detach() for p in parameters]
self.decay = decay
self.optimization_step = 0
def get_decay(self, optimization_step):
"""
Compute the decay factor for the exponential moving average.
"""
value = (1 + optimization_step) / (10 + optimization_step)
return 1 - min(self.decay, value)
@torch.no_grad()
def step(self, parameters):
parameters = list(parameters)
self.optimization_step += 1
self.decay = self.get_decay(self.optimization_step)
for s_param, param in zip(self.shadow_params, parameters):
if param.requires_grad:
tmp = self.decay * (s_param - param)
s_param.sub_(tmp)
else:
s_param.copy_(param)
torch.cuda.empty_cache()
def copy_to(self, parameters: Iterable[torch.nn.Parameter]) -> None:
"""
Copy current averaged parameters into given collection of parameters.
Args:
parameters: Iterable of `torch.nn.Parameter`; the parameters to be
updated with the stored moving averages. If `None`, the
parameters with which this `ExponentialMovingAverage` was
initialized will be used.
"""
parameters = list(parameters)
for s_param, param in zip(self.shadow_params, parameters):
param.data.copy_(s_param.data)
def to(self, device=None, dtype=None) -> None:
r"""Move internal buffers of the ExponentialMovingAverage to `device`.
Args:
device: like `device` argument to `torch.Tensor.to`
"""
# .to() on the tensors handles None correctly
self.shadow_params = [
p.to(device=device, dtype=dtype) if p.is_floating_point() else p.to(device=device)
for p in self.shadow_params
]
def main():
args = parse_args()
logging_dir = os.path.join(args.output_dir, args.logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
logging_dir=logging_dir,
)
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.push_to_hub:
if args.hub_model_id is None:
repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
else:
repo_name = args.hub_model_id
repo = Repository(args.output_dir, clone_from=repo_name)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:
gitignore.write("step_*\n")
if "epoch_*" not in gitignore:
gitignore.write("epoch_*\n")
elif args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
# Load models and create wrapper for stable diffusion
tokenizer = CLIPTokenizer.from_pretrained(
args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
)
text_encoder = CLIPTextModel.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="text_encoder",
revision=args.revision,
)
vae = AutoencoderKL.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="vae",
revision=args.revision,
)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="unet",
revision=args.revision,
)
# Freeze vae and text_encoder
vae.requires_grad_(False)
text_encoder.requires_grad_(False)
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Initialize the optimizer
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
)
optimizer_cls = bnb.optim.AdamW8bit
else:
optimizer_cls = torch.optim.AdamW
optimizer = optimizer_cls(
unet.parameters(),
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
noise_scheduler = DDPMScheduler.from_config(args.pretrained_model_name_or_path, subfolder="scheduler")
# Get the datasets: you can either provide your own training and evaluation files (see below)
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
dataset = load_dataset(
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
)
else:
data_files = {}
if args.train_data_dir is not None:
data_files["train"] = os.path.join(args.train_data_dir, "**")
dataset = load_dataset(
"imagefolder",
data_files=data_files,
cache_dir=args.cache_dir,
)
# See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
# Preprocessing the datasets.
# We need to tokenize inputs and targets.
column_names = dataset["train"].column_names
# 6. Get the column names for input/target.
dataset_columns = dataset_name_mapping.get(args.dataset_name, None)
if args.image_column is None:
image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
else:
image_column = args.image_column
if image_column not in column_names:
raise ValueError(
f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}"
)
if args.caption_column is None:
caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
else:
caption_column = args.caption_column
if caption_column not in column_names:
raise ValueError(
f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}"
)
# Preprocessing the datasets.
# We need to tokenize input captions and transform the images.
def tokenize_captions(examples, is_train=True):
captions = []
for caption in examples[caption_column]:
if isinstance(caption, str):
captions.append(caption)
elif isinstance(caption, (list, np.ndarray)):
# take a random caption if there are multiple
captions.append(random.choice(caption) if is_train else caption[0])
else:
raise ValueError(
f"Caption column `{caption_column}` should contain either strings or lists of strings."
)
inputs = tokenizer(captions, max_length=tokenizer.model_max_length, padding="do_not_pad", truncation=True)
input_ids = inputs.input_ids
return input_ids
train_transforms = transforms.Compose(
[
transforms.Resize((args.resolution, args.resolution), interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def preprocess_train(examples):
images = [image.convert("RGB") for image in examples[image_column]]
examples["pixel_values"] = [train_transforms(image) for image in images]
examples["input_ids"] = tokenize_captions(examples)
return examples
with accelerator.main_process_first():
if args.max_train_samples is not None:
dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
# Set the training transforms
train_dataset = dataset["train"].with_transform(preprocess_train)
def collate_fn(examples):
pixel_values = torch.stack([example["pixel_values"] for example in examples])
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
input_ids = [example["input_ids"] for example in examples]
padded_tokens = tokenizer.pad({"input_ids": input_ids}, padding=True, return_tensors="pt")
return {
"pixel_values": pixel_values,
"input_ids": padded_tokens.input_ids,
"attention_mask": padded_tokens.attention_mask,
}
train_dataloader = torch.utils.data.DataLoader(
train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=args.train_batch_size
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
)
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, optimizer, train_dataloader, lr_scheduler
)
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move text_encode and vae to gpu.
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
text_encoder.to(accelerator.device, dtype=weight_dtype)
vae.to(accelerator.device, dtype=weight_dtype)
# Create EMA for the unet.
if args.use_ema:
ema_unet = EMAModel(unet.parameters())
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("text2image-fine-tune", config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
global_step = 0
for epoch in range(args.num_train_epochs):
unet.train()
train_loss = 0.0
for step, batch in enumerate(train_dataloader):
with accelerator.accumulate(unet):
# Convert images to latent space
latents = vae.encode(batch["pixel_values"].to(weight_dtype)).latent_dist.sample()
latents = latents * 0.18215
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), device=latents.device)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Get the text embedding for conditioning
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
# Predict the noise residual and compute loss
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
# Gather the losses across all processes for logging (if we use distributed training).
avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
train_loss += avg_loss.item() / args.gradient_accumulation_steps
# Backpropagate
accelerator.backward(loss)
if accelerator.sync_gradients:
accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
if args.use_ema:
ema_unet.step(unet.parameters())
progress_bar.update(1)
global_step += 1
accelerator.log({"train_loss": train_loss}, step=global_step)
train_loss = 0.0
logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
if global_step >= args.max_train_steps:
break
# Create the pipeline using the trained modules and save it.
accelerator.wait_for_everyone()
if accelerator.is_main_process:
unet = accelerator.unwrap_model(unet)
if args.use_ema:
ema_unet.copy_to(unet.parameters())
pipeline = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
text_encoder=text_encoder,
vae=vae,
unet=unet,
revision=args.revision,
)
pipeline.save_pretrained(args.output_dir)
if args.push_to_hub:
repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
accelerator.end_training()
if __name__ == "__main__":
main()
| diffusers-ft-main | examples/text_to_image/train_text_to_image.py |
import argparse
import logging
import math
import os
import random
from pathlib import Path
from typing import Optional
import numpy as np
import torch
import torch.utils.checkpoint
import jax
import jax.numpy as jnp
import optax
import transformers
from datasets import load_dataset
from diffusers import (
FlaxAutoencoderKL,
FlaxDDPMScheduler,
FlaxPNDMScheduler,
FlaxStableDiffusionPipeline,
FlaxUNet2DConditionModel,
)
from diffusers.pipelines.stable_diffusion import FlaxStableDiffusionSafetyChecker
from flax import jax_utils
from flax.training import train_state
from flax.training.common_utils import shard
from huggingface_hub import HfFolder, Repository, whoami
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
logger = logging.getLogger(__name__)
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--train_data_dir",
type=str,
default=None,
help=(
"A folder containing the training data. Folder contents must follow the structure described in"
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
),
)
parser.add_argument(
"--image_column", type=str, default="image", help="The column of the dataset containing an image."
)
parser.add_argument(
"--caption_column",
type=str,
default="text",
help="The column of the dataset containing a caption or a list of captions.",
)
parser.add_argument(
"--max_train_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="sd-model-finetuned",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument("--seed", type=int, default=0, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop",
action="store_true",
help="Whether to center crop images before resizing to resolution (if not set, random crop will be used)",
)
parser.add_argument(
"--random_flip",
action="store_true",
help="whether to randomly flip images horizontally",
)
parser.add_argument(
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
' `"wandb"` and `"comet_ml"`. Use `"all"` (default) to report to all integrations.'
"Only applicable when `--with_tracking` is passed."
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default="no",
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
# Sanity checks
if args.dataset_name is None and args.train_data_dir is None:
raise ValueError("Need either a dataset name or a training folder.")
return args
def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
if token is None:
token = HfFolder.get_token()
if organization is None:
username = whoami(token)["name"]
return f"{username}/{model_id}"
else:
return f"{organization}/{model_id}"
dataset_name_mapping = {
"lambdalabs/pokemon-blip-captions": ("image", "text"),
}
def get_params_to_save(params):
return jax.device_get(jax.tree_util.tree_map(lambda x: x[0], params))
def main():
args = parse_args()
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
if jax.process_index() == 0:
transformers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if jax.process_index() == 0:
if args.push_to_hub:
if args.hub_model_id is None:
repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
else:
repo_name = args.hub_model_id
repo = Repository(args.output_dir, clone_from=repo_name)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:
gitignore.write("step_*\n")
if "epoch_*" not in gitignore:
gitignore.write("epoch_*\n")
elif args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
# Get the datasets: you can either provide your own training and evaluation files (see below)
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
dataset = load_dataset(
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
)
else:
data_files = {}
if args.train_data_dir is not None:
data_files["train"] = os.path.join(args.train_data_dir, "**")
dataset = load_dataset(
"imagefolder",
data_files=data_files,
cache_dir=args.cache_dir,
)
# See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
# Preprocessing the datasets.
# We need to tokenize inputs and targets.
column_names = dataset["train"].column_names
# 6. Get the column names for input/target.
dataset_columns = dataset_name_mapping.get(args.dataset_name, None)
if args.image_column is None:
image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
else:
image_column = args.image_column
if image_column not in column_names:
raise ValueError(
f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}"
)
if args.caption_column is None:
caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
else:
caption_column = args.caption_column
if caption_column not in column_names:
raise ValueError(
f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}"
)
# Preprocessing the datasets.
# We need to tokenize input captions and transform the images.
def tokenize_captions(examples, is_train=True):
captions = []
for caption in examples[caption_column]:
if isinstance(caption, str):
captions.append(caption)
elif isinstance(caption, (list, np.ndarray)):
# take a random caption if there are multiple
captions.append(random.choice(caption) if is_train else caption[0])
else:
raise ValueError(
f"Caption column `{caption_column}` should contain either strings or lists of strings."
)
inputs = tokenizer(captions, max_length=tokenizer.model_max_length, padding="do_not_pad", truncation=True)
input_ids = inputs.input_ids
return input_ids
train_transforms = transforms.Compose(
[
transforms.Resize((args.resolution, args.resolution), interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def preprocess_train(examples):
images = [image.convert("RGB") for image in examples[image_column]]
examples["pixel_values"] = [train_transforms(image) for image in images]
examples["input_ids"] = tokenize_captions(examples)
return examples
if jax.process_index() == 0:
if args.max_train_samples is not None:
dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
# Set the training transforms
train_dataset = dataset["train"].with_transform(preprocess_train)
def collate_fn(examples):
pixel_values = torch.stack([example["pixel_values"] for example in examples])
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
input_ids = [example["input_ids"] for example in examples]
padded_tokens = tokenizer.pad(
{"input_ids": input_ids}, padding="max_length", max_length=tokenizer.model_max_length, return_tensors="pt"
)
batch = {
"pixel_values": pixel_values,
"input_ids": padded_tokens.input_ids,
}
batch = {k: v.numpy() for k, v in batch.items()}
return batch
total_train_batch_size = args.train_batch_size * jax.local_device_count()
train_dataloader = torch.utils.data.DataLoader(
train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=total_train_batch_size, drop_last=True
)
weight_dtype = jnp.float32
if args.mixed_precision == "fp16":
weight_dtype = jnp.float16
elif args.mixed_precision == "bf16":
weight_dtype = jnp.bfloat16
# Load models and create wrapper for stable diffusion
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
text_encoder = FlaxCLIPTextModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", dtype=weight_dtype
)
vae, vae_params = FlaxAutoencoderKL.from_pretrained(
args.pretrained_model_name_or_path, subfolder="vae", dtype=weight_dtype
)
unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", dtype=weight_dtype
)
# Optimization
if args.scale_lr:
args.learning_rate = args.learning_rate * total_train_batch_size
constant_scheduler = optax.constant_schedule(args.learning_rate)
adamw = optax.adamw(
learning_rate=constant_scheduler,
b1=args.adam_beta1,
b2=args.adam_beta2,
eps=args.adam_epsilon,
weight_decay=args.adam_weight_decay,
)
optimizer = optax.chain(
optax.clip_by_global_norm(args.max_grad_norm),
adamw,
)
state = train_state.TrainState.create(apply_fn=unet.__call__, params=unet_params, tx=optimizer)
noise_scheduler = FlaxDDPMScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000
)
# Initialize our training
rng = jax.random.PRNGKey(args.seed)
train_rngs = jax.random.split(rng, jax.local_device_count())
def train_step(state, text_encoder_params, vae_params, batch, train_rng):
dropout_rng, sample_rng, new_train_rng = jax.random.split(train_rng, 3)
def compute_loss(params):
# Convert images to latent space
vae_outputs = vae.apply(
{"params": vae_params}, batch["pixel_values"], deterministic=True, method=vae.encode
)
latents = vae_outputs.latent_dist.sample(sample_rng)
# (NHWC) -> (NCHW)
latents = jnp.transpose(latents, (0, 3, 1, 2))
latents = latents * 0.18215
# Sample noise that we'll add to the latents
noise_rng, timestep_rng = jax.random.split(sample_rng)
noise = jax.random.normal(noise_rng, latents.shape)
# Sample a random timestep for each image
bsz = latents.shape[0]
timesteps = jax.random.randint(
timestep_rng,
(bsz,),
0,
noise_scheduler.config.num_train_timesteps,
)
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Get the text embedding for conditioning
encoder_hidden_states = text_encoder(
batch["input_ids"],
params=text_encoder_params,
train=False,
)[0]
# Predict the noise residual and compute loss
unet_outputs = unet.apply({"params": params}, noisy_latents, timesteps, encoder_hidden_states, train=True)
noise_pred = unet_outputs.sample
loss = (noise - noise_pred) ** 2
loss = loss.mean()
return loss
grad_fn = jax.value_and_grad(compute_loss)
loss, grad = grad_fn(state.params)
grad = jax.lax.pmean(grad, "batch")
new_state = state.apply_gradients(grads=grad)
metrics = {"loss": loss}
metrics = jax.lax.pmean(metrics, axis_name="batch")
return new_state, metrics, new_train_rng
# Create parallel version of the train step
p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
# Replicate the train state on each device
state = jax_utils.replicate(state)
text_encoder_params = jax_utils.replicate(text_encoder.params)
vae_params = jax_utils.replicate(vae_params)
# Train!
num_update_steps_per_epoch = math.ceil(len(train_dataloader))
# Scheduler and math around the number of training steps.
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel & distributed) = {total_train_batch_size}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
epochs = tqdm(range(args.num_train_epochs), desc="Epoch ... ", position=0)
for epoch in epochs:
# ======================== Training ================================
train_metrics = []
steps_per_epoch = len(train_dataset) // total_train_batch_size
train_step_progress_bar = tqdm(total=steps_per_epoch, desc="Training...", position=1, leave=False)
# train
for batch in train_dataloader:
batch = shard(batch)
state, train_metric, train_rngs = p_train_step(state, text_encoder_params, vae_params, batch, train_rngs)
train_metrics.append(train_metric)
train_step_progress_bar.update(1)
global_step += 1
if global_step >= args.max_train_steps:
break
train_metric = jax_utils.unreplicate(train_metric)
train_step_progress_bar.close()
epochs.write(f"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})")
# Create the pipeline using using the trained modules and save it.
if jax.process_index() == 0:
scheduler = FlaxPNDMScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True
)
safety_checker = FlaxStableDiffusionSafetyChecker.from_pretrained(
"CompVis/stable-diffusion-safety-checker", from_pt=True
)
pipeline = FlaxStableDiffusionPipeline(
text_encoder=text_encoder,
vae=vae,
unet=unet,
tokenizer=tokenizer,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"),
)
pipeline.save_pretrained(
args.output_dir,
params={
"text_encoder": get_params_to_save(text_encoder_params),
"vae": get_params_to_save(vae_params),
"unet": get_params_to_save(state.params),
"safety_checker": safety_checker.params,
},
)
if args.push_to_hub:
repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
if __name__ == "__main__":
main()
| diffusers-ft-main | examples/text_to_image/train_text_to_image_flax.py |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"The `inpainting.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionInpaintPipeline` instead."
)
| diffusers-ft-main | examples/inference/inpainting.py |
import warnings
from diffusers import StableDiffusionImg2ImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| diffusers-ft-main | examples/inference/image_to_image.py |
import argparse
import hashlib
import logging
import math
import os
from pathlib import Path
from typing import Optional
import numpy as np
import torch
import torch.utils.checkpoint
from torch.utils.data import Dataset
import jax
import jax.numpy as jnp
import optax
import transformers
from diffusers import (
FlaxAutoencoderKL,
FlaxDDPMScheduler,
FlaxPNDMScheduler,
FlaxStableDiffusionPipeline,
FlaxUNet2DConditionModel,
)
from diffusers.pipelines.stable_diffusion import FlaxStableDiffusionSafetyChecker
from flax import jax_utils
from flax.training import train_state
from flax.training.common_utils import shard
from huggingface_hub import HfFolder, Repository, whoami
from PIL import Image
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
logger = logging.getLogger(__name__)
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--instance_data_dir",
type=str,
default=None,
required=True,
help="A folder containing the training data of instance images.",
)
parser.add_argument(
"--class_data_dir",
type=str,
default=None,
required=False,
help="A folder containing the training data of class images.",
)
parser.add_argument(
"--instance_prompt",
type=str,
default=None,
help="The prompt with identifier specifying the instance",
)
parser.add_argument(
"--class_prompt",
type=str,
default=None,
help="The prompt to specify images in the same class as provided instance images.",
)
parser.add_argument(
"--with_prior_preservation",
default=False,
action="store_true",
help="Flag to add prior preservation loss.",
)
parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
parser.add_argument(
"--num_class_images",
type=int,
default=100,
help=(
"Minimal class images for prior preservation loss. If not have enough images, additional images will be"
" sampled with class_prompt."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="text-inversion-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--seed", type=int, default=0, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
)
parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder")
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument(
"--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-6,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default="no",
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.instance_data_dir is None:
raise ValueError("You must specify a train data directory.")
if args.with_prior_preservation:
if args.class_data_dir is None:
raise ValueError("You must specify a data directory for class images.")
if args.class_prompt is None:
raise ValueError("You must specify prompt for class images.")
return args
class DreamBoothDataset(Dataset):
"""
A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
It pre-processes the images and the tokenizes prompts.
"""
def __init__(
self,
instance_data_root,
instance_prompt,
tokenizer,
class_data_root=None,
class_prompt=None,
size=512,
center_crop=False,
):
self.size = size
self.center_crop = center_crop
self.tokenizer = tokenizer
self.instance_data_root = Path(instance_data_root)
if not self.instance_data_root.exists():
raise ValueError("Instance images root doesn't exists.")
self.instance_images_path = list(Path(instance_data_root).iterdir())
self.num_instance_images = len(self.instance_images_path)
self.instance_prompt = instance_prompt
self._length = self.num_instance_images
if class_data_root is not None:
self.class_data_root = Path(class_data_root)
self.class_data_root.mkdir(parents=True, exist_ok=True)
self.class_images_path = list(self.class_data_root.iterdir())
self.num_class_images = len(self.class_images_path)
self._length = max(self.num_class_images, self.num_instance_images)
self.class_prompt = class_prompt
else:
self.class_data_root = None
self.image_transforms = transforms.Compose(
[
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __len__(self):
return self._length
def __getitem__(self, index):
example = {}
instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
if not instance_image.mode == "RGB":
instance_image = instance_image.convert("RGB")
example["instance_images"] = self.image_transforms(instance_image)
example["instance_prompt_ids"] = self.tokenizer(
self.instance_prompt,
padding="do_not_pad",
truncation=True,
max_length=self.tokenizer.model_max_length,
).input_ids
if self.class_data_root:
class_image = Image.open(self.class_images_path[index % self.num_class_images])
if not class_image.mode == "RGB":
class_image = class_image.convert("RGB")
example["class_images"] = self.image_transforms(class_image)
example["class_prompt_ids"] = self.tokenizer(
self.class_prompt,
padding="do_not_pad",
truncation=True,
max_length=self.tokenizer.model_max_length,
).input_ids
return example
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
def __init__(self, prompt, num_samples):
self.prompt = prompt
self.num_samples = num_samples
def __len__(self):
return self.num_samples
def __getitem__(self, index):
example = {}
example["prompt"] = self.prompt
example["index"] = index
return example
def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
if token is None:
token = HfFolder.get_token()
if organization is None:
username = whoami(token)["name"]
return f"{username}/{model_id}"
else:
return f"{organization}/{model_id}"
def get_params_to_save(params):
return jax.device_get(jax.tree_util.tree_map(lambda x: x[0], params))
def main():
args = parse_args()
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
if jax.process_index() == 0:
transformers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
if args.seed is not None:
set_seed(args.seed)
rng = jax.random.PRNGKey(args.seed)
if args.with_prior_preservation:
class_images_dir = Path(args.class_data_dir)
if not class_images_dir.exists():
class_images_dir.mkdir(parents=True)
cur_class_images = len(list(class_images_dir.iterdir()))
if cur_class_images < args.num_class_images:
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, safety_checker=None
)
pipeline.set_progress_bar_config(disable=True)
num_new_images = args.num_class_images - cur_class_images
logger.info(f"Number of class images to sample: {num_new_images}.")
sample_dataset = PromptDataset(args.class_prompt, num_new_images)
total_sample_batch_size = args.sample_batch_size * jax.local_device_count()
sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=total_sample_batch_size)
for example in tqdm(
sample_dataloader, desc="Generating class images", disable=not jax.process_index() == 0
):
prompt_ids = pipeline.prepare_inputs(example["prompt"])
prompt_ids = shard(prompt_ids)
p_params = jax_utils.replicate(params)
rng = jax.random.split(rng)[0]
sample_rng = jax.random.split(rng, jax.device_count())
images = pipeline(prompt_ids, p_params, sample_rng, jit=True).images
images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
images = pipeline.numpy_to_pil(np.array(images))
for i, image in enumerate(images):
hash_image = hashlib.sha1(image.tobytes()).hexdigest()
image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
image.save(image_filename)
del pipeline
# Handle the repository creation
if jax.process_index() == 0:
if args.push_to_hub:
if args.hub_model_id is None:
repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
else:
repo_name = args.hub_model_id
repo = Repository(args.output_dir, clone_from=repo_name)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:
gitignore.write("step_*\n")
if "epoch_*" not in gitignore:
gitignore.write("epoch_*\n")
elif args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
# Load the tokenizer and add the placeholder token as a additional special token
if args.tokenizer_name:
tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
elif args.pretrained_model_name_or_path:
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
train_dataset = DreamBoothDataset(
instance_data_root=args.instance_data_dir,
instance_prompt=args.instance_prompt,
class_data_root=args.class_data_dir if args.with_prior_preservation else None,
class_prompt=args.class_prompt,
tokenizer=tokenizer,
size=args.resolution,
center_crop=args.center_crop,
)
def collate_fn(examples):
input_ids = [example["instance_prompt_ids"] for example in examples]
pixel_values = [example["instance_images"] for example in examples]
# Concat class and instance examples for prior preservation.
# We do this to avoid doing two forward passes.
if args.with_prior_preservation:
input_ids += [example["class_prompt_ids"] for example in examples]
pixel_values += [example["class_images"] for example in examples]
pixel_values = torch.stack(pixel_values)
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
input_ids = tokenizer.pad(
{"input_ids": input_ids}, padding="max_length", max_length=tokenizer.model_max_length, return_tensors="pt"
).input_ids
batch = {
"input_ids": input_ids,
"pixel_values": pixel_values,
}
batch = {k: v.numpy() for k, v in batch.items()}
return batch
total_train_batch_size = args.train_batch_size * jax.local_device_count()
train_dataloader = torch.utils.data.DataLoader(
train_dataset, batch_size=total_train_batch_size, shuffle=True, collate_fn=collate_fn, drop_last=True
)
weight_dtype = jnp.float32
if args.mixed_precision == "fp16":
weight_dtype = jnp.float16
elif args.mixed_precision == "bf16":
weight_dtype = jnp.bfloat16
# Load models and create wrapper for stable diffusion
text_encoder = FlaxCLIPTextModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", dtype=weight_dtype
)
vae, vae_params = FlaxAutoencoderKL.from_pretrained(
args.pretrained_model_name_or_path, subfolder="vae", dtype=weight_dtype
)
unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", dtype=weight_dtype
)
# Optimization
if args.scale_lr:
args.learning_rate = args.learning_rate * total_train_batch_size
constant_scheduler = optax.constant_schedule(args.learning_rate)
adamw = optax.adamw(
learning_rate=constant_scheduler,
b1=args.adam_beta1,
b2=args.adam_beta2,
eps=args.adam_epsilon,
weight_decay=args.adam_weight_decay,
)
optimizer = optax.chain(
optax.clip_by_global_norm(args.max_grad_norm),
adamw,
)
unet_state = train_state.TrainState.create(apply_fn=unet.__call__, params=unet_params, tx=optimizer)
text_encoder_state = train_state.TrainState.create(
apply_fn=text_encoder.__call__, params=text_encoder.params, tx=optimizer
)
noise_scheduler = FlaxDDPMScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000
)
# Initialize our training
train_rngs = jax.random.split(rng, jax.local_device_count())
def train_step(unet_state, text_encoder_state, vae_params, batch, train_rng):
dropout_rng, sample_rng, new_train_rng = jax.random.split(train_rng, 3)
if args.train_text_encoder:
params = {"text_encoder": text_encoder_state.params, "unet": unet_state.params}
else:
params = {"unet": unet_state.params}
def compute_loss(params):
# Convert images to latent space
vae_outputs = vae.apply(
{"params": vae_params}, batch["pixel_values"], deterministic=True, method=vae.encode
)
latents = vae_outputs.latent_dist.sample(sample_rng)
# (NHWC) -> (NCHW)
latents = jnp.transpose(latents, (0, 3, 1, 2))
latents = latents * 0.18215
# Sample noise that we'll add to the latents
noise_rng, timestep_rng = jax.random.split(sample_rng)
noise = jax.random.normal(noise_rng, latents.shape)
# Sample a random timestep for each image
bsz = latents.shape[0]
timesteps = jax.random.randint(
timestep_rng,
(bsz,),
0,
noise_scheduler.config.num_train_timesteps,
)
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Get the text embedding for conditioning
if args.train_text_encoder:
encoder_hidden_states = text_encoder_state.apply_fn(
batch["input_ids"], params=params["text_encoder"], dropout_rng=dropout_rng, train=True
)[0]
else:
encoder_hidden_states = text_encoder(
batch["input_ids"], params=text_encoder_state.params, train=False
)[0]
# Predict the noise residual
unet_outputs = unet.apply(
{"params": params["unet"]}, noisy_latents, timesteps, encoder_hidden_states, train=True
)
noise_pred = unet_outputs.sample
if args.with_prior_preservation:
# Chunk the noise and noise_pred into two parts and compute the loss on each part separately.
noise_pred, noise_pred_prior = jnp.split(noise_pred, 2, axis=0)
noise, noise_prior = jnp.split(noise, 2, axis=0)
# Compute instance loss
loss = (noise - noise_pred) ** 2
loss = loss.mean()
# Compute prior loss
prior_loss = (noise_prior - noise_pred_prior) ** 2
prior_loss = prior_loss.mean()
# Add the prior loss to the instance loss.
loss = loss + args.prior_loss_weight * prior_loss
else:
loss = (noise - noise_pred) ** 2
loss = loss.mean()
return loss
grad_fn = jax.value_and_grad(compute_loss)
loss, grad = grad_fn(params)
grad = jax.lax.pmean(grad, "batch")
new_unet_state = unet_state.apply_gradients(grads=grad["unet"])
if args.train_text_encoder:
new_text_encoder_state = text_encoder_state.apply_gradients(grads=grad["text_encoder"])
else:
new_text_encoder_state = text_encoder_state
metrics = {"loss": loss}
metrics = jax.lax.pmean(metrics, axis_name="batch")
return new_unet_state, new_text_encoder_state, metrics, new_train_rng
# Create parallel version of the train step
p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0, 1))
# Replicate the train state on each device
unet_state = jax_utils.replicate(unet_state)
text_encoder_state = jax_utils.replicate(text_encoder_state)
vae_params = jax_utils.replicate(vae_params)
# Train!
num_update_steps_per_epoch = math.ceil(len(train_dataloader))
# Scheduler and math around the number of training steps.
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel & distributed) = {total_train_batch_size}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
epochs = tqdm(range(args.num_train_epochs), desc="Epoch ... ", position=0)
for epoch in epochs:
# ======================== Training ================================
train_metrics = []
steps_per_epoch = len(train_dataset) // total_train_batch_size
train_step_progress_bar = tqdm(total=steps_per_epoch, desc="Training...", position=1, leave=False)
# train
for batch in train_dataloader:
batch = shard(batch)
unet_state, text_encoder_state, train_metric, train_rngs = p_train_step(
unet_state, text_encoder_state, vae_params, batch, train_rngs
)
train_metrics.append(train_metric)
train_step_progress_bar.update(1)
global_step += 1
if global_step >= args.max_train_steps:
break
train_metric = jax_utils.unreplicate(train_metric)
train_step_progress_bar.close()
epochs.write(f"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})")
# Create the pipeline using using the trained modules and save it.
if jax.process_index() == 0:
scheduler = FlaxPNDMScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True
)
safety_checker = FlaxStableDiffusionSafetyChecker.from_pretrained(
"CompVis/stable-diffusion-safety-checker", from_pt=True
)
pipeline = FlaxStableDiffusionPipeline(
text_encoder=text_encoder,
vae=vae,
unet=unet,
tokenizer=tokenizer,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"),
)
pipeline.save_pretrained(
args.output_dir,
params={
"text_encoder": get_params_to_save(text_encoder_state.params),
"vae": get_params_to_save(vae_params),
"unet": get_params_to_save(unet_state.params),
"safety_checker": safety_checker.params,
},
)
if args.push_to_hub:
repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
if __name__ == "__main__":
main()
| diffusers-ft-main | examples/dreambooth/train_dreambooth_flax.py |
import argparse
import hashlib
import itertools
import math
import os
from pathlib import Path
from typing import Optional
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.utils.data import Dataset
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
from diffusers.optimization import get_scheduler
from huggingface_hub import HfFolder, Repository, whoami
from PIL import Image
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer
logger = get_logger(__name__)
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--instance_data_dir",
type=str,
default=None,
required=True,
help="A folder containing the training data of instance images.",
)
parser.add_argument(
"--class_data_dir",
type=str,
default=None,
required=False,
help="A folder containing the training data of class images.",
)
parser.add_argument(
"--instance_prompt",
type=str,
default=None,
required=True,
help="The prompt with identifier specifying the instance",
)
parser.add_argument(
"--class_prompt",
type=str,
default=None,
help="The prompt to specify images in the same class as provided instance images.",
)
parser.add_argument(
"--with_prior_preservation",
default=False,
action="store_true",
help="Flag to add prior preservation loss.",
)
parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
parser.add_argument(
"--num_class_images",
type=int,
default=100,
help=(
"Minimal class images for prior preservation loss. If not have enough images, additional images will be"
" sampled with class_prompt."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="text-inversion-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
)
parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder")
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument(
"--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.")
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-6,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
if input_args is not None:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.with_prior_preservation:
if args.class_data_dir is None:
raise ValueError("You must specify a data directory for class images.")
if args.class_prompt is None:
raise ValueError("You must specify prompt for class images.")
else:
if args.class_data_dir is not None:
logger.warning("You need not use --class_data_dir without --with_prior_preservation.")
if args.class_prompt is not None:
logger.warning("You need not use --class_prompt without --with_prior_preservation.")
return args
class DreamBoothDataset(Dataset):
"""
A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
It pre-processes the images and the tokenizes prompts.
"""
def __init__(
self,
instance_data_root,
instance_prompt,
tokenizer,
class_data_root=None,
class_prompt=None,
size=512,
center_crop=False,
):
self.size = size
self.center_crop = center_crop
self.tokenizer = tokenizer
self.instance_data_root = Path(instance_data_root)
if not self.instance_data_root.exists():
raise ValueError("Instance images root doesn't exists.")
self.instance_images_path = list(Path(instance_data_root).iterdir())
self.num_instance_images = len(self.instance_images_path)
self.instance_prompt = instance_prompt
self._length = self.num_instance_images
if class_data_root is not None:
self.class_data_root = Path(class_data_root)
self.class_data_root.mkdir(parents=True, exist_ok=True)
self.class_images_path = list(self.class_data_root.iterdir())
self.num_class_images = len(self.class_images_path)
self._length = max(self.num_class_images, self.num_instance_images)
self.class_prompt = class_prompt
else:
self.class_data_root = None
self.image_transforms = transforms.Compose(
[
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __len__(self):
return self._length
def __getitem__(self, index):
example = {}
instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
if not instance_image.mode == "RGB":
instance_image = instance_image.convert("RGB")
example["instance_images"] = self.image_transforms(instance_image)
example["instance_prompt_ids"] = self.tokenizer(
self.instance_prompt,
padding="do_not_pad",
truncation=True,
max_length=self.tokenizer.model_max_length,
).input_ids
if self.class_data_root:
class_image = Image.open(self.class_images_path[index % self.num_class_images])
if not class_image.mode == "RGB":
class_image = class_image.convert("RGB")
example["class_images"] = self.image_transforms(class_image)
example["class_prompt_ids"] = self.tokenizer(
self.class_prompt,
padding="do_not_pad",
truncation=True,
max_length=self.tokenizer.model_max_length,
).input_ids
return example
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
def __init__(self, prompt, num_samples):
self.prompt = prompt
self.num_samples = num_samples
def __len__(self):
return self.num_samples
def __getitem__(self, index):
example = {}
example["prompt"] = self.prompt
example["index"] = index
return example
def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
if token is None:
token = HfFolder.get_token()
if organization is None:
username = whoami(token)["name"]
return f"{username}/{model_id}"
else:
return f"{organization}/{model_id}"
def main(args):
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with="tensorboard",
logging_dir=logging_dir,
)
# Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
# This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
# TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:
raise ValueError(
"Gradient accumulation is not supported when training the text encoder in distributed training. "
"Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
)
if args.seed is not None:
set_seed(args.seed)
if args.with_prior_preservation:
class_images_dir = Path(args.class_data_dir)
if not class_images_dir.exists():
class_images_dir.mkdir(parents=True)
cur_class_images = len(list(class_images_dir.iterdir()))
if cur_class_images < args.num_class_images:
torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32
pipeline = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
torch_dtype=torch_dtype,
safety_checker=None,
revision=args.revision,
)
pipeline.set_progress_bar_config(disable=True)
num_new_images = args.num_class_images - cur_class_images
logger.info(f"Number of class images to sample: {num_new_images}.")
sample_dataset = PromptDataset(args.class_prompt, num_new_images)
sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
sample_dataloader = accelerator.prepare(sample_dataloader)
pipeline.to(accelerator.device)
for example in tqdm(
sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
):
images = pipeline(example["prompt"]).images
for i, image in enumerate(images):
hash_image = hashlib.sha1(image.tobytes()).hexdigest()
image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
image.save(image_filename)
del pipeline
if torch.cuda.is_available():
torch.cuda.empty_cache()
# Handle the repository creation
if accelerator.is_main_process:
if args.push_to_hub:
if args.hub_model_id is None:
repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
else:
repo_name = args.hub_model_id
repo = Repository(args.output_dir, clone_from=repo_name)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:
gitignore.write("step_*\n")
if "epoch_*" not in gitignore:
gitignore.write("epoch_*\n")
elif args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
# Load the tokenizer
if args.tokenizer_name:
tokenizer = CLIPTokenizer.from_pretrained(
args.tokenizer_name,
revision=args.revision,
)
elif args.pretrained_model_name_or_path:
tokenizer = CLIPTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=args.revision,
)
# Load models and create wrapper for stable diffusion
text_encoder = CLIPTextModel.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="text_encoder",
revision=args.revision,
)
vae = AutoencoderKL.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="vae",
revision=args.revision,
)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="unet",
revision=args.revision,
)
vae.requires_grad_(False)
if not args.train_text_encoder:
text_encoder.requires_grad_(False)
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
if args.train_text_encoder:
text_encoder.gradient_checkpointing_enable()
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
)
optimizer_class = bnb.optim.AdamW8bit
else:
optimizer_class = torch.optim.AdamW
params_to_optimize = (
itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters()
)
optimizer = optimizer_class(
params_to_optimize,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
noise_scheduler = DDPMScheduler.from_config(args.pretrained_model_name_or_path, subfolder="scheduler")
train_dataset = DreamBoothDataset(
instance_data_root=args.instance_data_dir,
instance_prompt=args.instance_prompt,
class_data_root=args.class_data_dir if args.with_prior_preservation else None,
class_prompt=args.class_prompt,
tokenizer=tokenizer,
size=args.resolution,
center_crop=args.center_crop,
)
def collate_fn(examples):
input_ids = [example["instance_prompt_ids"] for example in examples]
pixel_values = [example["instance_images"] for example in examples]
# Concat class and instance examples for prior preservation.
# We do this to avoid doing two forward passes.
if args.with_prior_preservation:
input_ids += [example["class_prompt_ids"] for example in examples]
pixel_values += [example["class_images"] for example in examples]
pixel_values = torch.stack(pixel_values)
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
input_ids = tokenizer.pad(
{"input_ids": input_ids},
padding="max_length",
max_length=tokenizer.model_max_length,
return_tensors="pt",
).input_ids
batch = {
"input_ids": input_ids,
"pixel_values": pixel_values,
}
return batch
train_dataloader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn, num_workers=1
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
)
if args.train_text_encoder:
unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, text_encoder, optimizer, train_dataloader, lr_scheduler
)
else:
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, optimizer, train_dataloader, lr_scheduler
)
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move text_encode and vae to gpu.
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
vae.to(accelerator.device, dtype=weight_dtype)
if not args.train_text_encoder:
text_encoder.to(accelerator.device, dtype=weight_dtype)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("dreambooth", config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
global_step = 0
for epoch in range(args.num_train_epochs):
unet.train()
if args.train_text_encoder:
text_encoder.train()
for step, batch in enumerate(train_dataloader):
with accelerator.accumulate(unet):
# Convert images to latent space
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
latents = latents * 0.18215
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Get the text embedding for conditioning
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
# Predict the noise residual
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
if args.with_prior_preservation:
# Chunk the noise and model_pred into two parts and compute the loss on each part separately.
model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
target, target_prior = torch.chunk(target, 2, dim=0)
# Compute instance loss
loss = F.mse_loss(model_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean()
# Compute prior loss
prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
# Add the prior loss to the instance loss.
loss = loss + args.prior_loss_weight * prior_loss
else:
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
accelerator.backward(loss)
if accelerator.sync_gradients:
params_to_clip = (
itertools.chain(unet.parameters(), text_encoder.parameters())
if args.train_text_encoder
else unet.parameters()
)
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if global_step % args.save_steps == 0:
if accelerator.is_main_process:
pipeline = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=accelerator.unwrap_model(unet),
text_encoder=accelerator.unwrap_model(text_encoder),
revision=args.revision,
)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
pipeline.save_pretrained(save_path)
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break
accelerator.wait_for_everyone()
# Create the pipeline using using the trained modules and save it.
if accelerator.is_main_process:
pipeline = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=accelerator.unwrap_model(unet),
text_encoder=accelerator.unwrap_model(text_encoder),
revision=args.revision,
)
pipeline.save_pretrained(args.output_dir)
if args.push_to_hub:
repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
accelerator.end_training()
if __name__ == "__main__":
args = parse_args()
main(args)
| diffusers-ft-main | examples/dreambooth/train_dreambooth.py |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
from typing import Callable, List, Optional, Union
import torch
from diffusers import LMSDiscreteScheduler
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import is_accelerate_available, logging
from k_diffusion.external import CompVisDenoiser
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class ModelWrapper:
def __init__(self, model, alphas_cumprod):
self.model = model
self.alphas_cumprod = alphas_cumprod
def apply_model(self, *args, **kwargs):
return self.model(*args, **kwargs).sample
class StableDiffusionPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
def __init__(
self,
vae,
text_encoder,
tokenizer,
unet,
scheduler,
safety_checker,
feature_extractor,
):
super().__init__()
if safety_checker is None:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
# get correct sigmas from LMS
scheduler = LMSDiscreteScheduler.from_config(scheduler.config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
model = ModelWrapper(unet, scheduler.alphas_cumprod)
self.k_diffusion_model = CompVisDenoiser(model)
def set_sampler(self, scheduler_type: str):
library = importlib.import_module("k_diffusion")
sampling = getattr(library, "sampling")
self.sampler = getattr(sampling, scheduler_type)
def enable_xformers_memory_efficient_attention(self):
r"""
Enable memory efficient attention as implemented in xformers.
When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference
time. Speed up at training time is not guaranteed.
Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention
is used.
"""
self.unet.set_use_memory_efficient_attention_xformers(True)
def disable_xformers_memory_efficient_attention(self):
r"""
Disable memory efficient attention as implemented in xformers.
"""
self.unet.set_use_memory_efficient_attention_xformers(False)
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.
Args:
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(slice_size)
def disable_attention_slicing(self):
r"""
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
back to computing attention in one step.
"""
# set slice_size = `None` to disable `attention slicing`
self.enable_attention_slicing(None)
def enable_sequential_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
`torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
device = torch.device(f"cuda:{gpu_id}")
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
if cpu_offloaded_model is not None:
cpu_offload(cpu_offloaded_model, device)
@property
def _execution_device(self):
r"""
Returns the device on which the pipeline's models will be executed. After calling
`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
hooks.
"""
if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
return self.device
for module in self.unet.modules():
if (
hasattr(module, "_hf_hook")
and hasattr(module._hf_hook, "execution_device")
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device)
return self.device
def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `list(int)`):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
"""
batch_size = len(prompt) if isinstance(prompt, list) else 1
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids
if not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
text_embeddings = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
text_embeddings = text_embeddings[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
uncond_embeddings = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
uncond_embeddings = uncond_embeddings[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_embeddings.shape[1]
uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
return text_embeddings
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
else:
has_nsfw_concept = None
return image, has_nsfw_concept
def decode_latents(self, latents):
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
def check_inputs(self, prompt, height, width, callback_steps):
if not isinstance(prompt, str) and not isinstance(prompt, list):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // 8, width // 8)
if latents is None:
if device.type == "mps":
# randn does not work reproducibly on mps
latents = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device)
else:
latents = torch.randn(shape, generator=generator, device=device, dtype=dtype)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
return latents
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
height: int = 512,
width: int = 512,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
**kwargs,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
# 1. Check inputs. Raise error if not correct
self.check_inputs(prompt, height, width, callback_steps)
# 2. Define call parameters
batch_size = 1 if isinstance(prompt, str) else len(prompt)
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = True
if guidance_scale <= 1.0:
raise ValueError("has to use guidance_scale")
# 3. Encode input prompt
text_embeddings = self._encode_prompt(
prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=text_embeddings.device)
sigmas = self.scheduler.sigmas
# 5. Prepare latent variables
num_channels_latents = self.unet.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
text_embeddings.dtype,
device,
generator,
latents,
)
latents = latents * sigmas[0]
self.k_diffusion_model.sigmas = self.k_diffusion_model.sigmas.to(latents.device)
self.k_diffusion_model.log_sigmas = self.k_diffusion_model.log_sigmas.to(latents.device)
def model_fn(x, t):
latent_model_input = torch.cat([x] * 2)
noise_pred = self.k_diffusion_model(latent_model_input, t, encoder_hidden_states=text_embeddings)
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
return noise_pred
latents = self.sampler(model_fn, latents, sigmas)
# 8. Post-processing
image = self.decode_latents(latents)
# 9. Run safety checker
image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)
# 10. Convert to PIL
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| diffusers-ft-main | examples/community/sd_text2img_k_diffusion.py |
import inspect
from typing import Callable, List, Optional, Union
import torch
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, logging
from transformers import (
CLIPFeatureExtractor,
CLIPTextModel,
CLIPTokenizer,
MBart50TokenizerFast,
MBartForConditionalGeneration,
pipeline,
)
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def detect_language(pipe, prompt, batch_size):
"""helper function to detect language(s) of prompt"""
if batch_size == 1:
preds = pipe(prompt, top_k=1, truncation=True, max_length=128)
return preds[0]["label"]
else:
detected_languages = []
for p in prompt:
preds = pipe(p, top_k=1, truncation=True, max_length=128)
detected_languages.append(preds[0]["label"])
return detected_languages
def translate_prompt(prompt, translation_tokenizer, translation_model, device):
"""helper function to translate prompt to English"""
encoded_prompt = translation_tokenizer(prompt, return_tensors="pt").to(device)
generated_tokens = translation_model.generate(**encoded_prompt, max_new_tokens=1000)
en_trans = translation_tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
return en_trans[0]
class MultilingualStableDiffusion(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using Stable Diffusion in different languages.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
detection_pipeline ([`pipeline`]):
Transformers pipeline to detect prompt's language.
translation_model ([`MBartForConditionalGeneration`]):
Model to translate prompt to English, if necessary. Please refer to the
[model card](https://huggingface.co/docs/transformers/model_doc/mbart) for details.
translation_tokenizer ([`MBart50TokenizerFast`]):
Tokenizer of the translation model.
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
def __init__(
self,
detection_pipeline: pipeline,
translation_model: MBartForConditionalGeneration,
translation_tokenizer: MBart50TokenizerFast,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
self.register_modules(
detection_pipeline=detection_pipeline,
translation_model=translation_model,
translation_tokenizer=translation_tokenizer,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.
Args:
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(slice_size)
def disable_attention_slicing(self):
r"""
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
back to computing attention in one step.
"""
# set slice_size = `None` to disable `attention slicing`
self.enable_attention_slicing(None)
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
height: int = 512,
width: int = 512,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
**kwargs,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation. Can be in different languages.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
# detect language and translate if necessary
prompt_language = detect_language(self.detection_pipeline, prompt, batch_size)
if batch_size == 1 and prompt_language != "en":
prompt = translate_prompt(prompt, self.translation_tokenizer, self.translation_model, self.device)
if isinstance(prompt, list):
for index in range(batch_size):
if prompt_language[index] != "en":
p = translate_prompt(
prompt[index], self.translation_tokenizer, self.translation_model, self.device
)
prompt[index] = p
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
# detect language and translate it if necessary
negative_prompt_language = detect_language(self.detection_pipeline, negative_prompt, batch_size)
if negative_prompt_language != "en":
negative_prompt = translate_prompt(
negative_prompt, self.translation_tokenizer, self.translation_model, self.device
)
if isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
# detect language and translate it if necessary
if isinstance(negative_prompt, list):
negative_prompt_languages = detect_language(self.detection_pipeline, negative_prompt, batch_size)
for index in range(batch_size):
if negative_prompt_languages[index] != "en":
p = translate_prompt(
negative_prompt[index], self.translation_tokenizer, self.translation_model, self.device
)
negative_prompt[index] = p
uncond_tokens = negative_prompt
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_embeddings.shape[1]
uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
latents_dtype = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
self.device
)
else:
latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
latents = latents.to(self.device)
# set timesteps
self.scheduler.set_timesteps(num_inference_steps)
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
timesteps_tensor = self.scheduler.timesteps.to(self.device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
self.device
)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
)
else:
has_nsfw_concept = None
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| diffusers-ft-main | examples/community/multilingual_stable_diffusion.py |
import inspect
import re
from typing import Callable, List, Optional, Union
import numpy as np
import torch
import PIL
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging
# TODO: remove and import from diffusers.utils when the new version of diffusers is released
from packaging import version
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
PIL_INTERPOLATION = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling.BILINEAR,
"bicubic": PIL.Image.Resampling.BICUBIC,
"lanczos": PIL.Image.Resampling.LANCZOS,
"nearest": PIL.Image.Resampling.NEAREST,
}
else:
PIL_INTERPOLATION = {
"linear": PIL.Image.LINEAR,
"bilinear": PIL.Image.BILINEAR,
"bicubic": PIL.Image.BICUBIC,
"lanczos": PIL.Image.LANCZOS,
"nearest": PIL.Image.NEAREST,
}
# ------------------------------------------------------------------------------
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
re_attention = re.compile(
r"""
\\\(|
\\\)|
\\\[|
\\]|
\\\\|
\\|
\(|
\[|
:([+-]?[.\d]+)\)|
\)|
]|
[^\\()\[\]:]+|
:
""",
re.X,
)
def parse_prompt_attention(text):
"""
Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
Accepted tokens are:
(abc) - increases attention to abc by a multiplier of 1.1
(abc:3.12) - increases attention to abc by a multiplier of 3.12
[abc] - decreases attention to abc by a multiplier of 1.1
\( - literal character '('
\[ - literal character '['
\) - literal character ')'
\] - literal character ']'
\\ - literal character '\'
anything else - just text
>>> parse_prompt_attention('normal text')
[['normal text', 1.0]]
>>> parse_prompt_attention('an (important) word')
[['an ', 1.0], ['important', 1.1], [' word', 1.0]]
>>> parse_prompt_attention('(unbalanced')
[['unbalanced', 1.1]]
>>> parse_prompt_attention('\(literal\]')
[['(literal]', 1.0]]
>>> parse_prompt_attention('(unnecessary)(parens)')
[['unnecessaryparens', 1.1]]
>>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
[['a ', 1.0],
['house', 1.5730000000000004],
[' ', 1.1],
['on', 1.0],
[' a ', 1.1],
['hill', 0.55],
[', sun, ', 1.1],
['sky', 1.4641000000000006],
['.', 1.1]]
"""
res = []
round_brackets = []
square_brackets = []
round_bracket_multiplier = 1.1
square_bracket_multiplier = 1 / 1.1
def multiply_range(start_position, multiplier):
for p in range(start_position, len(res)):
res[p][1] *= multiplier
for m in re_attention.finditer(text):
text = m.group(0)
weight = m.group(1)
if text.startswith("\\"):
res.append([text[1:], 1.0])
elif text == "(":
round_brackets.append(len(res))
elif text == "[":
square_brackets.append(len(res))
elif weight is not None and len(round_brackets) > 0:
multiply_range(round_brackets.pop(), float(weight))
elif text == ")" and len(round_brackets) > 0:
multiply_range(round_brackets.pop(), round_bracket_multiplier)
elif text == "]" and len(square_brackets) > 0:
multiply_range(square_brackets.pop(), square_bracket_multiplier)
else:
res.append([text, 1.0])
for pos in round_brackets:
multiply_range(pos, round_bracket_multiplier)
for pos in square_brackets:
multiply_range(pos, square_bracket_multiplier)
if len(res) == 0:
res = [["", 1.0]]
# merge runs of identical weights
i = 0
while i + 1 < len(res):
if res[i][1] == res[i + 1][1]:
res[i][0] += res[i + 1][0]
res.pop(i + 1)
else:
i += 1
return res
def get_prompts_with_weights(pipe: DiffusionPipeline, prompt: List[str], max_length: int):
r"""
Tokenize a list of prompts and return its tokens with weights of each token.
No padding, starting or ending token is included.
"""
tokens = []
weights = []
truncated = False
for text in prompt:
texts_and_weights = parse_prompt_attention(text)
text_token = []
text_weight = []
for word, weight in texts_and_weights:
# tokenize and discard the starting and the ending token
token = pipe.tokenizer(word).input_ids[1:-1]
text_token += token
# copy the weight by length of token
text_weight += [weight] * len(token)
# stop if the text is too long (longer than truncation limit)
if len(text_token) > max_length:
truncated = True
break
# truncate
if len(text_token) > max_length:
truncated = True
text_token = text_token[:max_length]
text_weight = text_weight[:max_length]
tokens.append(text_token)
weights.append(text_weight)
if truncated:
logger.warning("Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples")
return tokens, weights
def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, no_boseos_middle=True, chunk_length=77):
r"""
Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length.
"""
max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)
weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length
for i in range(len(tokens)):
tokens[i] = [bos] + tokens[i] + [eos] * (max_length - 1 - len(tokens[i]))
if no_boseos_middle:
weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i]))
else:
w = []
if len(weights[i]) == 0:
w = [1.0] * weights_length
else:
for j in range(max_embeddings_multiples):
w.append(1.0) # weight for starting token in this chunk
w += weights[i][j * (chunk_length - 2) : min(len(weights[i]), (j + 1) * (chunk_length - 2))]
w.append(1.0) # weight for ending token in this chunk
w += [1.0] * (weights_length - len(w))
weights[i] = w[:]
return tokens, weights
def get_unweighted_text_embeddings(
pipe: DiffusionPipeline,
text_input: torch.Tensor,
chunk_length: int,
no_boseos_middle: Optional[bool] = True,
):
"""
When the length of tokens is a multiple of the capacity of the text encoder,
it should be split into chunks and sent to the text encoder individually.
"""
max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)
if max_embeddings_multiples > 1:
text_embeddings = []
for i in range(max_embeddings_multiples):
# extract the i-th chunk
text_input_chunk = text_input[:, i * (chunk_length - 2) : (i + 1) * (chunk_length - 2) + 2].clone()
# cover the head and the tail by the starting and the ending tokens
text_input_chunk[:, 0] = text_input[0, 0]
text_input_chunk[:, -1] = text_input[0, -1]
text_embedding = pipe.text_encoder(text_input_chunk)[0]
if no_boseos_middle:
if i == 0:
# discard the ending token
text_embedding = text_embedding[:, :-1]
elif i == max_embeddings_multiples - 1:
# discard the starting token
text_embedding = text_embedding[:, 1:]
else:
# discard both starting and ending tokens
text_embedding = text_embedding[:, 1:-1]
text_embeddings.append(text_embedding)
text_embeddings = torch.concat(text_embeddings, axis=1)
else:
text_embeddings = pipe.text_encoder(text_input)[0]
return text_embeddings
def get_weighted_text_embeddings(
pipe: DiffusionPipeline,
prompt: Union[str, List[str]],
uncond_prompt: Optional[Union[str, List[str]]] = None,
max_embeddings_multiples: Optional[int] = 1,
no_boseos_middle: Optional[bool] = False,
skip_parsing: Optional[bool] = False,
skip_weighting: Optional[bool] = False,
**kwargs,
):
r"""
Prompts can be assigned with local weights using brackets. For example,
prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful',
and the embedding tokens corresponding to the words get multiplied by a constant, 1.1.
Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.
Args:
pipe (`DiffusionPipeline`):
Pipe to provide access to the tokenizer and the text encoder.
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
uncond_prompt (`str` or `List[str]`):
The unconditional prompt or prompts for guide the image generation. If unconditional prompt
is provided, the embeddings of prompt and uncond_prompt are concatenated.
max_embeddings_multiples (`int`, *optional*, defaults to `1`):
The max multiple length of prompt embeddings compared to the max output length of text encoder.
no_boseos_middle (`bool`, *optional*, defaults to `False`):
If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and
ending token in each of the chunk in the middle.
skip_parsing (`bool`, *optional*, defaults to `False`):
Skip the parsing of brackets.
skip_weighting (`bool`, *optional*, defaults to `False`):
Skip the weighting. When the parsing is skipped, it is forced True.
"""
max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
if isinstance(prompt, str):
prompt = [prompt]
if not skip_parsing:
prompt_tokens, prompt_weights = get_prompts_with_weights(pipe, prompt, max_length - 2)
if uncond_prompt is not None:
if isinstance(uncond_prompt, str):
uncond_prompt = [uncond_prompt]
uncond_tokens, uncond_weights = get_prompts_with_weights(pipe, uncond_prompt, max_length - 2)
else:
prompt_tokens = [
token[1:-1] for token in pipe.tokenizer(prompt, max_length=max_length, truncation=True).input_ids
]
prompt_weights = [[1.0] * len(token) for token in prompt_tokens]
if uncond_prompt is not None:
if isinstance(uncond_prompt, str):
uncond_prompt = [uncond_prompt]
uncond_tokens = [
token[1:-1]
for token in pipe.tokenizer(uncond_prompt, max_length=max_length, truncation=True).input_ids
]
uncond_weights = [[1.0] * len(token) for token in uncond_tokens]
# round up the longest length of tokens to a multiple of (model_max_length - 2)
max_length = max([len(token) for token in prompt_tokens])
if uncond_prompt is not None:
max_length = max(max_length, max([len(token) for token in uncond_tokens]))
max_embeddings_multiples = min(
max_embeddings_multiples,
(max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1,
)
max_embeddings_multiples = max(1, max_embeddings_multiples)
max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
# pad the length of tokens and weights
bos = pipe.tokenizer.bos_token_id
eos = pipe.tokenizer.eos_token_id
prompt_tokens, prompt_weights = pad_tokens_and_weights(
prompt_tokens,
prompt_weights,
max_length,
bos,
eos,
no_boseos_middle=no_boseos_middle,
chunk_length=pipe.tokenizer.model_max_length,
)
prompt_tokens = torch.tensor(prompt_tokens, dtype=torch.long, device=pipe.device)
if uncond_prompt is not None:
uncond_tokens, uncond_weights = pad_tokens_and_weights(
uncond_tokens,
uncond_weights,
max_length,
bos,
eos,
no_boseos_middle=no_boseos_middle,
chunk_length=pipe.tokenizer.model_max_length,
)
uncond_tokens = torch.tensor(uncond_tokens, dtype=torch.long, device=pipe.device)
# get the embeddings
text_embeddings = get_unweighted_text_embeddings(
pipe,
prompt_tokens,
pipe.tokenizer.model_max_length,
no_boseos_middle=no_boseos_middle,
)
prompt_weights = torch.tensor(prompt_weights, dtype=text_embeddings.dtype, device=pipe.device)
if uncond_prompt is not None:
uncond_embeddings = get_unweighted_text_embeddings(
pipe,
uncond_tokens,
pipe.tokenizer.model_max_length,
no_boseos_middle=no_boseos_middle,
)
uncond_weights = torch.tensor(uncond_weights, dtype=uncond_embeddings.dtype, device=pipe.device)
# assign weights to the prompts and normalize in the sense of mean
# TODO: should we normalize by chunk or in a whole (current implementation)?
if (not skip_parsing) and (not skip_weighting):
previous_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)
text_embeddings *= prompt_weights.unsqueeze(-1)
current_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)
text_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)
if uncond_prompt is not None:
previous_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)
uncond_embeddings *= uncond_weights.unsqueeze(-1)
current_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)
uncond_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)
if uncond_prompt is not None:
return text_embeddings, uncond_embeddings
return text_embeddings, None
def preprocess_image(image):
w, h = image.size
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.0 * image - 1.0
def preprocess_mask(mask):
mask = mask.convert("L")
w, h = mask.size
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
mask = mask.resize((w // 8, h // 8), resample=PIL_INTERPOLATION["nearest"])
mask = np.array(mask).astype(np.float32) / 255.0
mask = np.tile(mask, (4, 1, 1))
mask = mask[None].transpose(0, 1, 2, 3) # what does this step do?
mask = 1 - mask # repaint white, keep black
mask = torch.from_numpy(mask)
return mask
class StableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using Stable Diffusion without tokens length limit, and support parsing
weighting in prompt.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
)
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["clip_sample"] = False
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
def enable_xformers_memory_efficient_attention(self):
r"""
Enable memory efficient attention as implemented in xformers.
When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference
time. Speed up at training time is not guaranteed.
Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention
is used.
"""
self.unet.set_use_memory_efficient_attention_xformers(True)
def disable_xformers_memory_efficient_attention(self):
r"""
Disable memory efficient attention as implemented in xformers.
"""
self.unet.set_use_memory_efficient_attention_xformers(False)
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.
Args:
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(slice_size)
def disable_attention_slicing(self):
r"""
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
back to computing attention in one step.
"""
# set slice_size = `None` to disable `attention slicing`
self.enable_attention_slicing(None)
def enable_sequential_cpu_offload(self):
r"""
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
`torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
device = self.device
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
if cpu_offloaded_model is not None:
cpu_offload(cpu_offloaded_model, device)
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
negative_prompt: Optional[Union[str, List[str]]] = None,
init_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
height: int = 512,
width: int = 512,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
strength: float = 0.8,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.FloatTensor] = None,
max_embeddings_multiples: Optional[int] = 3,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
is_cancelled_callback: Optional[Callable[[], bool]] = None,
callback_steps: Optional[int] = 1,
**kwargs,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
init_image (`torch.FloatTensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, to mask `init_image`. White pixels in the mask will be
replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to transform the reference `init_image`. Must be between 0 and 1.
`init_image` will be used as a starting point, adding more noise to it the larger the `strength`. The
number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
noise will be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `init_image`.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
The max multiple length of prompt embeddings compared to the max output length of text encoder.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
is_cancelled_callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. If the function returns
`True`, the inference will be cancelled.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
`None` if cancelled by `is_cancelled_callback`,
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
if isinstance(prompt, str):
batch_size = 1
prompt = [prompt]
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
# get prompt text embeddings
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if negative_prompt is None:
negative_prompt = [""] * batch_size
elif isinstance(negative_prompt, str):
negative_prompt = [negative_prompt] * batch_size
if batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
text_embeddings, uncond_embeddings = get_weighted_text_embeddings(
pipe=self,
prompt=prompt,
uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
max_embeddings_multiples=max_embeddings_multiples,
**kwargs,
)
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
if do_classifier_free_guidance:
bs_embed, seq_len, _ = uncond_embeddings.shape
uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
uncond_embeddings = uncond_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
# set timesteps
self.scheduler.set_timesteps(num_inference_steps)
latents_dtype = text_embeddings.dtype
init_latents_orig = None
mask = None
noise = None
if init_image is None:
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
latents_shape = (
batch_size * num_images_per_prompt,
self.unet.in_channels,
height // 8,
width // 8,
)
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
latents = torch.randn(
latents_shape,
generator=generator,
device="cpu",
dtype=latents_dtype,
).to(self.device)
else:
latents = torch.randn(
latents_shape,
generator=generator,
device=self.device,
dtype=latents_dtype,
)
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
latents = latents.to(self.device)
timesteps = self.scheduler.timesteps.to(self.device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
else:
if isinstance(init_image, PIL.Image.Image):
init_image = preprocess_image(init_image)
# encode the init image into latents and scale the latents
init_image = init_image.to(device=self.device, dtype=latents_dtype)
init_latent_dist = self.vae.encode(init_image).latent_dist
init_latents = init_latent_dist.sample(generator=generator)
init_latents = 0.18215 * init_latents
init_latents = torch.cat([init_latents] * batch_size * num_images_per_prompt, dim=0)
init_latents_orig = init_latents
# preprocess mask
if mask_image is not None:
if isinstance(mask_image, PIL.Image.Image):
mask_image = preprocess_mask(mask_image)
mask_image = mask_image.to(device=self.device, dtype=latents_dtype)
mask = torch.cat([mask_image] * batch_size * num_images_per_prompt)
# check sizes
if not mask.shape == init_latents.shape:
raise ValueError("The mask and init_image should be the same size!")
# get the original timestep using init_timestep
offset = self.scheduler.config.get("steps_offset", 0)
init_timestep = int(num_inference_steps * strength) + offset
init_timestep = min(init_timestep, num_inference_steps)
timesteps = self.scheduler.timesteps[-init_timestep]
timesteps = torch.tensor([timesteps] * batch_size * num_images_per_prompt, device=self.device)
# add noise to latents using the timesteps
if self.device.type == "mps":
# randn does not exist on mps
noise = torch.randn(
init_latents.shape,
generator=generator,
device="cpu",
dtype=latents_dtype,
).to(self.device)
else:
noise = torch.randn(
init_latents.shape,
generator=generator,
device=self.device,
dtype=latents_dtype,
)
latents = self.scheduler.add_noise(init_latents, noise, timesteps)
t_start = max(num_inference_steps - init_timestep + offset, 0)
timesteps = self.scheduler.timesteps[t_start:].to(self.device)
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
for i, t in enumerate(self.progress_bar(timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
if mask is not None:
# masking
init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t]))
latents = (init_latents_proper * mask) + (latents * (1 - mask))
# call the callback, if provided
if i % callback_steps == 0:
if callback is not None:
callback(i, t, latents)
if is_cancelled_callback is not None and is_cancelled_callback():
return None
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
self.device
)
image, has_nsfw_concept = self.safety_checker(
images=image,
clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype),
)
else:
has_nsfw_concept = None
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
def text2img(
self,
prompt: Union[str, List[str]],
negative_prompt: Optional[Union[str, List[str]]] = None,
height: int = 512,
width: int = 512,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.FloatTensor] = None,
max_embeddings_multiples: Optional[int] = 3,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
**kwargs,
):
r"""
Function for text-to-image generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
The max multiple length of prompt embeddings compared to the max output length of text encoder.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
return self.__call__(
prompt=prompt,
negative_prompt=negative_prompt,
height=height,
width=width,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
num_images_per_prompt=num_images_per_prompt,
eta=eta,
generator=generator,
latents=latents,
max_embeddings_multiples=max_embeddings_multiples,
output_type=output_type,
return_dict=return_dict,
callback=callback,
callback_steps=callback_steps,
**kwargs,
)
def img2img(
self,
init_image: Union[torch.FloatTensor, PIL.Image.Image],
prompt: Union[str, List[str]],
negative_prompt: Optional[Union[str, List[str]]] = None,
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
num_images_per_prompt: Optional[int] = 1,
eta: Optional[float] = 0.0,
generator: Optional[torch.Generator] = None,
max_embeddings_multiples: Optional[int] = 3,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
**kwargs,
):
r"""
Function for image-to-image generation.
Args:
init_image (`torch.FloatTensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to transform the reference `init_image`. Must be between 0 and 1.
`init_image` will be used as a starting point, adding more noise to it the larger the `strength`. The
number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
noise will be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `init_image`.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter will be modulated by `strength`.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
The max multiple length of prompt embeddings compared to the max output length of text encoder.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
return self.__call__(
prompt=prompt,
negative_prompt=negative_prompt,
init_image=init_image,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
strength=strength,
num_images_per_prompt=num_images_per_prompt,
eta=eta,
generator=generator,
max_embeddings_multiples=max_embeddings_multiples,
output_type=output_type,
return_dict=return_dict,
callback=callback,
callback_steps=callback_steps,
**kwargs,
)
def inpaint(
self,
init_image: Union[torch.FloatTensor, PIL.Image.Image],
mask_image: Union[torch.FloatTensor, PIL.Image.Image],
prompt: Union[str, List[str]],
negative_prompt: Optional[Union[str, List[str]]] = None,
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
num_images_per_prompt: Optional[int] = 1,
eta: Optional[float] = 0.0,
generator: Optional[torch.Generator] = None,
max_embeddings_multiples: Optional[int] = 3,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
**kwargs,
):
r"""
Function for inpaint.
Args:
init_image (`torch.FloatTensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process. This is the image whose masked region will be inpainted.
mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, to mask `init_image`. White pixels in the mask will be
replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`
is 1, the denoising process will be run on the masked area for the full number of iterations specified
in `num_inference_steps`. `init_image` will be used as a reference for the masked area, adding more
noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur.
num_inference_steps (`int`, *optional*, defaults to 50):
The reference number of denoising steps. More denoising steps usually lead to a higher quality image at
the expense of slower inference. This parameter will be modulated by `strength`, as explained above.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
The max multiple length of prompt embeddings compared to the max output length of text encoder.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
return self.__call__(
prompt=prompt,
negative_prompt=negative_prompt,
init_image=init_image,
mask_image=mask_image,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
strength=strength,
num_images_per_prompt=num_images_per_prompt,
eta=eta,
generator=generator,
max_embeddings_multiples=max_embeddings_multiples,
output_type=output_type,
return_dict=return_dict,
callback=callback,
callback_steps=callback_steps,
**kwargs,
)
| diffusers-ft-main | examples/community/lpw_stable_diffusion.py |
import inspect
import os
import random
import re
from dataclasses import dataclass
from typing import Callable, Dict, List, Optional, Union
import torch
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, logging
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
global_re_wildcard = re.compile(r"__([^_]*)__")
def get_filename(path: str):
# this doesn't work on Windows
return os.path.basename(path).split(".txt")[0]
def read_wildcard_values(path: str):
with open(path, encoding="utf8") as f:
return f.read().splitlines()
def grab_wildcard_values(wildcard_option_dict: Dict[str, List[str]] = {}, wildcard_files: List[str] = []):
for wildcard_file in wildcard_files:
filename = get_filename(wildcard_file)
read_values = read_wildcard_values(wildcard_file)
if filename not in wildcard_option_dict:
wildcard_option_dict[filename] = []
wildcard_option_dict[filename].extend(read_values)
return wildcard_option_dict
def replace_prompt_with_wildcards(
prompt: str, wildcard_option_dict: Dict[str, List[str]] = {}, wildcard_files: List[str] = []
):
new_prompt = prompt
# get wildcard options
wildcard_option_dict = grab_wildcard_values(wildcard_option_dict, wildcard_files)
for m in global_re_wildcard.finditer(new_prompt):
wildcard_value = m.group()
replace_value = random.choice(wildcard_option_dict[wildcard_value.strip("__")])
new_prompt = new_prompt.replace(wildcard_value, replace_value, 1)
return new_prompt
@dataclass
class WildcardStableDiffusionOutput(StableDiffusionPipelineOutput):
prompts: List[str]
class WildcardStableDiffusionPipeline(DiffusionPipeline):
r"""
Example Usage:
pipe = WildcardStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
revision="fp16",
torch_dtype=torch.float16,
)
prompt = "__animal__ sitting on a __object__ wearing a __clothing__"
out = pipe(
prompt,
wildcard_option_dict={
"clothing":["hat", "shirt", "scarf", "beret"]
},
wildcard_files=["object.txt", "animal.txt"],
num_prompt_samples=1
)
Pipeline for text-to-image generation with wild cards using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
height: int = 512,
width: int = 512,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
wildcard_option_dict: Dict[str, List[str]] = {},
wildcard_files: List[str] = [],
num_prompt_samples: Optional[int] = 1,
**kwargs,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
wildcard_option_dict (Dict[str, List[str]]):
dict with key as `wildcard` and values as a list of possible replacements. For example if a prompt, "A __animal__ sitting on a chair". A wildcard_option_dict can provide possible values for "animal" like this: {"animal":["dog", "cat", "fox"]}
wildcard_files: (List[str])
List of filenames of txt files for wildcard replacements. For example if a prompt, "A __animal__ sitting on a chair". A file can be provided ["animal.txt"]
num_prompt_samples: int
Number of times to sample wildcards for each prompt provided
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
if isinstance(prompt, str):
prompt = [
replace_prompt_with_wildcards(prompt, wildcard_option_dict, wildcard_files)
for i in range(num_prompt_samples)
]
batch_size = len(prompt)
elif isinstance(prompt, list):
prompt_list = []
for p in prompt:
for i in range(num_prompt_samples):
prompt_list.append(replace_prompt_with_wildcards(p, wildcard_option_dict, wildcard_files))
prompt = prompt_list
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_embeddings.shape[1]
uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
latents_dtype = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
self.device
)
else:
latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
latents = latents.to(self.device)
# set timesteps
self.scheduler.set_timesteps(num_inference_steps)
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
timesteps_tensor = self.scheduler.timesteps.to(self.device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
self.device
)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
)
else:
has_nsfw_concept = None
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return WildcardStableDiffusionOutput(images=image, nsfw_content_detected=has_nsfw_concept, prompts=prompt)
| diffusers-ft-main | examples/community/wildcard_stable_diffusion.py |
import inspect
from typing import Callable, List, Optional, Union
import torch
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
UNet2DConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging
from transformers import (
CLIPFeatureExtractor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class SpeechToImagePipeline(DiffusionPipeline):
def __init__(
self,
speech_model: WhisperForConditionalGeneration,
speech_processor: WhisperProcessor,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
):
super().__init__()
if safety_checker is None:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
self.register_modules(
speech_model=speech_model,
speech_processor=speech_processor,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
feature_extractor=feature_extractor,
)
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
if slice_size == "auto":
slice_size = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(slice_size)
def disable_attention_slicing(self):
self.enable_attention_slicing(None)
@torch.no_grad()
def __call__(
self,
audio,
sampling_rate=16_000,
height: int = 512,
width: int = 512,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
**kwargs,
):
inputs = self.speech_processor.feature_extractor(
audio, return_tensors="pt", sampling_rate=sampling_rate
).input_features.to(self.device)
predicted_ids = self.speech_model.generate(inputs, max_length=480_000)
prompt = self.speech_processor.tokenizer.batch_decode(predicted_ids, skip_special_tokens=True, normalize=True)[
0
]
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_embeddings.shape[1]
uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
latents_dtype = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
self.device
)
else:
latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
latents = latents.to(self.device)
# set timesteps
self.scheduler.set_timesteps(num_inference_steps)
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
timesteps_tensor = self.scheduler.timesteps.to(self.device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return image
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)
| diffusers-ft-main | examples/community/speech_to_image_diffusion.py |
#!/usr/bin/env python3
import torch
from diffusers import DiffusionPipeline
class UnetSchedulerOneForwardPipeline(DiffusionPipeline):
def __init__(self, unet, scheduler):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler)
def __call__(self):
image = torch.randn(
(1, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
)
timestep = 1
model_output = self.unet(image, timestep).sample
scheduler_output = self.scheduler.step(model_output, timestep, image).prev_sample
return scheduler_output
| diffusers-ft-main | examples/community/one_step_unet.py |
from typing import Optional, Tuple, Union
import torch
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
from diffusers.pipeline_utils import ImagePipelineOutput
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput
from einops import rearrange, reduce
BITS = 8
# convert to bit representations and back taken from https://github.com/lucidrains/bit-diffusion/blob/main/bit_diffusion/bit_diffusion.py
def decimal_to_bits(x, bits=BITS):
"""expects image tensor ranging from 0 to 1, outputs bit tensor ranging from -1 to 1"""
device = x.device
x = (x * 255).int().clamp(0, 255)
mask = 2 ** torch.arange(bits - 1, -1, -1, device=device)
mask = rearrange(mask, "d -> d 1 1")
x = rearrange(x, "b c h w -> b c 1 h w")
bits = ((x & mask) != 0).float()
bits = rearrange(bits, "b c d h w -> b (c d) h w")
bits = bits * 2 - 1
return bits
def bits_to_decimal(x, bits=BITS):
"""expects bits from -1 to 1, outputs image tensor from 0 to 1"""
device = x.device
x = (x > 0).int()
mask = 2 ** torch.arange(bits - 1, -1, -1, device=device, dtype=torch.int32)
mask = rearrange(mask, "d -> d 1 1")
x = rearrange(x, "b (c d) h w -> b c d h w", d=8)
dec = reduce(x * mask, "b c d h w -> b c h w", "sum")
return (dec / 255).clamp(0.0, 1.0)
# modified scheduler step functions for clamping the predicted x_0 between -bit_scale and +bit_scale
def ddim_bit_scheduler_step(
self,
model_output: torch.FloatTensor,
timestep: int,
sample: torch.FloatTensor,
eta: float = 0.0,
use_clipped_model_output: bool = True,
generator=None,
return_dict: bool = True,
) -> Union[DDIMSchedulerOutput, Tuple]:
"""
Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.FloatTensor`): direct output from learned diffusion model.
timestep (`int`): current discrete timestep in the diffusion chain.
sample (`torch.FloatTensor`):
current instance of sample being created by diffusion process.
eta (`float`): weight of noise for added noise in diffusion step.
use_clipped_model_output (`bool`): TODO
generator: random number generator.
return_dict (`bool`): option for returning tuple rather than DDIMSchedulerOutput class
Returns:
[`~schedulers.scheduling_utils.DDIMSchedulerOutput`] or `tuple`:
[`~schedulers.scheduling_utils.DDIMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
returning a tuple, the first element is the sample tensor.
"""
if self.num_inference_steps is None:
raise ValueError(
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
)
# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
# Ideally, read DDIM paper in-detail understanding
# Notation (<variable name> -> <name in paper>
# - pred_noise_t -> e_theta(x_t, t)
# - pred_original_sample -> f_theta(x_t, t) or x_0
# - std_dev_t -> sigma_t
# - eta -> η
# - pred_sample_direction -> "direction pointing to x_t"
# - pred_prev_sample -> "x_t-1"
# 1. get previous step value (=t-1)
prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
alpha_prod_t = self.alphas_cumprod[timestep]
alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
# 4. Clip "predicted x_0"
scale = self.bit_scale
if self.config.clip_sample:
pred_original_sample = torch.clamp(pred_original_sample, -scale, scale)
# 5. compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
variance = self._get_variance(timestep, prev_timestep)
std_dev_t = eta * variance ** (0.5)
if use_clipped_model_output:
# the model_output is always re-derived from the clipped x_0 in Glide
model_output = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * model_output
# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
if eta > 0:
# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
device = model_output.device if torch.is_tensor(model_output) else "cpu"
noise = torch.randn(model_output.shape, dtype=model_output.dtype, generator=generator).to(device)
variance = self._get_variance(timestep, prev_timestep) ** (0.5) * eta * noise
prev_sample = prev_sample + variance
if not return_dict:
return (prev_sample,)
return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
def ddpm_bit_scheduler_step(
self,
model_output: torch.FloatTensor,
timestep: int,
sample: torch.FloatTensor,
prediction_type="epsilon",
generator=None,
return_dict: bool = True,
) -> Union[DDPMSchedulerOutput, Tuple]:
"""
Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.FloatTensor`): direct output from learned diffusion model.
timestep (`int`): current discrete timestep in the diffusion chain.
sample (`torch.FloatTensor`):
current instance of sample being created by diffusion process.
prediction_type (`str`, default `epsilon`):
indicates whether the model predicts the noise (epsilon), or the samples (`sample`).
generator: random number generator.
return_dict (`bool`): option for returning tuple rather than DDPMSchedulerOutput class
Returns:
[`~schedulers.scheduling_utils.DDPMSchedulerOutput`] or `tuple`:
[`~schedulers.scheduling_utils.DDPMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
returning a tuple, the first element is the sample tensor.
"""
t = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1)
else:
predicted_variance = None
# 1. compute alphas, betas
alpha_prod_t = self.alphas_cumprod[t]
alpha_prod_t_prev = self.alphas_cumprod[t - 1] if t > 0 else self.one
beta_prod_t = 1 - alpha_prod_t
beta_prod_t_prev = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if prediction_type == "epsilon":
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
elif prediction_type == "sample":
pred_original_sample = model_output
else:
raise ValueError(f"Unsupported prediction_type {prediction_type}.")
# 3. Clip "predicted x_0"
scale = self.bit_scale
if self.config.clip_sample:
pred_original_sample = torch.clamp(pred_original_sample, -scale, scale)
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * self.betas[t]) / beta_prod_t
current_sample_coeff = self.alphas[t] ** (0.5) * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
variance = 0
if t > 0:
noise = torch.randn(
model_output.size(), dtype=model_output.dtype, layout=model_output.layout, generator=generator
).to(model_output.device)
variance = (self._get_variance(t, predicted_variance=predicted_variance) ** 0.5) * noise
pred_prev_sample = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return DDPMSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample)
class BitDiffusion(DiffusionPipeline):
def __init__(
self,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, DDPMScheduler],
bit_scale: Optional[float] = 1.0,
):
super().__init__()
self.bit_scale = bit_scale
self.scheduler.step = (
ddim_bit_scheduler_step if isinstance(scheduler, DDIMScheduler) else ddpm_bit_scheduler_step
)
self.register_modules(unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
height: Optional[int] = 256,
width: Optional[int] = 256,
num_inference_steps: Optional[int] = 50,
generator: Optional[torch.Generator] = None,
batch_size: Optional[int] = 1,
output_type: Optional[str] = "pil",
return_dict: bool = True,
**kwargs,
) -> Union[Tuple, ImagePipelineOutput]:
latents = torch.randn(
(batch_size, self.unet.in_channels, height, width),
generator=generator,
)
latents = decimal_to_bits(latents) * self.bit_scale
latents = latents.to(self.device)
self.scheduler.set_timesteps(num_inference_steps)
for t in self.progress_bar(self.scheduler.timesteps):
# predict the noise residual
noise_pred = self.unet(latents, t).sample
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents).prev_sample
image = bits_to_decimal(latents)
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| diffusers-ft-main | examples/community/bit_diffusion.py |
import inspect
import re
from typing import Callable, List, Optional, Union
import numpy as np
import torch
import PIL
from diffusers.onnx_utils import OnnxRuntimeModel
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
# TODO: remove and import from diffusers.utils when the new version of diffusers is released
from packaging import version
from transformers import CLIPFeatureExtractor, CLIPTokenizer
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
PIL_INTERPOLATION = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling.BILINEAR,
"bicubic": PIL.Image.Resampling.BICUBIC,
"lanczos": PIL.Image.Resampling.LANCZOS,
"nearest": PIL.Image.Resampling.NEAREST,
}
else:
PIL_INTERPOLATION = {
"linear": PIL.Image.LINEAR,
"bilinear": PIL.Image.BILINEAR,
"bicubic": PIL.Image.BICUBIC,
"lanczos": PIL.Image.LANCZOS,
"nearest": PIL.Image.NEAREST,
}
# ------------------------------------------------------------------------------
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
re_attention = re.compile(
r"""
\\\(|
\\\)|
\\\[|
\\]|
\\\\|
\\|
\(|
\[|
:([+-]?[.\d]+)\)|
\)|
]|
[^\\()\[\]:]+|
:
""",
re.X,
)
def parse_prompt_attention(text):
"""
Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
Accepted tokens are:
(abc) - increases attention to abc by a multiplier of 1.1
(abc:3.12) - increases attention to abc by a multiplier of 3.12
[abc] - decreases attention to abc by a multiplier of 1.1
\( - literal character '('
\[ - literal character '['
\) - literal character ')'
\] - literal character ']'
\\ - literal character '\'
anything else - just text
>>> parse_prompt_attention('normal text')
[['normal text', 1.0]]
>>> parse_prompt_attention('an (important) word')
[['an ', 1.0], ['important', 1.1], [' word', 1.0]]
>>> parse_prompt_attention('(unbalanced')
[['unbalanced', 1.1]]
>>> parse_prompt_attention('\(literal\]')
[['(literal]', 1.0]]
>>> parse_prompt_attention('(unnecessary)(parens)')
[['unnecessaryparens', 1.1]]
>>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
[['a ', 1.0],
['house', 1.5730000000000004],
[' ', 1.1],
['on', 1.0],
[' a ', 1.1],
['hill', 0.55],
[', sun, ', 1.1],
['sky', 1.4641000000000006],
['.', 1.1]]
"""
res = []
round_brackets = []
square_brackets = []
round_bracket_multiplier = 1.1
square_bracket_multiplier = 1 / 1.1
def multiply_range(start_position, multiplier):
for p in range(start_position, len(res)):
res[p][1] *= multiplier
for m in re_attention.finditer(text):
text = m.group(0)
weight = m.group(1)
if text.startswith("\\"):
res.append([text[1:], 1.0])
elif text == "(":
round_brackets.append(len(res))
elif text == "[":
square_brackets.append(len(res))
elif weight is not None and len(round_brackets) > 0:
multiply_range(round_brackets.pop(), float(weight))
elif text == ")" and len(round_brackets) > 0:
multiply_range(round_brackets.pop(), round_bracket_multiplier)
elif text == "]" and len(square_brackets) > 0:
multiply_range(square_brackets.pop(), square_bracket_multiplier)
else:
res.append([text, 1.0])
for pos in round_brackets:
multiply_range(pos, round_bracket_multiplier)
for pos in square_brackets:
multiply_range(pos, square_bracket_multiplier)
if len(res) == 0:
res = [["", 1.0]]
# merge runs of identical weights
i = 0
while i + 1 < len(res):
if res[i][1] == res[i + 1][1]:
res[i][0] += res[i + 1][0]
res.pop(i + 1)
else:
i += 1
return res
def get_prompts_with_weights(pipe, prompt: List[str], max_length: int):
r"""
Tokenize a list of prompts and return its tokens with weights of each token.
No padding, starting or ending token is included.
"""
tokens = []
weights = []
truncated = False
for text in prompt:
texts_and_weights = parse_prompt_attention(text)
text_token = []
text_weight = []
for word, weight in texts_and_weights:
# tokenize and discard the starting and the ending token
token = pipe.tokenizer(word, return_tensors="np").input_ids[0, 1:-1]
text_token += list(token)
# copy the weight by length of token
text_weight += [weight] * len(token)
# stop if the text is too long (longer than truncation limit)
if len(text_token) > max_length:
truncated = True
break
# truncate
if len(text_token) > max_length:
truncated = True
text_token = text_token[:max_length]
text_weight = text_weight[:max_length]
tokens.append(text_token)
weights.append(text_weight)
if truncated:
logger.warning("Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples")
return tokens, weights
def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, no_boseos_middle=True, chunk_length=77):
r"""
Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length.
"""
max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)
weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length
for i in range(len(tokens)):
tokens[i] = [bos] + tokens[i] + [eos] * (max_length - 1 - len(tokens[i]))
if no_boseos_middle:
weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i]))
else:
w = []
if len(weights[i]) == 0:
w = [1.0] * weights_length
else:
for j in range(max_embeddings_multiples):
w.append(1.0) # weight for starting token in this chunk
w += weights[i][j * (chunk_length - 2) : min(len(weights[i]), (j + 1) * (chunk_length - 2))]
w.append(1.0) # weight for ending token in this chunk
w += [1.0] * (weights_length - len(w))
weights[i] = w[:]
return tokens, weights
def get_unweighted_text_embeddings(
pipe,
text_input: np.array,
chunk_length: int,
no_boseos_middle: Optional[bool] = True,
):
"""
When the length of tokens is a multiple of the capacity of the text encoder,
it should be split into chunks and sent to the text encoder individually.
"""
max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)
if max_embeddings_multiples > 1:
text_embeddings = []
for i in range(max_embeddings_multiples):
# extract the i-th chunk
text_input_chunk = text_input[:, i * (chunk_length - 2) : (i + 1) * (chunk_length - 2) + 2].copy()
# cover the head and the tail by the starting and the ending tokens
text_input_chunk[:, 0] = text_input[0, 0]
text_input_chunk[:, -1] = text_input[0, -1]
text_embedding = pipe.text_encoder(input_ids=text_input_chunk)[0]
if no_boseos_middle:
if i == 0:
# discard the ending token
text_embedding = text_embedding[:, :-1]
elif i == max_embeddings_multiples - 1:
# discard the starting token
text_embedding = text_embedding[:, 1:]
else:
# discard both starting and ending tokens
text_embedding = text_embedding[:, 1:-1]
text_embeddings.append(text_embedding)
text_embeddings = np.concatenate(text_embeddings, axis=1)
else:
text_embeddings = pipe.text_encoder(input_ids=text_input)[0]
return text_embeddings
def get_weighted_text_embeddings(
pipe,
prompt: Union[str, List[str]],
uncond_prompt: Optional[Union[str, List[str]]] = None,
max_embeddings_multiples: Optional[int] = 4,
no_boseos_middle: Optional[bool] = False,
skip_parsing: Optional[bool] = False,
skip_weighting: Optional[bool] = False,
**kwargs,
):
r"""
Prompts can be assigned with local weights using brackets. For example,
prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful',
and the embedding tokens corresponding to the words get multiplied by a constant, 1.1.
Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.
Args:
pipe (`DiffusionPipeline`):
Pipe to provide access to the tokenizer and the text encoder.
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
uncond_prompt (`str` or `List[str]`):
The unconditional prompt or prompts for guide the image generation. If unconditional prompt
is provided, the embeddings of prompt and uncond_prompt are concatenated.
max_embeddings_multiples (`int`, *optional*, defaults to `1`):
The max multiple length of prompt embeddings compared to the max output length of text encoder.
no_boseos_middle (`bool`, *optional*, defaults to `False`):
If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and
ending token in each of the chunk in the middle.
skip_parsing (`bool`, *optional*, defaults to `False`):
Skip the parsing of brackets.
skip_weighting (`bool`, *optional*, defaults to `False`):
Skip the weighting. When the parsing is skipped, it is forced True.
"""
max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
if isinstance(prompt, str):
prompt = [prompt]
if not skip_parsing:
prompt_tokens, prompt_weights = get_prompts_with_weights(pipe, prompt, max_length - 2)
if uncond_prompt is not None:
if isinstance(uncond_prompt, str):
uncond_prompt = [uncond_prompt]
uncond_tokens, uncond_weights = get_prompts_with_weights(pipe, uncond_prompt, max_length - 2)
else:
prompt_tokens = [
token[1:-1]
for token in pipe.tokenizer(prompt, max_length=max_length, truncation=True, return_tensors="np").input_ids
]
prompt_weights = [[1.0] * len(token) for token in prompt_tokens]
if uncond_prompt is not None:
if isinstance(uncond_prompt, str):
uncond_prompt = [uncond_prompt]
uncond_tokens = [
token[1:-1]
for token in pipe.tokenizer(
uncond_prompt,
max_length=max_length,
truncation=True,
return_tensors="np",
).input_ids
]
uncond_weights = [[1.0] * len(token) for token in uncond_tokens]
# round up the longest length of tokens to a multiple of (model_max_length - 2)
max_length = max([len(token) for token in prompt_tokens])
if uncond_prompt is not None:
max_length = max(max_length, max([len(token) for token in uncond_tokens]))
max_embeddings_multiples = min(
max_embeddings_multiples,
(max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1,
)
max_embeddings_multiples = max(1, max_embeddings_multiples)
max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
# pad the length of tokens and weights
bos = pipe.tokenizer.bos_token_id
eos = pipe.tokenizer.eos_token_id
prompt_tokens, prompt_weights = pad_tokens_and_weights(
prompt_tokens,
prompt_weights,
max_length,
bos,
eos,
no_boseos_middle=no_boseos_middle,
chunk_length=pipe.tokenizer.model_max_length,
)
prompt_tokens = np.array(prompt_tokens, dtype=np.int32)
if uncond_prompt is not None:
uncond_tokens, uncond_weights = pad_tokens_and_weights(
uncond_tokens,
uncond_weights,
max_length,
bos,
eos,
no_boseos_middle=no_boseos_middle,
chunk_length=pipe.tokenizer.model_max_length,
)
uncond_tokens = np.array(uncond_tokens, dtype=np.int32)
# get the embeddings
text_embeddings = get_unweighted_text_embeddings(
pipe,
prompt_tokens,
pipe.tokenizer.model_max_length,
no_boseos_middle=no_boseos_middle,
)
prompt_weights = np.array(prompt_weights, dtype=text_embeddings.dtype)
if uncond_prompt is not None:
uncond_embeddings = get_unweighted_text_embeddings(
pipe,
uncond_tokens,
pipe.tokenizer.model_max_length,
no_boseos_middle=no_boseos_middle,
)
uncond_weights = np.array(uncond_weights, dtype=uncond_embeddings.dtype)
# assign weights to the prompts and normalize in the sense of mean
# TODO: should we normalize by chunk or in a whole (current implementation)?
if (not skip_parsing) and (not skip_weighting):
previous_mean = text_embeddings.mean(axis=(-2, -1))
text_embeddings *= prompt_weights[:, :, None]
text_embeddings *= (previous_mean / text_embeddings.mean(axis=(-2, -1)))[:, None, None]
if uncond_prompt is not None:
previous_mean = uncond_embeddings.mean(axis=(-2, -1))
uncond_embeddings *= uncond_weights[:, :, None]
uncond_embeddings *= (previous_mean / uncond_embeddings.mean(axis=(-2, -1)))[:, None, None]
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
if uncond_prompt is not None:
return text_embeddings, uncond_embeddings
return text_embeddings
def preprocess_image(image):
w, h = image.size
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
return 2.0 * image - 1.0
def preprocess_mask(mask):
mask = mask.convert("L")
w, h = mask.size
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
mask = mask.resize((w // 8, h // 8), resample=PIL_INTERPOLATION["nearest"])
mask = np.array(mask).astype(np.float32) / 255.0
mask = np.tile(mask, (4, 1, 1))
mask = mask[None].transpose(0, 1, 2, 3) # what does this step do?
mask = 1 - mask # repaint white, keep black
return mask
class OnnxStableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using Stable Diffusion without tokens length limit, and support parsing
weighting in prompt.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
"""
def __init__(
self,
vae_encoder: OnnxRuntimeModel,
vae_decoder: OnnxRuntimeModel,
text_encoder: OnnxRuntimeModel,
tokenizer: CLIPTokenizer,
unet: OnnxRuntimeModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: OnnxRuntimeModel,
feature_extractor: CLIPFeatureExtractor,
):
super().__init__()
self.register_modules(
vae_encoder=vae_encoder,
vae_decoder=vae_decoder,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
negative_prompt: Optional[Union[str, List[str]]] = None,
init_image: Union[np.ndarray, PIL.Image.Image] = None,
mask_image: Union[np.ndarray, PIL.Image.Image] = None,
height: int = 512,
width: int = 512,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
strength: float = 0.8,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[np.random.RandomState] = None,
latents: Optional[np.ndarray] = None,
max_embeddings_multiples: Optional[int] = 3,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
is_cancelled_callback: Optional[Callable[[], bool]] = None,
callback_steps: Optional[int] = 1,
**kwargs,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
init_image (`np.ndarray` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
mask_image (`np.ndarray` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, to mask `init_image`. White pixels in the mask will be
replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to transform the reference `init_image`. Must be between 0 and 1.
`init_image` will be used as a starting point, adding more noise to it the larger the `strength`. The
number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
noise will be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `init_image`.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`np.random.RandomState`, *optional*):
A np.random.RandomState to make generation deterministic.
latents (`np.ndarray`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
The max multiple length of prompt embeddings compared to the max output length of text encoder.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
is_cancelled_callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. If the function returns
`True`, the inference will be cancelled.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
`None` if cancelled by `is_cancelled_callback`,
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
if isinstance(prompt, str):
batch_size = 1
prompt = [prompt]
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
# get prompt text embeddings
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if negative_prompt is None:
negative_prompt = [""] * batch_size
elif isinstance(negative_prompt, str):
negative_prompt = [negative_prompt] * batch_size
if batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
if generator is None:
generator = np.random
text_embeddings, uncond_embeddings = get_weighted_text_embeddings(
pipe=self,
prompt=prompt,
uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
max_embeddings_multiples=max_embeddings_multiples,
**kwargs,
)
text_embeddings = text_embeddings.repeat(num_images_per_prompt, 0)
if do_classifier_free_guidance:
uncond_embeddings = uncond_embeddings.repeat(num_images_per_prompt, 0)
text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
# set timesteps
self.scheduler.set_timesteps(num_inference_steps)
latents_dtype = text_embeddings.dtype
init_latents_orig = None
mask = None
noise = None
if init_image is None:
latents_shape = (
batch_size * num_images_per_prompt,
4,
height // 8,
width // 8,
)
if latents is None:
latents = generator.randn(*latents_shape).astype(latents_dtype)
elif latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
timesteps = self.scheduler.timesteps.to(self.device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
else:
if isinstance(init_image, PIL.Image.Image):
init_image = preprocess_image(init_image)
# encode the init image into latents and scale the latents
init_image = init_image.astype(latents_dtype)
init_latents = self.vae_encoder(sample=init_image)[0]
init_latents = 0.18215 * init_latents
init_latents = np.concatenate([init_latents] * batch_size * num_images_per_prompt)
init_latents_orig = init_latents
# preprocess mask
if mask_image is not None:
if isinstance(mask_image, PIL.Image.Image):
mask_image = preprocess_mask(mask_image)
mask_image = mask_image.astype(latents_dtype)
mask = np.concatenate([mask_image] * batch_size * num_images_per_prompt)
# check sizes
if not mask.shape == init_latents.shape:
print(mask.shape, init_latents.shape)
raise ValueError("The mask and init_image should be the same size!")
# get the original timestep using init_timestep
offset = self.scheduler.config.get("steps_offset", 0)
init_timestep = int(num_inference_steps * strength) + offset
init_timestep = min(init_timestep, num_inference_steps)
timesteps = self.scheduler.timesteps[-init_timestep]
timesteps = torch.tensor([timesteps] * batch_size * num_images_per_prompt)
# add noise to latents using the timesteps
noise = generator.randn(*init_latents.shape).astype(latents_dtype)
latents = self.scheduler.add_noise(
torch.from_numpy(init_latents), torch.from_numpy(noise), timesteps
).numpy()
t_start = max(num_inference_steps - init_timestep + offset, 0)
timesteps = self.scheduler.timesteps[t_start:]
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
for i, t in enumerate(self.progress_bar(timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
sample=latent_model_input,
timestep=np.array([t]),
encoder_hidden_states=text_embeddings,
)
noise_pred = noise_pred[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample.numpy()
if mask is not None:
# masking
init_latents_proper = self.scheduler.add_noise(
torch.from_numpy(init_latents_orig),
torch.from_numpy(noise),
torch.tensor([t]),
).numpy()
latents = (init_latents_proper * mask) + (latents * (1 - mask))
# call the callback, if provided
if i % callback_steps == 0:
if callback is not None:
callback(i, t, latents)
if is_cancelled_callback is not None and is_cancelled_callback():
return None
latents = 1 / 0.18215 * latents
# image = self.vae_decoder(latent_sample=latents)[0]
# it seems likes there is a problem for using half-precision vae decoder if batchsize>1
image = []
for i in range(latents.shape[0]):
image.append(self.vae_decoder(latent_sample=latents[i : i + 1])[0])
image = np.concatenate(image)
image = np.clip(image / 2 + 0.5, 0, 1)
image = image.transpose((0, 2, 3, 1))
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(
self.numpy_to_pil(image), return_tensors="np"
).pixel_values.astype(image.dtype)
# There will throw an error if use safety_checker directly and batchsize>1
images, has_nsfw_concept = [], []
for i in range(image.shape[0]):
image_i, has_nsfw_concept_i = self.safety_checker(
clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
)
images.append(image_i)
has_nsfw_concept.append(has_nsfw_concept_i[0])
image = np.concatenate(images)
else:
has_nsfw_concept = None
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
def text2img(
self,
prompt: Union[str, List[str]],
negative_prompt: Optional[Union[str, List[str]]] = None,
height: int = 512,
width: int = 512,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[np.random.RandomState] = None,
latents: Optional[np.ndarray] = None,
max_embeddings_multiples: Optional[int] = 3,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
callback_steps: Optional[int] = 1,
**kwargs,
):
r"""
Function for text-to-image generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`np.random.RandomState`, *optional*):
A np.random.RandomState to make generation deterministic.
latents (`np.ndarray`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
The max multiple length of prompt embeddings compared to the max output length of text encoder.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
return self.__call__(
prompt=prompt,
negative_prompt=negative_prompt,
height=height,
width=width,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
num_images_per_prompt=num_images_per_prompt,
eta=eta,
generator=generator,
latents=latents,
max_embeddings_multiples=max_embeddings_multiples,
output_type=output_type,
return_dict=return_dict,
callback=callback,
callback_steps=callback_steps,
**kwargs,
)
def img2img(
self,
init_image: Union[np.ndarray, PIL.Image.Image],
prompt: Union[str, List[str]],
negative_prompt: Optional[Union[str, List[str]]] = None,
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
num_images_per_prompt: Optional[int] = 1,
eta: Optional[float] = 0.0,
generator: Optional[np.random.RandomState] = None,
max_embeddings_multiples: Optional[int] = 3,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
callback_steps: Optional[int] = 1,
**kwargs,
):
r"""
Function for image-to-image generation.
Args:
init_image (`np.ndarray` or `PIL.Image.Image`):
`Image`, or ndarray representing an image batch, that will be used as the starting point for the
process.
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to transform the reference `init_image`. Must be between 0 and 1.
`init_image` will be used as a starting point, adding more noise to it the larger the `strength`. The
number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
noise will be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `init_image`.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter will be modulated by `strength`.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`np.random.RandomState`, *optional*):
A np.random.RandomState to make generation deterministic.
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
The max multiple length of prompt embeddings compared to the max output length of text encoder.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
return self.__call__(
prompt=prompt,
negative_prompt=negative_prompt,
init_image=init_image,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
strength=strength,
num_images_per_prompt=num_images_per_prompt,
eta=eta,
generator=generator,
max_embeddings_multiples=max_embeddings_multiples,
output_type=output_type,
return_dict=return_dict,
callback=callback,
callback_steps=callback_steps,
**kwargs,
)
def inpaint(
self,
init_image: Union[np.ndarray, PIL.Image.Image],
mask_image: Union[np.ndarray, PIL.Image.Image],
prompt: Union[str, List[str]],
negative_prompt: Optional[Union[str, List[str]]] = None,
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
num_images_per_prompt: Optional[int] = 1,
eta: Optional[float] = 0.0,
generator: Optional[np.random.RandomState] = None,
max_embeddings_multiples: Optional[int] = 3,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
callback_steps: Optional[int] = 1,
**kwargs,
):
r"""
Function for inpaint.
Args:
init_image (`np.ndarray` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process. This is the image whose masked region will be inpainted.
mask_image (`np.ndarray` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, to mask `init_image`. White pixels in the mask will be
replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`
is 1, the denoising process will be run on the masked area for the full number of iterations specified
in `num_inference_steps`. `init_image` will be used as a reference for the masked area, adding more
noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur.
num_inference_steps (`int`, *optional*, defaults to 50):
The reference number of denoising steps. More denoising steps usually lead to a higher quality image at
the expense of slower inference. This parameter will be modulated by `strength`, as explained above.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`np.random.RandomState`, *optional*):
A np.random.RandomState to make generation deterministic.
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
The max multiple length of prompt embeddings compared to the max output length of text encoder.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
return self.__call__(
prompt=prompt,
negative_prompt=negative_prompt,
init_image=init_image,
mask_image=mask_image,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
strength=strength,
num_images_per_prompt=num_images_per_prompt,
eta=eta,
generator=generator,
max_embeddings_multiples=max_embeddings_multiples,
output_type=output_type,
return_dict=return_dict,
callback=callback,
callback_steps=callback_steps,
**kwargs,
)
| diffusers-ft-main | examples/community/lpw_stable_diffusion_onnx.py |
"""
modeled after the textual_inversion.py / train_dreambooth.py and the work
of justinpinkney here: https://github.com/justinpinkney/stable-diffusion/blob/main/notebooks/imagic.ipynb
"""
import inspect
import warnings
from typing import List, Optional, Union
import numpy as np
import torch
import torch.nn.functional as F
import PIL
from accelerate import Accelerator
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
# TODO: remove and import from diffusers.utils when the new version of diffusers is released
from packaging import version
from tqdm.auto import tqdm
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
PIL_INTERPOLATION = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling.BILINEAR,
"bicubic": PIL.Image.Resampling.BICUBIC,
"lanczos": PIL.Image.Resampling.LANCZOS,
"nearest": PIL.Image.Resampling.NEAREST,
}
else:
PIL_INTERPOLATION = {
"linear": PIL.Image.LINEAR,
"bilinear": PIL.Image.BILINEAR,
"bicubic": PIL.Image.BICUBIC,
"lanczos": PIL.Image.LANCZOS,
"nearest": PIL.Image.NEAREST,
}
# ------------------------------------------------------------------------------
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def preprocess(image):
w, h = image.size
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.0 * image - 1.0
class ImagicStableDiffusionPipeline(DiffusionPipeline):
r"""
Pipeline for imagic image editing.
See paper here: https://arxiv.org/pdf/2210.09276.pdf
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offsensive or harmful.
Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.
Args:
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(slice_size)
def disable_attention_slicing(self):
r"""
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
back to computing attention in one step.
"""
# set slice_size = `None` to disable `attention slicing`
self.enable_attention_slicing(None)
def train(
self,
prompt: Union[str, List[str]],
init_image: Union[torch.FloatTensor, PIL.Image.Image],
height: Optional[int] = 512,
width: Optional[int] = 512,
generator: Optional[torch.Generator] = None,
embedding_learning_rate: float = 0.001,
diffusion_model_learning_rate: float = 2e-6,
text_embedding_optimization_steps: int = 500,
model_fine_tuning_optimization_steps: int = 1000,
**kwargs,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
accelerator = Accelerator(
gradient_accumulation_steps=1,
mixed_precision="fp16",
)
if "torch_device" in kwargs:
device = kwargs.pop("torch_device")
warnings.warn(
"`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
" Consider using `pipe.to(torch_device)` instead."
)
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
self.to(device)
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
# Freeze vae and unet
self.vae.requires_grad_(False)
self.unet.requires_grad_(False)
self.text_encoder.requires_grad_(False)
self.unet.eval()
self.vae.eval()
self.text_encoder.eval()
if accelerator.is_main_process:
accelerator.init_trackers(
"imagic",
config={
"embedding_learning_rate": embedding_learning_rate,
"text_embedding_optimization_steps": text_embedding_optimization_steps,
},
)
# get text embeddings for prompt
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncaton=True,
return_tensors="pt",
)
text_embeddings = torch.nn.Parameter(
self.text_encoder(text_input.input_ids.to(self.device))[0], requires_grad=True
)
text_embeddings = text_embeddings.detach()
text_embeddings.requires_grad_()
text_embeddings_orig = text_embeddings.clone()
# Initialize the optimizer
optimizer = torch.optim.Adam(
[text_embeddings], # only optimize the embeddings
lr=embedding_learning_rate,
)
if isinstance(init_image, PIL.Image.Image):
init_image = preprocess(init_image)
latents_dtype = text_embeddings.dtype
init_image = init_image.to(device=self.device, dtype=latents_dtype)
init_latent_image_dist = self.vae.encode(init_image).latent_dist
init_image_latents = init_latent_image_dist.sample(generator=generator)
init_image_latents = 0.18215 * init_image_latents
progress_bar = tqdm(range(text_embedding_optimization_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
global_step = 0
logger.info("First optimizing the text embedding to better reconstruct the init image")
for _ in range(text_embedding_optimization_steps):
with accelerator.accumulate(text_embeddings):
# Sample noise that we'll add to the latents
noise = torch.randn(init_image_latents.shape).to(init_image_latents.device)
timesteps = torch.randint(1000, (1,), device=init_image_latents.device)
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = self.scheduler.add_noise(init_image_latents, noise, timesteps)
# Predict the noise residual
noise_pred = self.unet(noisy_latents, timesteps, text_embeddings).sample
loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
accelerator.backward(loss)
optimizer.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
logs = {"loss": loss.detach().item()} # , "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
accelerator.wait_for_everyone()
text_embeddings.requires_grad_(False)
# Now we fine tune the unet to better reconstruct the image
self.unet.requires_grad_(True)
self.unet.train()
optimizer = torch.optim.Adam(
self.unet.parameters(), # only optimize unet
lr=diffusion_model_learning_rate,
)
progress_bar = tqdm(range(model_fine_tuning_optimization_steps), disable=not accelerator.is_local_main_process)
logger.info("Next fine tuning the entire model to better reconstruct the init image")
for _ in range(model_fine_tuning_optimization_steps):
with accelerator.accumulate(self.unet.parameters()):
# Sample noise that we'll add to the latents
noise = torch.randn(init_image_latents.shape).to(init_image_latents.device)
timesteps = torch.randint(1000, (1,), device=init_image_latents.device)
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = self.scheduler.add_noise(init_image_latents, noise, timesteps)
# Predict the noise residual
noise_pred = self.unet(noisy_latents, timesteps, text_embeddings).sample
loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
accelerator.backward(loss)
optimizer.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
logs = {"loss": loss.detach().item()} # , "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
accelerator.wait_for_everyone()
self.text_embeddings_orig = text_embeddings_orig
self.text_embeddings = text_embeddings
@torch.no_grad()
def __call__(
self,
alpha: float = 1.2,
height: Optional[int] = 512,
width: Optional[int] = 512,
num_inference_steps: Optional[int] = 50,
generator: Optional[torch.Generator] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
guidance_scale: float = 7.5,
eta: float = 0.0,
**kwargs,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if self.text_embeddings is None:
raise ValueError("Please run the pipe.train() before trying to generate an image.")
if self.text_embeddings_orig is None:
raise ValueError("Please run the pipe.train() before trying to generate an image.")
text_embeddings = alpha * self.text_embeddings_orig + (1 - alpha) * self.text_embeddings
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens = [""]
max_length = self.tokenizer.model_max_length
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_embeddings.shape[1]
uncond_embeddings = uncond_embeddings.view(1, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
latents_shape = (1, self.unet.in_channels, height // 8, width // 8)
latents_dtype = text_embeddings.dtype
if self.device.type == "mps":
# randn does not exist on mps
latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
self.device
)
else:
latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
# set timesteps
self.scheduler.set_timesteps(num_inference_steps)
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
timesteps_tensor = self.scheduler.timesteps.to(self.device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
self.device
)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
)
else:
has_nsfw_concept = None
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| diffusers-ft-main | examples/community/imagic_stable_diffusion.py |
import inspect
from typing import List, Optional, Union
import torch
from torch import nn
from torch.nn import functional as F
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
UNet2DConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
class MakeCutouts(nn.Module):
def __init__(self, cut_size, cut_power=1.0):
super().__init__()
self.cut_size = cut_size
self.cut_power = cut_power
def forward(self, pixel_values, num_cutouts):
sideY, sideX = pixel_values.shape[2:4]
max_size = min(sideX, sideY)
min_size = min(sideX, sideY, self.cut_size)
cutouts = []
for _ in range(num_cutouts):
size = int(torch.rand([]) ** self.cut_power * (max_size - min_size) + min_size)
offsetx = torch.randint(0, sideX - size + 1, ())
offsety = torch.randint(0, sideY - size + 1, ())
cutout = pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
cutouts.append(F.adaptive_avg_pool2d(cutout, self.cut_size))
return torch.cat(cutouts)
def spherical_dist_loss(x, y):
x = F.normalize(x, dim=-1)
y = F.normalize(y, dim=-1)
return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
def set_requires_grad(model, value):
for param in model.parameters():
param.requires_grad = value
class CLIPGuidedStableDiffusion(DiffusionPipeline):
"""CLIP guided stable diffusion based on the amazing repo by @crowsonkb and @Jack000
- https://github.com/Jack000/glid-3-xl
- https://github.dev/crowsonkb/k-diffusion
"""
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
clip_model: CLIPModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler],
feature_extractor: CLIPFeatureExtractor,
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder=text_encoder,
clip_model=clip_model,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
feature_extractor=feature_extractor,
)
self.normalize = transforms.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
cut_out_size = (
feature_extractor.size
if isinstance(feature_extractor.size, int)
else feature_extractor.size["shortest_edge"]
)
self.make_cutouts = MakeCutouts(cut_out_size)
set_requires_grad(self.text_encoder, False)
set_requires_grad(self.clip_model, False)
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(slice_size)
def disable_attention_slicing(self):
self.enable_attention_slicing(None)
def freeze_vae(self):
set_requires_grad(self.vae, False)
def unfreeze_vae(self):
set_requires_grad(self.vae, True)
def freeze_unet(self):
set_requires_grad(self.unet, False)
def unfreeze_unet(self):
set_requires_grad(self.unet, True)
@torch.enable_grad()
def cond_fn(
self,
latents,
timestep,
index,
text_embeddings,
noise_pred_original,
text_embeddings_clip,
clip_guidance_scale,
num_cutouts,
use_cutouts=True,
):
latents = latents.detach().requires_grad_()
if isinstance(self.scheduler, LMSDiscreteScheduler):
sigma = self.scheduler.sigmas[index]
# the model input needs to be scaled to match the continuous ODE formulation in K-LMS
latent_model_input = latents / ((sigma**2 + 1) ** 0.5)
else:
latent_model_input = latents
# predict the noise residual
noise_pred = self.unet(latent_model_input, timestep, encoder_hidden_states=text_embeddings).sample
if isinstance(self.scheduler, (PNDMScheduler, DDIMScheduler)):
alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
beta_prod_t = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)
fac = torch.sqrt(beta_prod_t)
sample = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler, LMSDiscreteScheduler):
sigma = self.scheduler.sigmas[index]
sample = latents - sigma * noise_pred
else:
raise ValueError(f"scheduler type {type(self.scheduler)} not supported")
sample = 1 / 0.18215 * sample
image = self.vae.decode(sample).sample
image = (image / 2 + 0.5).clamp(0, 1)
if use_cutouts:
image = self.make_cutouts(image, num_cutouts)
else:
image = transforms.Resize(self.feature_extractor.size)(image)
image = self.normalize(image).to(latents.dtype)
image_embeddings_clip = self.clip_model.get_image_features(image)
image_embeddings_clip = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
if use_cutouts:
dists = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip)
dists = dists.view([num_cutouts, sample.shape[0], -1])
loss = dists.sum(2).mean(0).sum() * clip_guidance_scale
else:
loss = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip).mean() * clip_guidance_scale
grads = -torch.autograd.grad(loss, latents)[0]
if isinstance(self.scheduler, LMSDiscreteScheduler):
latents = latents.detach() + grads * (sigma**2)
noise_pred = noise_pred_original
else:
noise_pred = noise_pred_original - torch.sqrt(beta_prod_t) * grads
return noise_pred, latents
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
height: Optional[int] = 512,
width: Optional[int] = 512,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
clip_guidance_scale: Optional[float] = 100,
clip_prompt: Optional[Union[str, List[str]]] = None,
num_cutouts: Optional[int] = 4,
use_cutouts: Optional[bool] = True,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
):
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
# get prompt text embeddings
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
# duplicate text embeddings for each generation per prompt
text_embeddings = text_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
if clip_guidance_scale > 0:
if clip_prompt is not None:
clip_text_input = self.tokenizer(
clip_prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
).input_ids.to(self.device)
else:
clip_text_input = text_input.input_ids.to(self.device)
text_embeddings_clip = self.clip_model.get_text_features(clip_text_input)
text_embeddings_clip = text_embeddings_clip / text_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
# duplicate text embeddings clip for each generation per prompt
text_embeddings_clip = text_embeddings_clip.repeat_interleave(num_images_per_prompt, dim=0)
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
max_length = text_input.input_ids.shape[-1]
uncond_input = self.tokenizer([""], padding="max_length", max_length=max_length, return_tensors="pt")
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# duplicate unconditional embeddings for each generation per prompt
uncond_embeddings = uncond_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
latents_dtype = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
self.device
)
else:
latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
latents = latents.to(self.device)
# set timesteps
accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
extra_set_kwargs = {}
if accepts_offset:
extra_set_kwargs["offset"] = 1
self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
timesteps_tensor = self.scheduler.timesteps.to(self.device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
# perform classifier free guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
text_embeddings_for_guidance = (
text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
)
noise_pred, latents = self.cond_fn(
latents,
t,
i,
text_embeddings_for_guidance,
noise_pred,
text_embeddings_clip,
clip_guidance_scale,
num_cutouts,
use_cutouts,
)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# scale and decode the image latents with vae
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)
| diffusers-ft-main | examples/community/clip_guided_stable_diffusion.py |
"""
modified based on diffusion library from Huggingface: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class SeedResizeStableDiffusionPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.
Args:
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(slice_size)
def disable_attention_slicing(self):
r"""
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
back to computing attention in one step.
"""
# set slice_size = `None` to disable `attention slicing`
self.enable_attention_slicing(None)
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
height: int = 512,
width: int = 512,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
text_embeddings: Optional[torch.FloatTensor] = None,
**kwargs,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
if text_embeddings is None:
text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""]
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_embeddings.shape[1]
uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)
uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
latents_shape_reference = (batch_size * num_images_per_prompt, self.unet.in_channels, 64, 64)
latents_dtype = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
latents_reference = torch.randn(
latents_shape_reference, generator=generator, device="cpu", dtype=latents_dtype
).to(self.device)
latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
self.device
)
else:
latents_reference = torch.randn(
latents_shape_reference, generator=generator, device=self.device, dtype=latents_dtype
)
latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
else:
if latents_reference.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
latents_reference = latents_reference.to(self.device)
latents = latents.to(self.device)
# This is the key part of the pipeline where we
# try to ensure that the generated images w/ the same seed
# but different sizes actually result in similar images
dx = (latents_shape[3] - latents_shape_reference[3]) // 2
dy = (latents_shape[2] - latents_shape_reference[2]) // 2
w = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
h = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
tx = 0 if dx < 0 else dx
ty = 0 if dy < 0 else dy
dx = max(-dx, 0)
dy = max(-dy, 0)
# import pdb
# pdb.set_trace()
latents[:, :, ty : ty + h, tx : tx + w] = latents_reference[:, :, dy : dy + h, dx : dx + w]
# set timesteps
self.scheduler.set_timesteps(num_inference_steps)
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
timesteps_tensor = self.scheduler.timesteps.to(self.device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
self.device
)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
)
else:
has_nsfw_concept = None
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| diffusers-ft-main | examples/community/seed_resize_stable_diffusion.py |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
import PIL.Image
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionImg2ImgPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionPipeline,
UNet2DConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class StableDiffusionMegaPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionMegaSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
@property
def components(self) -> Dict[str, Any]:
return {k: getattr(self, k) for k in self.config.keys() if not k.startswith("_")}
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.
Args:
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(slice_size)
def disable_attention_slicing(self):
r"""
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
back to computing attention in one step.
"""
# set slice_size = `None` to disable `attention slicing`
self.enable_attention_slicing(None)
@torch.no_grad()
def inpaint(
self,
prompt: Union[str, List[str]],
init_image: Union[torch.FloatTensor, PIL.Image.Image],
mask_image: Union[torch.FloatTensor, PIL.Image.Image],
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: Optional[float] = 0.0,
generator: Optional[torch.Generator] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
):
# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
return StableDiffusionInpaintPipelineLegacy(**self.components)(
prompt=prompt,
init_image=init_image,
mask_image=mask_image,
strength=strength,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
eta=eta,
generator=generator,
output_type=output_type,
return_dict=return_dict,
callback=callback,
)
@torch.no_grad()
def img2img(
self,
prompt: Union[str, List[str]],
init_image: Union[torch.FloatTensor, PIL.Image.Image],
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: Optional[float] = 0.0,
generator: Optional[torch.Generator] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
**kwargs,
):
# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
return StableDiffusionImg2ImgPipeline(**self.components)(
prompt=prompt,
init_image=init_image,
strength=strength,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
eta=eta,
generator=generator,
output_type=output_type,
return_dict=return_dict,
callback=callback,
callback_steps=callback_steps,
)
@torch.no_grad()
def text2img(
self,
prompt: Union[str, List[str]],
height: int = 512,
width: int = 512,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
):
# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
return StableDiffusionPipeline(**self.components)(
prompt=prompt,
height=height,
width=width,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
eta=eta,
generator=generator,
latents=latents,
output_type=output_type,
return_dict=return_dict,
callback=callback,
callback_steps=callback_steps,
)
| diffusers-ft-main | examples/community/stable_diffusion_mega.py |
import inspect
from typing import Callable, List, Optional, Tuple, Union
import numpy as np
import torch
import PIL
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, logging
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def prepare_mask_and_masked_image(image, mask):
image = np.array(image.convert("RGB"))
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
mask = np.array(mask.convert("L"))
mask = mask.astype(np.float32) / 255.0
mask = mask[None, None]
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
mask = torch.from_numpy(mask)
masked_image = image * (mask < 0.5)
return mask, masked_image
def check_size(image, height, width):
if isinstance(image, PIL.Image.Image):
w, h = image.size
elif isinstance(image, torch.Tensor):
*_, h, w = image.shape
if h != height or w != width:
raise ValueError(f"Image size should be {height}x{width}, but got {h}x{w}")
def overlay_inner_image(image, inner_image, paste_offset: Tuple[int] = (0, 0)):
inner_image = inner_image.convert("RGBA")
image = image.convert("RGB")
image.paste(inner_image, paste_offset, inner_image)
image = image.convert("RGB")
return image
class ImageToImageInpaintingPipeline(DiffusionPipeline):
r"""
Pipeline for text-guided image-to-image inpainting using Stable Diffusion. *This is an experimental feature*.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.
Args:
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(slice_size)
def disable_attention_slicing(self):
r"""
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
back to computing attention in one step.
"""
# set slice_size = `None` to disable `attention slicing`
self.enable_attention_slicing(None)
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
image: Union[torch.FloatTensor, PIL.Image.Image],
inner_image: Union[torch.FloatTensor, PIL.Image.Image],
mask_image: Union[torch.FloatTensor, PIL.Image.Image],
height: int = 512,
width: int = 512,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
**kwargs,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
image (`torch.Tensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
be masked out with `mask_image` and repainted according to `prompt`.
inner_image (`torch.Tensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch which will be overlayed onto `image`. Non-transparent
regions of `inner_image` must fit inside white pixels in `mask_image`. Expects four channels, with
the last channel representing the alpha channel, which will be used to blend `inner_image` with
`image`. If not provided, it will be forcibly cast to RGBA.
mask_image (`PIL.Image.Image`):
`Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
instead of 3, so the expected shape would be `(B, H, W, 1)`.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
# check if input sizes are correct
check_size(image, height, width)
check_size(inner_image, height, width)
check_size(mask_image, height, width)
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""]
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_embeddings.shape[1]
uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)
uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
num_channels_latents = self.vae.config.latent_channels
latents_shape = (batch_size * num_images_per_prompt, num_channels_latents, height // 8, width // 8)
latents_dtype = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
self.device
)
else:
latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
latents = latents.to(self.device)
# overlay the inner image
image = overlay_inner_image(image, inner_image)
# prepare mask and masked_image
mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
mask = mask.to(device=self.device, dtype=text_embeddings.dtype)
masked_image = masked_image.to(device=self.device, dtype=text_embeddings.dtype)
# resize the mask to latents shape as we concatenate the mask to the latents
mask = torch.nn.functional.interpolate(mask, size=(height // 8, width // 8))
# encode the mask image into latents space so we can concatenate it to the latents
masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)
masked_image_latents = 0.18215 * masked_image_latents
# duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
mask = mask.repeat(batch_size * num_images_per_prompt, 1, 1, 1)
masked_image_latents = masked_image_latents.repeat(batch_size * num_images_per_prompt, 1, 1, 1)
mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
masked_image_latents = (
torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
)
num_channels_mask = mask.shape[1]
num_channels_masked_image = masked_image_latents.shape[1]
if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
raise ValueError(
f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
" `pipeline.unet` or your `mask_image` or `image` input."
)
# set timesteps
self.scheduler.set_timesteps(num_inference_steps)
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
timesteps_tensor = self.scheduler.timesteps.to(self.device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
# concat latents, mask, masked_image_latents in the channel dimension
latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
self.device
)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
)
else:
has_nsfw_concept = None
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| diffusers-ft-main | examples/community/img2img_inpainting.py |
"""
modified based on diffusion library from Huggingface: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
"""
import inspect
import warnings
from typing import List, Optional, Union
import torch
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
class ComposableStableDiffusionPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offsensive or harmful.
Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.
Args:
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(slice_size)
def disable_attention_slicing(self):
r"""
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
back to computing attention in one step.
"""
# set slice_size = `None` to disable `attention slicing`
self.enable_attention_slicing(None)
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
height: Optional[int] = 512,
width: Optional[int] = 512,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
eta: Optional[float] = 0.0,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
weights: Optional[str] = "",
**kwargs,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
if "torch_device" in kwargs:
device = kwargs.pop("torch_device")
warnings.warn(
"`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
" Consider using `pipe.to(torch_device)` instead."
)
# Set device as before (to be removed in 0.3.0)
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
self.to(device)
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if "|" in prompt:
prompt = [x.strip() for x in prompt.split("|")]
print(f"composing {prompt}...")
# get prompt text embeddings
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
if not weights:
# specify weights for prompts (excluding the unconditional score)
print("using equal weights for all prompts...")
pos_weights = torch.tensor(
[1 / (text_embeddings.shape[0] - 1)] * (text_embeddings.shape[0] - 1), device=self.device
).reshape(-1, 1, 1, 1)
neg_weights = torch.tensor([1.0], device=self.device).reshape(-1, 1, 1, 1)
mask = torch.tensor([False] + [True] * pos_weights.shape[0], dtype=torch.bool)
else:
# set prompt weight for each
num_prompts = len(prompt) if isinstance(prompt, list) else 1
weights = [float(w.strip()) for w in weights.split("|")]
if len(weights) < num_prompts:
weights.append(1.0)
weights = torch.tensor(weights, device=self.device)
assert len(weights) == text_embeddings.shape[0], "weights specified are not equal to the number of prompts"
pos_weights = []
neg_weights = []
mask = [] # first one is unconditional score
for w in weights:
if w > 0:
pos_weights.append(w)
mask.append(True)
else:
neg_weights.append(abs(w))
mask.append(False)
# normalize the weights
pos_weights = torch.tensor(pos_weights, device=self.device).reshape(-1, 1, 1, 1)
pos_weights = pos_weights / pos_weights.sum()
neg_weights = torch.tensor(neg_weights, device=self.device).reshape(-1, 1, 1, 1)
neg_weights = neg_weights / neg_weights.sum()
mask = torch.tensor(mask, device=self.device, dtype=torch.bool)
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
max_length = text_input.input_ids.shape[-1]
if torch.all(mask):
# no negative prompts, so we use empty string as the negative prompt
uncond_input = self.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
)
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
# update negative weights
neg_weights = torch.tensor([1.0], device=self.device)
mask = torch.tensor([False] + mask.detach().tolist(), device=self.device, dtype=torch.bool)
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
latents_device = "cpu" if self.device.type == "mps" else self.device
latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8)
if latents is None:
latents = torch.randn(
latents_shape,
generator=generator,
device=latents_device,
)
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
latents = latents.to(self.device)
# set timesteps
accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
extra_set_kwargs = {}
if accepts_offset:
extra_set_kwargs["offset"] = 1
self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
# if we use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas
if isinstance(self.scheduler, LMSDiscreteScheduler):
latents = latents * self.scheduler.sigmas[0]
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = (
torch.cat([latents] * text_embeddings.shape[0]) if do_classifier_free_guidance else latents
)
if isinstance(self.scheduler, LMSDiscreteScheduler):
sigma = self.scheduler.sigmas[i]
# the model input needs to be scaled to match the continuous ODE formulation in K-LMS
latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
# reduce memory by predicting each score sequentially
noise_preds = []
# predict the noise residual
for latent_in, text_embedding_in in zip(
torch.chunk(latent_model_input, chunks=latent_model_input.shape[0], dim=0),
torch.chunk(text_embeddings, chunks=text_embeddings.shape[0], dim=0),
):
noise_preds.append(self.unet(latent_in, t, encoder_hidden_states=text_embedding_in).sample)
noise_preds = torch.cat(noise_preds, dim=0)
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond = (noise_preds[~mask] * neg_weights).sum(dim=0, keepdims=True)
noise_pred_text = (noise_preds[mask] * pos_weights).sum(dim=0, keepdims=True)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
if isinstance(self.scheduler, LMSDiscreteScheduler):
latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs).prev_sample
else:
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# scale and decode the image latents with vae
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
# run safety checker
safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values)
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| diffusers-ft-main | examples/community/composable_stable_diffusion.py |
import inspect
import time
from pathlib import Path
from typing import Callable, List, Optional, Union
import numpy as np
import torch
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, logging
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def slerp(t, v0, v1, DOT_THRESHOLD=0.9995):
"""helper function to spherically interpolate two arrays v1 v2"""
if not isinstance(v0, np.ndarray):
inputs_are_torch = True
input_device = v0.device
v0 = v0.cpu().numpy()
v1 = v1.cpu().numpy()
dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
if np.abs(dot) > DOT_THRESHOLD:
v2 = (1 - t) * v0 + t * v1
else:
theta_0 = np.arccos(dot)
sin_theta_0 = np.sin(theta_0)
theta_t = theta_0 * t
sin_theta_t = np.sin(theta_t)
s0 = np.sin(theta_0 - theta_t) / sin_theta_0
s1 = sin_theta_t / sin_theta_0
v2 = s0 * v0 + s1 * v1
if inputs_are_torch:
v2 = torch.from_numpy(v2).to(input_device)
return v2
class StableDiffusionWalkPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.
Args:
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(slice_size)
def disable_attention_slicing(self):
r"""
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
back to computing attention in one step.
"""
# set slice_size = `None` to disable `attention slicing`
self.enable_attention_slicing(None)
@torch.no_grad()
def __call__(
self,
prompt: Optional[Union[str, List[str]]] = None,
height: int = 512,
width: int = 512,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
text_embeddings: Optional[torch.FloatTensor] = None,
**kwargs,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*, defaults to `None`):
The prompt or prompts to guide the image generation. If not provided, `text_embeddings` is required.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
text_embeddings (`torch.FloatTensor`, *optional*, defaults to `None`):
Pre-generated text embeddings to be used as inputs for image generation. Can be used in place of
`prompt` to avoid re-computing the embeddings. If not provided, the embeddings will be generated from
the supplied `prompt`.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if text_embeddings is None:
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
print(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
else:
batch_size = text_embeddings.shape[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = self.tokenizer.model_max_length
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_embeddings.shape[1]
uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
latents_dtype = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
self.device
)
else:
latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
latents = latents.to(self.device)
# set timesteps
self.scheduler.set_timesteps(num_inference_steps)
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
timesteps_tensor = self.scheduler.timesteps.to(self.device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
self.device
)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
)
else:
has_nsfw_concept = None
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
def embed_text(self, text):
"""takes in text and turns it into text embeddings"""
text_input = self.tokenizer(
text,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
with torch.no_grad():
embed = self.text_encoder(text_input.input_ids.to(self.device))[0]
return embed
def get_noise(self, seed, dtype=torch.float32, height=512, width=512):
"""Takes in random seed and returns corresponding noise vector"""
return torch.randn(
(1, self.unet.in_channels, height // 8, width // 8),
generator=torch.Generator(device=self.device).manual_seed(seed),
device=self.device,
dtype=dtype,
)
def walk(
self,
prompts: List[str],
seeds: List[int],
num_interpolation_steps: Optional[int] = 6,
output_dir: Optional[str] = "./dreams",
name: Optional[str] = None,
batch_size: Optional[int] = 1,
height: Optional[int] = 512,
width: Optional[int] = 512,
guidance_scale: Optional[float] = 7.5,
num_inference_steps: Optional[int] = 50,
eta: Optional[float] = 0.0,
) -> List[str]:
"""
Walks through a series of prompts and seeds, interpolating between them and saving the results to disk.
Args:
prompts (`List[str]`):
List of prompts to generate images for.
seeds (`List[int]`):
List of seeds corresponding to provided prompts. Must be the same length as prompts.
num_interpolation_steps (`int`, *optional*, defaults to 6):
Number of interpolation steps to take between prompts.
output_dir (`str`, *optional*, defaults to `./dreams`):
Directory to save the generated images to.
name (`str`, *optional*, defaults to `None`):
Subdirectory of `output_dir` to save the generated images to. If `None`, the name will
be the current time.
batch_size (`int`, *optional*, defaults to 1):
Number of images to generate at once.
height (`int`, *optional*, defaults to 512):
Height of the generated images.
width (`int`, *optional*, defaults to 512):
Width of the generated images.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
Returns:
`List[str]`: List of paths to the generated images.
"""
if not len(prompts) == len(seeds):
raise ValueError(
f"Number of prompts and seeds must be equalGot {len(prompts)} prompts and {len(seeds)} seeds"
)
name = name or time.strftime("%Y%m%d-%H%M%S")
save_path = Path(output_dir) / name
save_path.mkdir(exist_ok=True, parents=True)
frame_idx = 0
frame_filepaths = []
for prompt_a, prompt_b, seed_a, seed_b in zip(prompts, prompts[1:], seeds, seeds[1:]):
# Embed Text
embed_a = self.embed_text(prompt_a)
embed_b = self.embed_text(prompt_b)
# Get Noise
noise_dtype = embed_a.dtype
noise_a = self.get_noise(seed_a, noise_dtype, height, width)
noise_b = self.get_noise(seed_b, noise_dtype, height, width)
noise_batch, embeds_batch = None, None
T = np.linspace(0.0, 1.0, num_interpolation_steps)
for i, t in enumerate(T):
noise = slerp(float(t), noise_a, noise_b)
embed = torch.lerp(embed_a, embed_b, t)
noise_batch = noise if noise_batch is None else torch.cat([noise_batch, noise], dim=0)
embeds_batch = embed if embeds_batch is None else torch.cat([embeds_batch, embed], dim=0)
batch_is_ready = embeds_batch.shape[0] == batch_size or i + 1 == T.shape[0]
if batch_is_ready:
outputs = self(
latents=noise_batch,
text_embeddings=embeds_batch,
height=height,
width=width,
guidance_scale=guidance_scale,
eta=eta,
num_inference_steps=num_inference_steps,
)
noise_batch, embeds_batch = None, None
for image in outputs["images"]:
frame_filepath = str(save_path / f"frame_{frame_idx:06d}.png")
image.save(frame_filepath)
frame_filepaths.append(frame_filepath)
frame_idx += 1
return frame_filepaths
| diffusers-ft-main | examples/community/interpolate_stable_diffusion.py |
from typing import Callable, List, Optional, Union
import torch
import PIL
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging
from transformers import (
CLIPFeatureExtractor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class TextInpainting(DiffusionPipeline):
r"""
Pipeline for text based inpainting using Stable Diffusion.
Uses CLIPSeg to get a mask from the given text, then calls the Inpainting pipeline with the generated mask
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
segmentation_model ([`CLIPSegForImageSegmentation`]):
CLIPSeg Model to generate mask from the given text. Please refer to the [model card]() for details.
segmentation_processor ([`CLIPSegProcessor`]):
CLIPSeg processor to get image, text features to translate prompt to English, if necessary. Please refer to the
[model card](https://huggingface.co/docs/transformers/model_doc/clipseg) for details.
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
def __init__(
self,
segmentation_model: CLIPSegForImageSegmentation,
segmentation_processor: CLIPSegProcessor,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "skip_prk_steps") and scheduler.config.skip_prk_steps is False:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration"
" `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
" sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
" Hub, it would be very nice if you could open a Pull request for the"
" `scheduler/scheduler_config.json` file"
)
deprecate("skip_prk_steps not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["skip_prk_steps"] = True
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
self.register_modules(
segmentation_model=segmentation_model,
segmentation_processor=segmentation_processor,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.
Args:
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(slice_size)
def disable_attention_slicing(self):
r"""
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
back to computing attention in one step.
"""
# set slice_size = `None` to disable `attention slicing`
self.enable_attention_slicing(None)
def enable_sequential_cpu_offload(self):
r"""
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
`torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
device = torch.device("cuda")
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
if cpu_offloaded_model is not None:
cpu_offload(cpu_offloaded_model, device)
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def _execution_device(self):
r"""
Returns the device on which the pipeline's models will be executed. After calling
`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
hooks.
"""
if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
return self.device
for module in self.unet.modules():
if (
hasattr(module, "_hf_hook")
and hasattr(module._hf_hook, "execution_device")
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device)
return self.device
def enable_xformers_memory_efficient_attention(self):
r"""
Enable memory efficient attention as implemented in xformers.
When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference
time. Speed up at training time is not guaranteed.
Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention
is used.
"""
self.unet.set_use_memory_efficient_attention_xformers(True)
def disable_xformers_memory_efficient_attention(self):
r"""
Disable memory efficient attention as implemented in xformers.
"""
self.unet.set_use_memory_efficient_attention_xformers(False)
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
image: Union[torch.FloatTensor, PIL.Image.Image],
text: str,
height: int = 512,
width: int = 512,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
**kwargs,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
image (`PIL.Image.Image`):
`Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
be masked out with `mask_image` and repainted according to `prompt`.
text (`str``):
The text to use to generate the mask.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
# We use the input text to generate the mask
inputs = self.segmentation_processor(
text=[text], images=[image], padding="max_length", return_tensors="pt"
).to(self.device)
outputs = self.segmentation_model(**inputs)
mask = torch.sigmoid(outputs.logits).cpu().detach().unsqueeze(-1).numpy()
mask_pil = self.numpy_to_pil(mask)[0].resize(image.size)
# Run inpainting pipeline with the generated mask
inpainting_pipeline = StableDiffusionInpaintPipeline(
vae=self.vae,
text_encoder=self.text_encoder,
tokenizer=self.tokenizer,
unet=self.unet,
scheduler=self.scheduler,
safety_checker=self.safety_checker,
feature_extractor=self.feature_extractor,
)
return inpainting_pipeline(
prompt=prompt,
image=image,
mask_image=mask_pil,
height=height,
width=width,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
eta=eta,
generator=generator,
latents=latents,
output_type=output_type,
return_dict=return_dict,
callback=callback,
callback_steps=callback_steps,
)
| diffusers-ft-main | examples/community/text_inpainting.py |
import d4rl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
config = dict(
n_samples=64,
horizon=32,
num_inference_steps=20,
n_guide_steps=2,
scale_grad_by_std=True,
scale=0.1,
eta=0.0,
t_grad_cutoff=2,
device="cpu",
)
if __name__ == "__main__":
env_name = "hopper-medium-v2"
env = gym.make(env_name)
pipeline = ValueGuidedRLPipeline.from_pretrained(
"bglick13/hopper-medium-v2-value-function-hor32",
env=env,
)
env.seed(0)
obs = env.reset()
total_reward = 0
total_score = 0
T = 1000
rollout = [obs.copy()]
try:
for t in tqdm.tqdm(range(T)):
# call the policy
denorm_actions = pipeline(obs, planning_horizon=32)
# execute action in environment
next_observation, reward, terminal, _ = env.step(denorm_actions)
score = env.get_normalized_score(total_reward)
# update return
total_reward += reward
total_score += score
print(
f"Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"
f" {total_score}"
)
# save observations for rendering
rollout.append(next_observation.copy())
obs = next_observation
except KeyboardInterrupt:
pass
print(f"Total reward: {total_reward}")
| diffusers-ft-main | examples/rl/run_diffuser_locomotion.py |
import d4rl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
config = dict(
n_samples=64,
horizon=32,
num_inference_steps=20,
n_guide_steps=0,
scale_grad_by_std=True,
scale=0.1,
eta=0.0,
t_grad_cutoff=2,
device="cpu",
)
if __name__ == "__main__":
env_name = "hopper-medium-v2"
env = gym.make(env_name)
pipeline = ValueGuidedRLPipeline.from_pretrained(
"bglick13/hopper-medium-v2-value-function-hor32",
env=env,
)
env.seed(0)
obs = env.reset()
total_reward = 0
total_score = 0
T = 1000
rollout = [obs.copy()]
try:
for t in tqdm.tqdm(range(T)):
# Call the policy
denorm_actions = pipeline(obs, planning_horizon=32)
# execute action in environment
next_observation, reward, terminal, _ = env.step(denorm_actions)
score = env.get_normalized_score(total_reward)
# update return
total_reward += reward
total_score += score
print(
f"Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"
f" {total_score}"
)
# save observations for rendering
rollout.append(next_observation.copy())
obs = next_observation
except KeyboardInterrupt:
pass
print(f"Total reward: {total_reward}")
| diffusers-ft-main | examples/rl/run_diffuser_gen_trajectories.py |
import argparse
import torch
import OmegaConf
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def convert_ldm_original(checkpoint_path, config_path, output_path):
config = OmegaConf.load(config_path)
state_dict = torch.load(checkpoint_path, map_location="cpu")["model"]
keys = list(state_dict.keys())
# extract state_dict for VQVAE
first_stage_dict = {}
first_stage_key = "first_stage_model."
for key in keys:
if key.startswith(first_stage_key):
first_stage_dict[key.replace(first_stage_key, "")] = state_dict[key]
# extract state_dict for UNetLDM
unet_state_dict = {}
unet_key = "model.diffusion_model."
for key in keys:
if key.startswith(unet_key):
unet_state_dict[key.replace(unet_key, "")] = state_dict[key]
vqvae_init_args = config.model.params.first_stage_config.params
unet_init_args = config.model.params.unet_config.params
vqvae = VQModel(**vqvae_init_args).eval()
vqvae.load_state_dict(first_stage_dict)
unet = UNetLDMModel(**unet_init_args).eval()
unet.load_state_dict(unet_state_dict)
noise_scheduler = DDIMScheduler(
timesteps=config.model.params.timesteps,
beta_schedule="scaled_linear",
beta_start=config.model.params.linear_start,
beta_end=config.model.params.linear_end,
clip_sample=False,
)
pipeline = LDMPipeline(vqvae, unet, noise_scheduler)
pipeline.save_pretrained(output_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint_path", type=str, required=True)
parser.add_argument("--config_path", type=str, required=True)
parser.add_argument("--output_path", type=str, required=True)
args = parser.parse_args()
convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
| diffusers-ft-main | scripts/conversion_ldm_uncond.py |
# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
# *Only* converts the UNet, VAE, and Text Encoder.
# Does not convert optimizer state or any other thing.
import argparse
import os.path as osp
import torch
# =================#
# UNet Conversion #
# =================#
unet_conversion_map = [
# (stable-diffusion, HF Diffusers)
("time_embed.0.weight", "time_embedding.linear_1.weight"),
("time_embed.0.bias", "time_embedding.linear_1.bias"),
("time_embed.2.weight", "time_embedding.linear_2.weight"),
("time_embed.2.bias", "time_embedding.linear_2.bias"),
("input_blocks.0.0.weight", "conv_in.weight"),
("input_blocks.0.0.bias", "conv_in.bias"),
("out.0.weight", "conv_norm_out.weight"),
("out.0.bias", "conv_norm_out.bias"),
("out.2.weight", "conv_out.weight"),
("out.2.bias", "conv_out.bias"),
]
unet_conversion_map_resnet = [
# (stable-diffusion, HF Diffusers)
("in_layers.0", "norm1"),
("in_layers.2", "conv1"),
("out_layers.0", "norm2"),
("out_layers.3", "conv2"),
("emb_layers.1", "time_emb_proj"),
("skip_connection", "conv_shortcut"),
]
unet_conversion_map_layer = []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
# loop over downblocks/upblocks
for j in range(2):
# loop over resnets/attentions for downblocks
hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
if i < 3:
# no attention layers in down_blocks.3
hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
for j in range(3):
# loop over resnets/attentions for upblocks
hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
if i > 0:
# no attention layers in up_blocks.0
hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
if i < 3:
# no downsample in down_blocks.3
hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
# no upsample in up_blocks.3
hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
hf_mid_atn_prefix = "mid_block.attentions.0."
sd_mid_atn_prefix = "middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
for j in range(2):
hf_mid_res_prefix = f"mid_block.resnets.{j}."
sd_mid_res_prefix = f"middle_block.{2*j}."
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def convert_unet_state_dict(unet_state_dict):
# buyer beware: this is a *brittle* function,
# and correct output requires that all of these pieces interact in
# the exact order in which I have arranged them.
mapping = {k: k for k in unet_state_dict.keys()}
for sd_name, hf_name in unet_conversion_map:
mapping[hf_name] = sd_name
for k, v in mapping.items():
if "resnets" in k:
for sd_part, hf_part in unet_conversion_map_resnet:
v = v.replace(hf_part, sd_part)
mapping[k] = v
for k, v in mapping.items():
for sd_part, hf_part in unet_conversion_map_layer:
v = v.replace(hf_part, sd_part)
mapping[k] = v
new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
return new_state_dict
# ================#
# VAE Conversion #
# ================#
vae_conversion_map = [
# (stable-diffusion, HF Diffusers)
("nin_shortcut", "conv_shortcut"),
("norm_out", "conv_norm_out"),
("mid.attn_1.", "mid_block.attentions.0."),
]
for i in range(4):
# down_blocks have two resnets
for j in range(2):
hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
sd_down_prefix = f"encoder.down.{i}.block.{j}."
vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
if i < 3:
hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
sd_downsample_prefix = f"down.{i}.downsample."
vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
sd_upsample_prefix = f"up.{3-i}.upsample."
vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
# up_blocks have three resnets
# also, up blocks in hf are numbered in reverse from sd
for j in range(3):
hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
sd_up_prefix = f"decoder.up.{3-i}.block.{j}."
vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
hf_mid_res_prefix = f"mid_block.resnets.{i}."
sd_mid_res_prefix = f"mid.block_{i+1}."
vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
vae_conversion_map_attn = [
# (stable-diffusion, HF Diffusers)
("norm.", "group_norm."),
("q.", "query."),
("k.", "key."),
("v.", "value."),
("proj_out.", "proj_attn."),
]
def reshape_weight_for_sd(w):
# convert HF linear weights to SD conv2d weights
return w.reshape(*w.shape, 1, 1)
def convert_vae_state_dict(vae_state_dict):
mapping = {k: k for k in vae_state_dict.keys()}
for k, v in mapping.items():
for sd_part, hf_part in vae_conversion_map:
v = v.replace(hf_part, sd_part)
mapping[k] = v
for k, v in mapping.items():
if "attentions" in k:
for sd_part, hf_part in vae_conversion_map_attn:
v = v.replace(hf_part, sd_part)
mapping[k] = v
new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
weights_to_convert = ["q", "k", "v", "proj_out"]
for k, v in new_state_dict.items():
for weight_name in weights_to_convert:
if f"mid.attn_1.{weight_name}.weight" in k:
print(f"Reshaping {k} for SD format")
new_state_dict[k] = reshape_weight_for_sd(v)
return new_state_dict
# =========================#
# Text Encoder Conversion #
# =========================#
# pretty much a no-op
def convert_text_enc_state_dict(text_enc_dict):
return text_enc_dict
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
args = parser.parse_args()
assert args.model_path is not None, "Must provide a model path!"
assert args.checkpoint_path is not None, "Must provide a checkpoint path!"
unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
text_enc_path = osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
# Convert the UNet model
unet_state_dict = torch.load(unet_path, map_location="cpu")
unet_state_dict = convert_unet_state_dict(unet_state_dict)
unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}
# Convert the VAE model
vae_state_dict = torch.load(vae_path, map_location="cpu")
vae_state_dict = convert_vae_state_dict(vae_state_dict)
vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()}
# Convert the text encoder model
text_enc_dict = torch.load(text_enc_path, map_location="cpu")
text_enc_dict = convert_text_enc_state_dict(text_enc_dict)
text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}
# Put together new checkpoint
state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
if args.half:
state_dict = {k: v.half() for k, v in state_dict.items()}
state_dict = {"state_dict": state_dict}
torch.save(state_dict, args.checkpoint_path)
| diffusers-ft-main | scripts/convert_diffusers_to_original_stable_diffusion.py |
diffusers-ft-main | scripts/__init__.py |
|
import argparse
import json
import torch
from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNet2DModel, VQModel
def shave_segments(path, n_shave_prefix_segments=1):
"""
Removes segments. Positive values shave the first segments, negative shave the last segments.
"""
if n_shave_prefix_segments >= 0:
return ".".join(path.split(".")[n_shave_prefix_segments:])
else:
return ".".join(path.split(".")[:n_shave_prefix_segments])
def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
mapping = []
for old_item in old_list:
new_item = old_item
new_item = new_item.replace("block.", "resnets.")
new_item = new_item.replace("conv_shorcut", "conv1")
new_item = new_item.replace("in_shortcut", "conv_shortcut")
new_item = new_item.replace("temb_proj", "time_emb_proj")
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def renew_attention_paths(old_list, n_shave_prefix_segments=0, in_mid=False):
mapping = []
for old_item in old_list:
new_item = old_item
# In `model.mid`, the layer is called `attn`.
if not in_mid:
new_item = new_item.replace("attn", "attentions")
new_item = new_item.replace(".k.", ".key.")
new_item = new_item.replace(".v.", ".value.")
new_item = new_item.replace(".q.", ".query.")
new_item = new_item.replace("proj_out", "proj_attn")
new_item = new_item.replace("norm", "group_norm")
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def assign_to_checkpoint(
paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None
):
assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
if attention_paths_to_split is not None:
if config is None:
raise ValueError("Please specify the config if setting 'attention_paths_to_split' to 'True'.")
for path, path_map in attention_paths_to_split.items():
old_tensor = old_checkpoint[path]
channels = old_tensor.shape[0] // 3
target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)
num_heads = old_tensor.shape[0] // config.get("num_head_channels", 1) // 3
old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
query, key, value = old_tensor.split(channels // num_heads, dim=1)
checkpoint[path_map["query"]] = query.reshape(target_shape).squeeze()
checkpoint[path_map["key"]] = key.reshape(target_shape).squeeze()
checkpoint[path_map["value"]] = value.reshape(target_shape).squeeze()
for path in paths:
new_path = path["new"]
if attention_paths_to_split is not None and new_path in attention_paths_to_split:
continue
new_path = new_path.replace("down.", "down_blocks.")
new_path = new_path.replace("up.", "up_blocks.")
if additional_replacements is not None:
for replacement in additional_replacements:
new_path = new_path.replace(replacement["old"], replacement["new"])
if "attentions" in new_path:
checkpoint[new_path] = old_checkpoint[path["old"]].squeeze()
else:
checkpoint[new_path] = old_checkpoint[path["old"]]
def convert_ddpm_checkpoint(checkpoint, config):
"""
Takes a state dict and a config, and returns a converted checkpoint.
"""
new_checkpoint = {}
new_checkpoint["time_embedding.linear_1.weight"] = checkpoint["temb.dense.0.weight"]
new_checkpoint["time_embedding.linear_1.bias"] = checkpoint["temb.dense.0.bias"]
new_checkpoint["time_embedding.linear_2.weight"] = checkpoint["temb.dense.1.weight"]
new_checkpoint["time_embedding.linear_2.bias"] = checkpoint["temb.dense.1.bias"]
new_checkpoint["conv_norm_out.weight"] = checkpoint["norm_out.weight"]
new_checkpoint["conv_norm_out.bias"] = checkpoint["norm_out.bias"]
new_checkpoint["conv_in.weight"] = checkpoint["conv_in.weight"]
new_checkpoint["conv_in.bias"] = checkpoint["conv_in.bias"]
new_checkpoint["conv_out.weight"] = checkpoint["conv_out.weight"]
new_checkpoint["conv_out.bias"] = checkpoint["conv_out.bias"]
num_down_blocks = len({".".join(layer.split(".")[:2]) for layer in checkpoint if "down" in layer})
down_blocks = {
layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
}
num_up_blocks = len({".".join(layer.split(".")[:2]) for layer in checkpoint if "up" in layer})
up_blocks = {layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)}
for i in range(num_down_blocks):
block_id = (i - 1) // (config["layers_per_block"] + 1)
if any("downsample" in layer for layer in down_blocks[i]):
new_checkpoint[f"down_blocks.{i}.downsamplers.0.conv.weight"] = checkpoint[
f"down.{i}.downsample.op.weight"
]
new_checkpoint[f"down_blocks.{i}.downsamplers.0.conv.bias"] = checkpoint[f"down.{i}.downsample.op.bias"]
# new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
# new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']
if any("block" in layer for layer in down_blocks[i]):
num_blocks = len(
{".".join(shave_segments(layer, 2).split(".")[:2]) for layer in down_blocks[i] if "block" in layer}
)
blocks = {
layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
for layer_id in range(num_blocks)
}
if num_blocks > 0:
for j in range(config["layers_per_block"]):
paths = renew_resnet_paths(blocks[j])
assign_to_checkpoint(paths, new_checkpoint, checkpoint)
if any("attn" in layer for layer in down_blocks[i]):
num_attn = len(
{".".join(shave_segments(layer, 2).split(".")[:2]) for layer in down_blocks[i] if "attn" in layer}
)
attns = {
layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
for layer_id in range(num_blocks)
}
if num_attn > 0:
for j in range(config["layers_per_block"]):
paths = renew_attention_paths(attns[j])
assign_to_checkpoint(paths, new_checkpoint, checkpoint, config=config)
mid_block_1_layers = [key for key in checkpoint if "mid.block_1" in key]
mid_block_2_layers = [key for key in checkpoint if "mid.block_2" in key]
mid_attn_1_layers = [key for key in checkpoint if "mid.attn_1" in key]
# Mid new 2
paths = renew_resnet_paths(mid_block_1_layers)
assign_to_checkpoint(
paths,
new_checkpoint,
checkpoint,
additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}],
)
paths = renew_resnet_paths(mid_block_2_layers)
assign_to_checkpoint(
paths,
new_checkpoint,
checkpoint,
additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}],
)
paths = renew_attention_paths(mid_attn_1_layers, in_mid=True)
assign_to_checkpoint(
paths,
new_checkpoint,
checkpoint,
additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}],
)
for i in range(num_up_blocks):
block_id = num_up_blocks - 1 - i
if any("upsample" in layer for layer in up_blocks[i]):
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = checkpoint[
f"up.{i}.upsample.conv.weight"
]
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = checkpoint[f"up.{i}.upsample.conv.bias"]
if any("block" in layer for layer in up_blocks[i]):
num_blocks = len(
{".".join(shave_segments(layer, 2).split(".")[:2]) for layer in up_blocks[i] if "block" in layer}
)
blocks = {
layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(num_blocks)
}
if num_blocks > 0:
for j in range(config["layers_per_block"] + 1):
replace_indices = {"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"}
paths = renew_resnet_paths(blocks[j])
assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices])
if any("attn" in layer for layer in up_blocks[i]):
num_attn = len(
{".".join(shave_segments(layer, 2).split(".")[:2]) for layer in up_blocks[i] if "attn" in layer}
)
attns = {
layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(num_blocks)
}
if num_attn > 0:
for j in range(config["layers_per_block"] + 1):
replace_indices = {"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"}
paths = renew_attention_paths(attns[j])
assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices])
new_checkpoint = {k.replace("mid_new_2", "mid_block"): v for k, v in new_checkpoint.items()}
return new_checkpoint
def convert_vq_autoenc_checkpoint(checkpoint, config):
"""
Takes a state dict and a config, and returns a converted checkpoint.
"""
new_checkpoint = {}
new_checkpoint["encoder.conv_norm_out.weight"] = checkpoint["encoder.norm_out.weight"]
new_checkpoint["encoder.conv_norm_out.bias"] = checkpoint["encoder.norm_out.bias"]
new_checkpoint["encoder.conv_in.weight"] = checkpoint["encoder.conv_in.weight"]
new_checkpoint["encoder.conv_in.bias"] = checkpoint["encoder.conv_in.bias"]
new_checkpoint["encoder.conv_out.weight"] = checkpoint["encoder.conv_out.weight"]
new_checkpoint["encoder.conv_out.bias"] = checkpoint["encoder.conv_out.bias"]
new_checkpoint["decoder.conv_norm_out.weight"] = checkpoint["decoder.norm_out.weight"]
new_checkpoint["decoder.conv_norm_out.bias"] = checkpoint["decoder.norm_out.bias"]
new_checkpoint["decoder.conv_in.weight"] = checkpoint["decoder.conv_in.weight"]
new_checkpoint["decoder.conv_in.bias"] = checkpoint["decoder.conv_in.bias"]
new_checkpoint["decoder.conv_out.weight"] = checkpoint["decoder.conv_out.weight"]
new_checkpoint["decoder.conv_out.bias"] = checkpoint["decoder.conv_out.bias"]
num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in checkpoint if "down" in layer})
down_blocks = {
layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
}
num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in checkpoint if "up" in layer})
up_blocks = {layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)}
for i in range(num_down_blocks):
block_id = (i - 1) // (config["layers_per_block"] + 1)
if any("downsample" in layer for layer in down_blocks[i]):
new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = checkpoint[
f"encoder.down.{i}.downsample.conv.weight"
]
new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = checkpoint[
f"encoder.down.{i}.downsample.conv.bias"
]
if any("block" in layer for layer in down_blocks[i]):
num_blocks = len(
{".".join(shave_segments(layer, 3).split(".")[:3]) for layer in down_blocks[i] if "block" in layer}
)
blocks = {
layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
for layer_id in range(num_blocks)
}
if num_blocks > 0:
for j in range(config["layers_per_block"]):
paths = renew_resnet_paths(blocks[j])
assign_to_checkpoint(paths, new_checkpoint, checkpoint)
if any("attn" in layer for layer in down_blocks[i]):
num_attn = len(
{".".join(shave_segments(layer, 3).split(".")[:3]) for layer in down_blocks[i] if "attn" in layer}
)
attns = {
layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
for layer_id in range(num_blocks)
}
if num_attn > 0:
for j in range(config["layers_per_block"]):
paths = renew_attention_paths(attns[j])
assign_to_checkpoint(paths, new_checkpoint, checkpoint, config=config)
mid_block_1_layers = [key for key in checkpoint if "mid.block_1" in key]
mid_block_2_layers = [key for key in checkpoint if "mid.block_2" in key]
mid_attn_1_layers = [key for key in checkpoint if "mid.attn_1" in key]
# Mid new 2
paths = renew_resnet_paths(mid_block_1_layers)
assign_to_checkpoint(
paths,
new_checkpoint,
checkpoint,
additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}],
)
paths = renew_resnet_paths(mid_block_2_layers)
assign_to_checkpoint(
paths,
new_checkpoint,
checkpoint,
additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}],
)
paths = renew_attention_paths(mid_attn_1_layers, in_mid=True)
assign_to_checkpoint(
paths,
new_checkpoint,
checkpoint,
additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}],
)
for i in range(num_up_blocks):
block_id = num_up_blocks - 1 - i
if any("upsample" in layer for layer in up_blocks[i]):
new_checkpoint[f"decoder.up_blocks.{block_id}.upsamplers.0.conv.weight"] = checkpoint[
f"decoder.up.{i}.upsample.conv.weight"
]
new_checkpoint[f"decoder.up_blocks.{block_id}.upsamplers.0.conv.bias"] = checkpoint[
f"decoder.up.{i}.upsample.conv.bias"
]
if any("block" in layer for layer in up_blocks[i]):
num_blocks = len(
{".".join(shave_segments(layer, 3).split(".")[:3]) for layer in up_blocks[i] if "block" in layer}
)
blocks = {
layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(num_blocks)
}
if num_blocks > 0:
for j in range(config["layers_per_block"] + 1):
replace_indices = {"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"}
paths = renew_resnet_paths(blocks[j])
assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices])
if any("attn" in layer for layer in up_blocks[i]):
num_attn = len(
{".".join(shave_segments(layer, 3).split(".")[:3]) for layer in up_blocks[i] if "attn" in layer}
)
attns = {
layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(num_blocks)
}
if num_attn > 0:
for j in range(config["layers_per_block"] + 1):
replace_indices = {"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"}
paths = renew_attention_paths(attns[j])
assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices])
new_checkpoint = {k.replace("mid_new_2", "mid_block"): v for k, v in new_checkpoint.items()}
new_checkpoint["quant_conv.weight"] = checkpoint["quant_conv.weight"]
new_checkpoint["quant_conv.bias"] = checkpoint["quant_conv.bias"]
if "quantize.embedding.weight" in checkpoint:
new_checkpoint["quantize.embedding.weight"] = checkpoint["quantize.embedding.weight"]
new_checkpoint["post_quant_conv.weight"] = checkpoint["post_quant_conv.weight"]
new_checkpoint["post_quant_conv.bias"] = checkpoint["post_quant_conv.bias"]
return new_checkpoint
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the architecture.",
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
args = parser.parse_args()
checkpoint = torch.load(args.checkpoint_path)
with open(args.config_file) as f:
config = json.loads(f.read())
# unet case
key_prefix_set = set(key.split(".")[0] for key in checkpoint.keys())
if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
converted_checkpoint = convert_vq_autoenc_checkpoint(checkpoint, config)
else:
converted_checkpoint = convert_ddpm_checkpoint(checkpoint, config)
if "ddpm" in config:
del config["ddpm"]
if config["_class_name"] == "VQModel":
model = VQModel(**config)
model.load_state_dict(converted_checkpoint)
model.save_pretrained(args.dump_path)
elif config["_class_name"] == "AutoencoderKL":
model = AutoencoderKL(**config)
model.load_state_dict(converted_checkpoint)
model.save_pretrained(args.dump_path)
else:
model = UNet2DModel(**config)
model.load_state_dict(converted_checkpoint)
scheduler = DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
pipe = DDPMPipeline(unet=model, scheduler=scheduler)
pipe.save_pretrained(args.dump_path)
| diffusers-ft-main | scripts/convert_ddpm_original_checkpoint_to_diffusers.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Conversion script for the LDM checkpoints. """
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNet2DModel, VQModel
def shave_segments(path, n_shave_prefix_segments=1):
"""
Removes segments. Positive values shave the first segments, negative shave the last segments.
"""
if n_shave_prefix_segments >= 0:
return ".".join(path.split(".")[n_shave_prefix_segments:])
else:
return ".".join(path.split(".")[:n_shave_prefix_segments])
def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside resnets to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item.replace("in_layers.0", "norm1")
new_item = new_item.replace("in_layers.2", "conv1")
new_item = new_item.replace("out_layers.0", "norm2")
new_item = new_item.replace("out_layers.3", "conv2")
new_item = new_item.replace("emb_layers.1", "time_emb_proj")
new_item = new_item.replace("skip_connection", "conv_shortcut")
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def renew_attention_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside attentions to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item
new_item = new_item.replace("norm.weight", "group_norm.weight")
new_item = new_item.replace("norm.bias", "group_norm.bias")
new_item = new_item.replace("proj_out.weight", "proj_attn.weight")
new_item = new_item.replace("proj_out.bias", "proj_attn.bias")
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def assign_to_checkpoint(
paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None
):
"""
This does the final conversion step: take locally converted weights and apply a global renaming
to them. It splits attention layers, and takes into account additional replacements
that may arise.
Assigns the weights to the new checkpoint.
"""
assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
# Splits the attention layers into three variables.
if attention_paths_to_split is not None:
for path, path_map in attention_paths_to_split.items():
old_tensor = old_checkpoint[path]
channels = old_tensor.shape[0] // 3
target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)
num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
query, key, value = old_tensor.split(channels // num_heads, dim=1)
checkpoint[path_map["query"]] = query.reshape(target_shape)
checkpoint[path_map["key"]] = key.reshape(target_shape)
checkpoint[path_map["value"]] = value.reshape(target_shape)
for path in paths:
new_path = path["new"]
# These have already been assigned
if attention_paths_to_split is not None and new_path in attention_paths_to_split:
continue
# Global renaming happens here
new_path = new_path.replace("middle_block.0", "mid_block.resnets.0")
new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")
new_path = new_path.replace("middle_block.2", "mid_block.resnets.1")
if additional_replacements is not None:
for replacement in additional_replacements:
new_path = new_path.replace(replacement["old"], replacement["new"])
# proj_attn.weight has to be converted from conv 1D to linear
if "proj_attn.weight" in new_path:
checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]
else:
checkpoint[new_path] = old_checkpoint[path["old"]]
def convert_ldm_checkpoint(checkpoint, config):
"""
Takes a state dict and a config, and returns a converted checkpoint.
"""
new_checkpoint = {}
new_checkpoint["time_embedding.linear_1.weight"] = checkpoint["time_embed.0.weight"]
new_checkpoint["time_embedding.linear_1.bias"] = checkpoint["time_embed.0.bias"]
new_checkpoint["time_embedding.linear_2.weight"] = checkpoint["time_embed.2.weight"]
new_checkpoint["time_embedding.linear_2.bias"] = checkpoint["time_embed.2.bias"]
new_checkpoint["conv_in.weight"] = checkpoint["input_blocks.0.0.weight"]
new_checkpoint["conv_in.bias"] = checkpoint["input_blocks.0.0.bias"]
new_checkpoint["conv_norm_out.weight"] = checkpoint["out.0.weight"]
new_checkpoint["conv_norm_out.bias"] = checkpoint["out.0.bias"]
new_checkpoint["conv_out.weight"] = checkpoint["out.2.weight"]
new_checkpoint["conv_out.bias"] = checkpoint["out.2.bias"]
# Retrieves the keys for the input blocks only
num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in checkpoint if "input_blocks" in layer})
input_blocks = {
layer_id: [key for key in checkpoint if f"input_blocks.{layer_id}" in key]
for layer_id in range(num_input_blocks)
}
# Retrieves the keys for the middle blocks only
num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in checkpoint if "middle_block" in layer})
middle_blocks = {
layer_id: [key for key in checkpoint if f"middle_block.{layer_id}" in key]
for layer_id in range(num_middle_blocks)
}
# Retrieves the keys for the output blocks only
num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in checkpoint if "output_blocks" in layer})
output_blocks = {
layer_id: [key for key in checkpoint if f"output_blocks.{layer_id}" in key]
for layer_id in range(num_output_blocks)
}
for i in range(1, num_input_blocks):
block_id = (i - 1) // (config["num_res_blocks"] + 1)
layer_in_block_id = (i - 1) % (config["num_res_blocks"] + 1)
resnets = [key for key in input_blocks[i] if f"input_blocks.{i}.0" in key]
attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
if f"input_blocks.{i}.0.op.weight" in checkpoint:
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = checkpoint[
f"input_blocks.{i}.0.op.weight"
]
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = checkpoint[
f"input_blocks.{i}.0.op.bias"
]
continue
paths = renew_resnet_paths(resnets)
meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
resnet_op = {"old": "resnets.2.op", "new": "downsamplers.0.op"}
assign_to_checkpoint(
paths, new_checkpoint, checkpoint, additional_replacements=[meta_path, resnet_op], config=config
)
if len(attentions):
paths = renew_attention_paths(attentions)
meta_path = {
"old": f"input_blocks.{i}.1",
"new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}",
}
to_split = {
f"input_blocks.{i}.1.qkv.bias": {
"key": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
"query": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
"value": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
},
f"input_blocks.{i}.1.qkv.weight": {
"key": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
"query": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
"value": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
},
}
assign_to_checkpoint(
paths,
new_checkpoint,
checkpoint,
additional_replacements=[meta_path],
attention_paths_to_split=to_split,
config=config,
)
resnet_0 = middle_blocks[0]
attentions = middle_blocks[1]
resnet_1 = middle_blocks[2]
resnet_0_paths = renew_resnet_paths(resnet_0)
assign_to_checkpoint(resnet_0_paths, new_checkpoint, checkpoint, config=config)
resnet_1_paths = renew_resnet_paths(resnet_1)
assign_to_checkpoint(resnet_1_paths, new_checkpoint, checkpoint, config=config)
attentions_paths = renew_attention_paths(attentions)
to_split = {
"middle_block.1.qkv.bias": {
"key": "mid_block.attentions.0.key.bias",
"query": "mid_block.attentions.0.query.bias",
"value": "mid_block.attentions.0.value.bias",
},
"middle_block.1.qkv.weight": {
"key": "mid_block.attentions.0.key.weight",
"query": "mid_block.attentions.0.query.weight",
"value": "mid_block.attentions.0.value.weight",
},
}
assign_to_checkpoint(
attentions_paths, new_checkpoint, checkpoint, attention_paths_to_split=to_split, config=config
)
for i in range(num_output_blocks):
block_id = i // (config["num_res_blocks"] + 1)
layer_in_block_id = i % (config["num_res_blocks"] + 1)
output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
output_block_list = {}
for layer in output_block_layers:
layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)
if layer_id in output_block_list:
output_block_list[layer_id].append(layer_name)
else:
output_block_list[layer_id] = [layer_name]
if len(output_block_list) > 1:
resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
resnet_0_paths = renew_resnet_paths(resnets)
paths = renew_resnet_paths(resnets)
meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[meta_path], config=config)
if ["conv.weight", "conv.bias"] in output_block_list.values():
index = list(output_block_list.values()).index(["conv.weight", "conv.bias"])
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = checkpoint[
f"output_blocks.{i}.{index}.conv.weight"
]
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = checkpoint[
f"output_blocks.{i}.{index}.conv.bias"
]
# Clear attentions as they have been attributed above.
if len(attentions) == 2:
attentions = []
if len(attentions):
paths = renew_attention_paths(attentions)
meta_path = {
"old": f"output_blocks.{i}.1",
"new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
}
to_split = {
f"output_blocks.{i}.1.qkv.bias": {
"key": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
"query": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
"value": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
},
f"output_blocks.{i}.1.qkv.weight": {
"key": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
"query": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
"value": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
},
}
assign_to_checkpoint(
paths,
new_checkpoint,
checkpoint,
additional_replacements=[meta_path],
attention_paths_to_split=to_split if any("qkv" in key for key in attentions) else None,
config=config,
)
else:
resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)
for path in resnet_0_paths:
old_path = ".".join(["output_blocks", str(i), path["old"]])
new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]])
new_checkpoint[new_path] = checkpoint[old_path]
return new_checkpoint
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the architecture.",
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
args = parser.parse_args()
checkpoint = torch.load(args.checkpoint_path)
with open(args.config_file) as f:
config = json.loads(f.read())
converted_checkpoint = convert_ldm_checkpoint(checkpoint, config)
if "ldm" in config:
del config["ldm"]
model = UNet2DModel(**config)
model.load_state_dict(converted_checkpoint)
try:
scheduler = DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
vqvae = VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))
pipe = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
pipe.save_pretrained(args.dump_path)
except:
model.save_pretrained(args.dump_path)
| diffusers-ft-main | scripts/convert_ldm_original_checkpoint_to_diffusers.py |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import shutil
from pathlib import Path
import torch
from torch.onnx import export
import onnx
from diffusers import OnnxStableDiffusionPipeline, StableDiffusionPipeline
from diffusers.onnx_utils import OnnxRuntimeModel
from packaging import version
is_torch_less_than_1_11 = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def onnx_export(
model,
model_args: tuple,
output_path: Path,
ordered_input_names,
output_names,
dynamic_axes,
opset,
use_external_data_format=False,
):
output_path.parent.mkdir(parents=True, exist_ok=True)
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
model,
model_args,
f=output_path.as_posix(),
input_names=ordered_input_names,
output_names=output_names,
dynamic_axes=dynamic_axes,
do_constant_folding=True,
use_external_data_format=use_external_data_format,
enable_onnx_checker=True,
opset_version=opset,
)
else:
export(
model,
model_args,
f=output_path.as_posix(),
input_names=ordered_input_names,
output_names=output_names,
dynamic_axes=dynamic_axes,
do_constant_folding=True,
opset_version=opset,
)
@torch.no_grad()
def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = False):
dtype = torch.float16 if fp16 else torch.float32
if fp16 and torch.cuda.is_available():
device = "cuda"
elif fp16 and not torch.cuda.is_available():
raise ValueError("`float16` model export is only supported on GPUs with CUDA")
else:
device = "cpu"
pipeline = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=dtype).to(device)
output_path = Path(output_path)
# TEXT ENCODER
num_tokens = pipeline.text_encoder.config.max_position_embeddings
text_hidden_size = pipeline.text_encoder.config.hidden_size
text_input = pipeline.tokenizer(
"A sample prompt",
padding="max_length",
max_length=pipeline.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
onnx_export(
pipeline.text_encoder,
# casting to torch.int32 until the CLIP fix is released: https://github.com/huggingface/transformers/pull/18515/files
model_args=(text_input.input_ids.to(device=device, dtype=torch.int32)),
output_path=output_path / "text_encoder" / "model.onnx",
ordered_input_names=["input_ids"],
output_names=["last_hidden_state", "pooler_output"],
dynamic_axes={
"input_ids": {0: "batch", 1: "sequence"},
},
opset=opset,
)
del pipeline.text_encoder
# UNET
unet_in_channels = pipeline.unet.config.in_channels
unet_sample_size = pipeline.unet.config.sample_size
unet_path = output_path / "unet" / "model.onnx"
onnx_export(
pipeline.unet,
model_args=(
torch.randn(2, unet_in_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype),
torch.randn(2).to(device=device, dtype=dtype),
torch.randn(2, num_tokens, text_hidden_size).to(device=device, dtype=dtype),
False,
),
output_path=unet_path,
ordered_input_names=["sample", "timestep", "encoder_hidden_states", "return_dict"],
output_names=["out_sample"], # has to be different from "sample" for correct tracing
dynamic_axes={
"sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
"timestep": {0: "batch"},
"encoder_hidden_states": {0: "batch", 1: "sequence"},
},
opset=opset,
use_external_data_format=True, # UNet is > 2GB, so the weights need to be split
)
unet_model_path = str(unet_path.absolute().as_posix())
unet_dir = os.path.dirname(unet_model_path)
unet = onnx.load(unet_model_path)
# clean up existing tensor files
shutil.rmtree(unet_dir)
os.mkdir(unet_dir)
# collate external tensor files into one
onnx.save_model(
unet,
unet_model_path,
save_as_external_data=True,
all_tensors_to_one_file=True,
location="weights.pb",
convert_attribute=False,
)
del pipeline.unet
# VAE ENCODER
vae_encoder = pipeline.vae
vae_in_channels = vae_encoder.config.in_channels
vae_sample_size = vae_encoder.config.sample_size
# need to get the raw tensor output (sample) from the encoder
vae_encoder.forward = lambda sample, return_dict: vae_encoder.encode(sample, return_dict)[0].sample()
onnx_export(
vae_encoder,
model_args=(
torch.randn(1, vae_in_channels, vae_sample_size, vae_sample_size).to(device=device, dtype=dtype),
False,
),
output_path=output_path / "vae_encoder" / "model.onnx",
ordered_input_names=["sample", "return_dict"],
output_names=["latent_sample"],
dynamic_axes={
"sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
},
opset=opset,
)
# VAE DECODER
vae_decoder = pipeline.vae
vae_latent_channels = vae_decoder.config.latent_channels
vae_out_channels = vae_decoder.config.out_channels
# forward only through the decoder part
vae_decoder.forward = vae_encoder.decode
onnx_export(
vae_decoder,
model_args=(
torch.randn(1, vae_latent_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype),
False,
),
output_path=output_path / "vae_decoder" / "model.onnx",
ordered_input_names=["latent_sample", "return_dict"],
output_names=["sample"],
dynamic_axes={
"latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
},
opset=opset,
)
del pipeline.vae
# SAFETY CHECKER
if pipeline.safety_checker is not None:
safety_checker = pipeline.safety_checker
clip_num_channels = safety_checker.config.vision_config.num_channels
clip_image_size = safety_checker.config.vision_config.image_size
safety_checker.forward = safety_checker.forward_onnx
onnx_export(
pipeline.safety_checker,
model_args=(
torch.randn(
1,
clip_num_channels,
clip_image_size,
clip_image_size,
).to(device=device, dtype=dtype),
torch.randn(1, vae_sample_size, vae_sample_size, vae_out_channels).to(device=device, dtype=dtype),
),
output_path=output_path / "safety_checker" / "model.onnx",
ordered_input_names=["clip_input", "images"],
output_names=["out_images", "has_nsfw_concepts"],
dynamic_axes={
"clip_input": {0: "batch", 1: "channels", 2: "height", 3: "width"},
"images": {0: "batch", 1: "height", 2: "width", 3: "channels"},
},
opset=opset,
)
del pipeline.safety_checker
safety_checker = OnnxRuntimeModel.from_pretrained(output_path / "safety_checker")
feature_extractor = pipeline.feature_extractor
else:
safety_checker = None
feature_extractor = None
onnx_pipeline = OnnxStableDiffusionPipeline(
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_encoder"),
vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_decoder"),
text_encoder=OnnxRuntimeModel.from_pretrained(output_path / "text_encoder"),
tokenizer=pipeline.tokenizer,
unet=OnnxRuntimeModel.from_pretrained(output_path / "unet"),
scheduler=pipeline.scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
requires_safety_checker=safety_checker is not None,
)
onnx_pipeline.save_pretrained(output_path)
print("ONNX pipeline saved to", output_path)
del pipeline
del onnx_pipeline
_ = OnnxStableDiffusionPipeline.from_pretrained(output_path, provider="CPUExecutionProvider")
print("ONNX pipeline is loadable")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_path",
type=str,
required=True,
help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
)
parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--opset",
default=14,
type=int,
help="The version of the ONNX operator set to use.",
)
parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")
args = parser.parse_args()
convert_models(args.model_path, args.output_path, args.opset, args.fp16)
| diffusers-ft-main | scripts/convert_stable_diffusion_checkpoint_to_onnx.py |
import json
import os
import torch
from diffusers import UNet1DModel
os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)
def unet(hor):
if hor == 128:
down_block_types = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
block_out_channels = (32, 128, 256)
up_block_types = ("UpResnetBlock1D", "UpResnetBlock1D")
elif hor == 32:
down_block_types = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
block_out_channels = (32, 64, 128, 256)
up_block_types = ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
model = torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch")
state_dict = model.state_dict()
config = dict(
down_block_types=down_block_types,
block_out_channels=block_out_channels,
up_block_types=up_block_types,
layers_per_block=1,
use_timestep_embedding=True,
out_block_type="OutConv1DBlock",
norm_num_groups=8,
downsample_each_block=False,
in_channels=14,
out_channels=14,
extra_in_channels=0,
time_embedding_type="positional",
flip_sin_to_cos=False,
freq_shift=1,
sample_size=65536,
mid_block_type="MidResTemporalBlock1D",
act_fn="mish",
)
hf_value_function = UNet1DModel(**config)
print(f"length of state dict: {len(state_dict.keys())}")
print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")
mapping = dict((k, hfk) for k, hfk in zip(model.state_dict().keys(), hf_value_function.state_dict().keys()))
for k, v in mapping.items():
state_dict[v] = state_dict.pop(k)
hf_value_function.load_state_dict(state_dict)
torch.save(hf_value_function.state_dict(), f"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin")
with open(f"hub/hopper-medium-v2/unet/hor{hor}/config.json", "w") as f:
json.dump(config, f)
def value_function():
config = dict(
in_channels=14,
down_block_types=("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
up_block_types=(),
out_block_type="ValueFunction",
mid_block_type="ValueFunctionMidBlock1D",
block_out_channels=(32, 64, 128, 256),
layers_per_block=1,
downsample_each_block=True,
sample_size=65536,
out_channels=14,
extra_in_channels=0,
time_embedding_type="positional",
use_timestep_embedding=True,
flip_sin_to_cos=False,
freq_shift=1,
norm_num_groups=8,
act_fn="mish",
)
model = torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch")
state_dict = model
hf_value_function = UNet1DModel(**config)
print(f"length of state dict: {len(state_dict.keys())}")
print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")
mapping = dict((k, hfk) for k, hfk in zip(state_dict.keys(), hf_value_function.state_dict().keys()))
for k, v in mapping.items():
state_dict[v] = state_dict.pop(k)
hf_value_function.load_state_dict(state_dict)
torch.save(hf_value_function.state_dict(), "hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin")
with open("hub/hopper-medium-v2/value_function/config.json", "w") as f:
json.dump(config, f)
if __name__ == "__main__":
unet(32)
# unet(128)
value_function()
| diffusers-ft-main | scripts/convert_models_diffuser_to_diffusers.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Conversion script for the NCSNPP checkpoints. """
import argparse
import json
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNet2DModel
def convert_ncsnpp_checkpoint(checkpoint, config):
"""
Takes a state dict and the path to
"""
new_model_architecture = UNet2DModel(**config)
new_model_architecture.time_proj.W.data = checkpoint["all_modules.0.W"].data
new_model_architecture.time_proj.weight.data = checkpoint["all_modules.0.W"].data
new_model_architecture.time_embedding.linear_1.weight.data = checkpoint["all_modules.1.weight"].data
new_model_architecture.time_embedding.linear_1.bias.data = checkpoint["all_modules.1.bias"].data
new_model_architecture.time_embedding.linear_2.weight.data = checkpoint["all_modules.2.weight"].data
new_model_architecture.time_embedding.linear_2.bias.data = checkpoint["all_modules.2.bias"].data
new_model_architecture.conv_in.weight.data = checkpoint["all_modules.3.weight"].data
new_model_architecture.conv_in.bias.data = checkpoint["all_modules.3.bias"].data
new_model_architecture.conv_norm_out.weight.data = checkpoint[list(checkpoint.keys())[-4]].data
new_model_architecture.conv_norm_out.bias.data = checkpoint[list(checkpoint.keys())[-3]].data
new_model_architecture.conv_out.weight.data = checkpoint[list(checkpoint.keys())[-2]].data
new_model_architecture.conv_out.bias.data = checkpoint[list(checkpoint.keys())[-1]].data
module_index = 4
def set_attention_weights(new_layer, old_checkpoint, index):
new_layer.query.weight.data = old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
new_layer.key.weight.data = old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
new_layer.value.weight.data = old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T
new_layer.query.bias.data = old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
new_layer.key.bias.data = old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
new_layer.value.bias.data = old_checkpoint[f"all_modules.{index}.NIN_2.b"].data
new_layer.proj_attn.weight.data = old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
new_layer.proj_attn.bias.data = old_checkpoint[f"all_modules.{index}.NIN_3.b"].data
new_layer.group_norm.weight.data = old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
new_layer.group_norm.bias.data = old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
def set_resnet_weights(new_layer, old_checkpoint, index):
new_layer.conv1.weight.data = old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
new_layer.conv1.bias.data = old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
new_layer.norm1.weight.data = old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
new_layer.norm1.bias.data = old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
new_layer.conv2.weight.data = old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
new_layer.conv2.bias.data = old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
new_layer.norm2.weight.data = old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
new_layer.norm2.bias.data = old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data
new_layer.time_emb_proj.weight.data = old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
new_layer.time_emb_proj.bias.data = old_checkpoint[f"all_modules.{index}.Dense_0.bias"].data
if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
new_layer.conv_shortcut.weight.data = old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
new_layer.conv_shortcut.bias.data = old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data
for i, block in enumerate(new_model_architecture.downsample_blocks):
has_attentions = hasattr(block, "attentions")
for j in range(len(block.resnets)):
set_resnet_weights(block.resnets[j], checkpoint, module_index)
module_index += 1
if has_attentions:
set_attention_weights(block.attentions[j], checkpoint, module_index)
module_index += 1
if hasattr(block, "downsamplers") and block.downsamplers is not None:
set_resnet_weights(block.resnet_down, checkpoint, module_index)
module_index += 1
block.skip_conv.weight.data = checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
block.skip_conv.bias.data = checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
module_index += 1
set_resnet_weights(new_model_architecture.mid_block.resnets[0], checkpoint, module_index)
module_index += 1
set_attention_weights(new_model_architecture.mid_block.attentions[0], checkpoint, module_index)
module_index += 1
set_resnet_weights(new_model_architecture.mid_block.resnets[1], checkpoint, module_index)
module_index += 1
for i, block in enumerate(new_model_architecture.up_blocks):
has_attentions = hasattr(block, "attentions")
for j in range(len(block.resnets)):
set_resnet_weights(block.resnets[j], checkpoint, module_index)
module_index += 1
if has_attentions:
set_attention_weights(
block.attentions[0], checkpoint, module_index
) # why can there only be a single attention layer for up?
module_index += 1
if hasattr(block, "resnet_up") and block.resnet_up is not None:
block.skip_norm.weight.data = checkpoint[f"all_modules.{module_index}.weight"].data
block.skip_norm.bias.data = checkpoint[f"all_modules.{module_index}.bias"].data
module_index += 1
block.skip_conv.weight.data = checkpoint[f"all_modules.{module_index}.weight"].data
block.skip_conv.bias.data = checkpoint[f"all_modules.{module_index}.bias"].data
module_index += 1
set_resnet_weights(block.resnet_up, checkpoint, module_index)
module_index += 1
new_model_architecture.conv_norm_out.weight.data = checkpoint[f"all_modules.{module_index}.weight"].data
new_model_architecture.conv_norm_out.bias.data = checkpoint[f"all_modules.{module_index}.bias"].data
module_index += 1
new_model_architecture.conv_out.weight.data = checkpoint[f"all_modules.{module_index}.weight"].data
new_model_architecture.conv_out.bias.data = checkpoint[f"all_modules.{module_index}.bias"].data
return new_model_architecture.state_dict()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path",
default="/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin",
type=str,
required=False,
help="Path to the checkpoint to convert.",
)
parser.add_argument(
"--config_file",
default="/Users/arthurzucker/Work/diffusers/ArthurZ/config.json",
type=str,
required=False,
help="The config json file corresponding to the architecture.",
)
parser.add_argument(
"--dump_path",
default="/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt",
type=str,
required=False,
help="Path to the output model.",
)
args = parser.parse_args()
checkpoint = torch.load(args.checkpoint_path, map_location="cpu")
with open(args.config_file) as f:
config = json.loads(f.read())
converted_checkpoint = convert_ncsnpp_checkpoint(
checkpoint,
config,
)
if "sde" in config:
del config["sde"]
model = UNet2DModel(**config)
model.load_state_dict(converted_checkpoint)
try:
scheduler = ScoreSdeVeScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
pipe = ScoreSdeVePipeline(unet=model, scheduler=scheduler)
pipe.save_pretrained(args.dump_path)
except:
model.save_pretrained(args.dump_path)
| diffusers-ft-main | scripts/convert_ncsnpp_original_checkpoint_to_diffusers.py |
import random
import torch
from diffusers import UNet2DModel
from huggingface_hub import HfApi
api = HfApi()
results = {}
# fmt: off
results["google_ddpm_cifar10_32"] = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
-1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
results["google_ddpm_ema_bedroom_256"] = torch.tensor([
-2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
-2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
results["CompVis_ldm_celebahq_256"] = torch.tensor([
-0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
-0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
-0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
results["google_ncsnpp_ffhq_1024"] = torch.tensor([
0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
-0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
-0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
results["google_ncsnpp_bedroom_256"] = torch.tensor([
0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
-0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
-0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
results["google_ncsnpp_celebahq_256"] = torch.tensor([
0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
-0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
-0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
results["google_ncsnpp_church_256"] = torch.tensor([
0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
-0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
-0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
results["google_ncsnpp_ffhq_256"] = torch.tensor([
0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
-0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
-0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
results["google_ddpm_cat_256"] = torch.tensor([
-1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
-2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
results["google_ddpm_celebahq_256"] = torch.tensor([
-1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
-2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
results["google_ddpm_ema_celebahq_256"] = torch.tensor([
-1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
-2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
results["google_ddpm_church_256"] = torch.tensor([
-2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
-3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
results["google_ddpm_bedroom_256"] = torch.tensor([
-2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
-2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
results["google_ddpm_ema_church_256"] = torch.tensor([
-2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
-3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
results["google_ddpm_ema_cat_256"] = torch.tensor([
-1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
-2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on
models = api.list_models(filter="diffusers")
for mod in models:
if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
local_checkpoint = "/home/patrick/google_checkpoints/" + mod.modelId.split("/")[-1]
print(f"Started running {mod.modelId}!!!")
if mod.modelId.startswith("CompVis"):
model = UNet2DModel.from_pretrained(local_checkpoint, subfolder="unet")
else:
model = UNet2DModel.from_pretrained(local_checkpoint)
torch.manual_seed(0)
random.seed(0)
noise = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
time_step = torch.tensor([10] * noise.shape[0])
with torch.no_grad():
logits = model(noise, time_step).sample
assert torch.allclose(
logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1e-3
)
print(f"{mod.modelId} has passed successfully!!!")
| diffusers-ft-main | scripts/generate_logits.py |
#!/usr/bin/env python3
import argparse
import math
import os
from copy import deepcopy
import torch
from torch import nn
from audio_diffusion.models import DiffusionAttnUnet1D
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNet1DModel
from diffusion import sampling
MODELS_MAP = {
"gwf-440k": {
"url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
"sample_rate": 48000,
"sample_size": 65536,
},
"jmann-small-190k": {
"url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
"sample_rate": 48000,
"sample_size": 65536,
},
"jmann-large-580k": {
"url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
"sample_rate": 48000,
"sample_size": 131072,
},
"maestro-uncond-150k": {
"url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
"sample_rate": 16000,
"sample_size": 65536,
},
"unlocked-uncond-250k": {
"url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
"sample_rate": 16000,
"sample_size": 65536,
},
"honk-140k": {
"url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
"sample_rate": 16000,
"sample_size": 65536,
},
}
def alpha_sigma_to_t(alpha, sigma):
"""Returns a timestep, given the scaling factors for the clean image and for
the noise."""
return torch.atan2(sigma, alpha) / math.pi * 2
def get_crash_schedule(t):
sigma = torch.sin(t * math.pi / 2) ** 2
alpha = (1 - sigma**2) ** 0.5
return alpha_sigma_to_t(alpha, sigma)
class Object(object):
pass
class DiffusionUncond(nn.Module):
def __init__(self, global_args):
super().__init__()
self.diffusion = DiffusionAttnUnet1D(global_args, n_attn_layers=4)
self.diffusion_ema = deepcopy(self.diffusion)
self.rng = torch.quasirandom.SobolEngine(1, scramble=True)
def download(model_name):
url = MODELS_MAP[model_name]["url"]
os.system(f"wget {url} ./")
return f"./{model_name}.ckpt"
DOWN_NUM_TO_LAYER = {
"1": "resnets.0",
"2": "attentions.0",
"3": "resnets.1",
"4": "attentions.1",
"5": "resnets.2",
"6": "attentions.2",
}
UP_NUM_TO_LAYER = {
"8": "resnets.0",
"9": "attentions.0",
"10": "resnets.1",
"11": "attentions.1",
"12": "resnets.2",
"13": "attentions.2",
}
MID_NUM_TO_LAYER = {
"1": "resnets.0",
"2": "attentions.0",
"3": "resnets.1",
"4": "attentions.1",
"5": "resnets.2",
"6": "attentions.2",
"8": "resnets.3",
"9": "attentions.3",
"10": "resnets.4",
"11": "attentions.4",
"12": "resnets.5",
"13": "attentions.5",
}
DEPTH_0_TO_LAYER = {
"0": "resnets.0",
"1": "resnets.1",
"2": "resnets.2",
"4": "resnets.0",
"5": "resnets.1",
"6": "resnets.2",
}
RES_CONV_MAP = {
"skip": "conv_skip",
"main.0": "conv_1",
"main.1": "group_norm_1",
"main.3": "conv_2",
"main.4": "group_norm_2",
}
ATTN_MAP = {
"norm": "group_norm",
"qkv_proj": ["query", "key", "value"],
"out_proj": ["proj_attn"],
}
def convert_resconv_naming(name):
if name.startswith("skip"):
return name.replace("skip", RES_CONV_MAP["skip"])
# name has to be of format main.{digit}
if not name.startswith("main."):
raise ValueError(f"ResConvBlock error with {name}")
return name.replace(name[:6], RES_CONV_MAP[name[:6]])
def convert_attn_naming(name):
for key, value in ATTN_MAP.items():
if name.startswith(key) and not isinstance(value, list):
return name.replace(key, value)
elif name.startswith(key):
return [name.replace(key, v) for v in value]
raise ValueError(f"Attn error with {name}")
def rename(input_string, max_depth=13):
string = input_string
if string.split(".")[0] == "timestep_embed":
return string.replace("timestep_embed", "time_proj")
depth = 0
if string.startswith("net.3."):
depth += 1
string = string[6:]
elif string.startswith("net."):
string = string[4:]
while string.startswith("main.7."):
depth += 1
string = string[7:]
if string.startswith("main."):
string = string[5:]
# mid block
if string[:2].isdigit():
layer_num = string[:2]
string_left = string[2:]
else:
layer_num = string[0]
string_left = string[1:]
if depth == max_depth:
new_layer = MID_NUM_TO_LAYER[layer_num]
prefix = "mid_block"
elif depth > 0 and int(layer_num) < 7:
new_layer = DOWN_NUM_TO_LAYER[layer_num]
prefix = f"down_blocks.{depth}"
elif depth > 0 and int(layer_num) > 7:
new_layer = UP_NUM_TO_LAYER[layer_num]
prefix = f"up_blocks.{max_depth - depth - 1}"
elif depth == 0:
new_layer = DEPTH_0_TO_LAYER[layer_num]
prefix = f"up_blocks.{max_depth - 1}" if int(layer_num) > 3 else "down_blocks.0"
if not string_left.startswith("."):
raise ValueError(f"Naming error with {input_string} and string_left: {string_left}.")
string_left = string_left[1:]
if "resnets" in new_layer:
string_left = convert_resconv_naming(string_left)
elif "attentions" in new_layer:
new_string_left = convert_attn_naming(string_left)
string_left = new_string_left
if not isinstance(string_left, list):
new_string = prefix + "." + new_layer + "." + string_left
else:
new_string = [prefix + "." + new_layer + "." + s for s in string_left]
return new_string
def rename_orig_weights(state_dict):
new_state_dict = {}
for k, v in state_dict.items():
if k.endswith("kernel"):
# up- and downsample layers, don't have trainable weights
continue
new_k = rename(k)
# check if we need to transform from Conv => Linear for attention
if isinstance(new_k, list):
new_state_dict = transform_conv_attns(new_state_dict, new_k, v)
else:
new_state_dict[new_k] = v
return new_state_dict
def transform_conv_attns(new_state_dict, new_k, v):
if len(new_k) == 1:
if len(v.shape) == 3:
# weight
new_state_dict[new_k[0]] = v[:, :, 0]
else:
# bias
new_state_dict[new_k[0]] = v
else:
# qkv matrices
trippled_shape = v.shape[0]
single_shape = trippled_shape // 3
for i in range(3):
if len(v.shape) == 3:
new_state_dict[new_k[i]] = v[i * single_shape : (i + 1) * single_shape, :, 0]
else:
new_state_dict[new_k[i]] = v[i * single_shape : (i + 1) * single_shape]
return new_state_dict
def main(args):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_name = args.model_path.split("/")[-1].split(".")[0]
if not os.path.isfile(args.model_path):
assert (
model_name == args.model_path
), f"Make sure to provide one of the official model names {MODELS_MAP.keys()}"
args.model_path = download(model_name)
sample_rate = MODELS_MAP[model_name]["sample_rate"]
sample_size = MODELS_MAP[model_name]["sample_size"]
config = Object()
config.sample_size = sample_size
config.sample_rate = sample_rate
config.latent_dim = 0
diffusers_model = UNet1DModel(sample_size=sample_size, sample_rate=sample_rate)
diffusers_state_dict = diffusers_model.state_dict()
orig_model = DiffusionUncond(config)
orig_model.load_state_dict(torch.load(args.model_path, map_location=device)["state_dict"])
orig_model = orig_model.diffusion_ema.eval()
orig_model_state_dict = orig_model.state_dict()
renamed_state_dict = rename_orig_weights(orig_model_state_dict)
renamed_minus_diffusers = set(renamed_state_dict.keys()) - set(diffusers_state_dict.keys())
diffusers_minus_renamed = set(diffusers_state_dict.keys()) - set(renamed_state_dict.keys())
assert len(renamed_minus_diffusers) == 0, f"Problem with {renamed_minus_diffusers}"
assert all(k.endswith("kernel") for k in list(diffusers_minus_renamed)), f"Problem with {diffusers_minus_renamed}"
for key, value in renamed_state_dict.items():
assert (
diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
), f"Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"
if key == "time_proj.weight":
value = value.squeeze()
diffusers_state_dict[key] = value
diffusers_model.load_state_dict(diffusers_state_dict)
steps = 100
seed = 33
diffusers_scheduler = IPNDMScheduler(num_train_timesteps=steps)
generator = torch.manual_seed(seed)
noise = torch.randn([1, 2, config.sample_size], generator=generator).to(device)
t = torch.linspace(1, 0, steps + 1, device=device)[:-1]
step_list = get_crash_schedule(t)
pipe = DanceDiffusionPipeline(unet=diffusers_model, scheduler=diffusers_scheduler)
generator = torch.manual_seed(33)
audio = pipe(num_inference_steps=steps, generator=generator).audios
generated = sampling.iplms_sample(orig_model, noise, step_list, {})
generated = generated.clamp(-1, 1)
diff_sum = (generated - audio).abs().sum()
diff_max = (generated - audio).abs().max()
if args.save:
pipe.save_pretrained(args.checkpoint_path)
print("Diff sum", diff_sum)
print("Diff max", diff_max)
assert diff_max < 1e-3, f"Diff max: {diff_max} is too much :-/"
print(f"Conversion for {model_name} successful!")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
parser.add_argument(
"--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
)
parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
args = parser.parse_args()
main(args)
| diffusers-ft-main | scripts/convert_dance_diffusion_to_diffusers.py |
"""
This script ports models from VQ-diffusion (https://github.com/microsoft/VQ-Diffusion) to diffusers.
It currently only supports porting the ITHQ dataset.
ITHQ dataset:
```sh
# From the root directory of diffusers.
# Download the VQVAE checkpoint
$ wget https://facevcstandard.blob.core.windows.net/v-zhictang/Improved-VQ-Diffusion_model_release/ithq_vqvae.pth?sv=2020-10-02&st=2022-05-30T15%3A17%3A18Z&se=2030-05-31T15%3A17%3A00Z&sr=b&sp=r&sig=1jVavHFPpUjDs%2FTO1V3PTezaNbPp2Nx8MxiWI7y6fEY%3D -O ithq_vqvae.pth
# Download the VQVAE config
# NOTE that in VQ-diffusion the documented file is `configs/ithq.yaml` but the target class
# `image_synthesis.modeling.codecs.image_codec.ema_vqvae.PatchVQVAE`
# loads `OUTPUT/pretrained_model/taming_dvae/config.yaml`
$ wget https://raw.githubusercontent.com/microsoft/VQ-Diffusion/main/OUTPUT/pretrained_model/taming_dvae/config.yaml -O ithq_vqvae.yaml
# Download the main model checkpoint
$ wget https://facevcstandard.blob.core.windows.net/v-zhictang/Improved-VQ-Diffusion_model_release/ithq_learnable.pth?sv=2020-10-02&st=2022-05-30T10%3A22%3A06Z&se=2030-05-31T10%3A22%3A00Z&sr=b&sp=r&sig=GOE%2Bza02%2FPnGxYVOOPtwrTR4RA3%2F5NVgMxdW4kjaEZ8%3D -O ithq_learnable.pth
# Download the main model config
$ wget https://raw.githubusercontent.com/microsoft/VQ-Diffusion/main/configs/ithq.yaml -O ithq.yaml
# run the convert script
$ python ./scripts/convert_vq_diffusion_to_diffusers.py \
--checkpoint_path ./ithq_learnable.pth \
--original_config_file ./ithq.yaml \
--vqvae_checkpoint_path ./ithq_vqvae.pth \
--vqvae_original_config_file ./ithq_vqvae.yaml \
--dump_path <path to save pre-trained `VQDiffusionPipeline`>
```
"""
import argparse
import tempfile
import torch
import yaml
from accelerate import init_empty_weights, load_checkpoint_and_dispatch
from diffusers import Transformer2DModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from transformers import CLIPTextModel, CLIPTokenizer
from yaml.loader import FullLoader
try:
from omegaconf import OmegaConf
except ImportError:
raise ImportError(
"OmegaConf is required to convert the VQ Diffusion checkpoints. Please install it with `pip install"
" OmegaConf`."
)
# vqvae model
PORTED_VQVAES = ["image_synthesis.modeling.codecs.image_codec.patch_vqgan.PatchVQGAN"]
def vqvae_model_from_original_config(original_config):
assert original_config.target in PORTED_VQVAES, f"{original_config.target} has not yet been ported to diffusers."
original_config = original_config.params
original_encoder_config = original_config.encoder_config.params
original_decoder_config = original_config.decoder_config.params
in_channels = original_encoder_config.in_channels
out_channels = original_decoder_config.out_ch
down_block_types = get_down_block_types(original_encoder_config)
up_block_types = get_up_block_types(original_decoder_config)
assert original_encoder_config.ch == original_decoder_config.ch
assert original_encoder_config.ch_mult == original_decoder_config.ch_mult
block_out_channels = tuple(
[original_encoder_config.ch * a_ch_mult for a_ch_mult in original_encoder_config.ch_mult]
)
assert original_encoder_config.num_res_blocks == original_decoder_config.num_res_blocks
layers_per_block = original_encoder_config.num_res_blocks
assert original_encoder_config.z_channels == original_decoder_config.z_channels
latent_channels = original_encoder_config.z_channels
num_vq_embeddings = original_config.n_embed
# Hard coded value for ResnetBlock.GoupNorm(num_groups) in VQ-diffusion
norm_num_groups = 32
e_dim = original_config.embed_dim
model = VQModel(
in_channels=in_channels,
out_channels=out_channels,
down_block_types=down_block_types,
up_block_types=up_block_types,
block_out_channels=block_out_channels,
layers_per_block=layers_per_block,
latent_channels=latent_channels,
num_vq_embeddings=num_vq_embeddings,
norm_num_groups=norm_num_groups,
vq_embed_dim=e_dim,
)
return model
def get_down_block_types(original_encoder_config):
attn_resolutions = coerce_attn_resolutions(original_encoder_config.attn_resolutions)
num_resolutions = len(original_encoder_config.ch_mult)
resolution = coerce_resolution(original_encoder_config.resolution)
curr_res = resolution
down_block_types = []
for _ in range(num_resolutions):
if curr_res in attn_resolutions:
down_block_type = "AttnDownEncoderBlock2D"
else:
down_block_type = "DownEncoderBlock2D"
down_block_types.append(down_block_type)
curr_res = [r // 2 for r in curr_res]
return down_block_types
def get_up_block_types(original_decoder_config):
attn_resolutions = coerce_attn_resolutions(original_decoder_config.attn_resolutions)
num_resolutions = len(original_decoder_config.ch_mult)
resolution = coerce_resolution(original_decoder_config.resolution)
curr_res = [r // 2 ** (num_resolutions - 1) for r in resolution]
up_block_types = []
for _ in reversed(range(num_resolutions)):
if curr_res in attn_resolutions:
up_block_type = "AttnUpDecoderBlock2D"
else:
up_block_type = "UpDecoderBlock2D"
up_block_types.append(up_block_type)
curr_res = [r * 2 for r in curr_res]
return up_block_types
def coerce_attn_resolutions(attn_resolutions):
attn_resolutions = OmegaConf.to_object(attn_resolutions)
attn_resolutions_ = []
for ar in attn_resolutions:
if isinstance(ar, (list, tuple)):
attn_resolutions_.append(list(ar))
else:
attn_resolutions_.append([ar, ar])
return attn_resolutions_
def coerce_resolution(resolution):
resolution = OmegaConf.to_object(resolution)
if isinstance(resolution, int):
resolution = [resolution, resolution] # H, W
elif isinstance(resolution, (tuple, list)):
resolution = list(resolution)
else:
raise ValueError("Unknown type of resolution:", resolution)
return resolution
# done vqvae model
# vqvae checkpoint
def vqvae_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):
diffusers_checkpoint = {}
diffusers_checkpoint.update(vqvae_encoder_to_diffusers_checkpoint(model, checkpoint))
# quant_conv
diffusers_checkpoint.update(
{
"quant_conv.weight": checkpoint["quant_conv.weight"],
"quant_conv.bias": checkpoint["quant_conv.bias"],
}
)
# quantize
diffusers_checkpoint.update({"quantize.embedding.weight": checkpoint["quantize.embedding"]})
# post_quant_conv
diffusers_checkpoint.update(
{
"post_quant_conv.weight": checkpoint["post_quant_conv.weight"],
"post_quant_conv.bias": checkpoint["post_quant_conv.bias"],
}
)
# decoder
diffusers_checkpoint.update(vqvae_decoder_to_diffusers_checkpoint(model, checkpoint))
return diffusers_checkpoint
def vqvae_encoder_to_diffusers_checkpoint(model, checkpoint):
diffusers_checkpoint = {}
# conv_in
diffusers_checkpoint.update(
{
"encoder.conv_in.weight": checkpoint["encoder.conv_in.weight"],
"encoder.conv_in.bias": checkpoint["encoder.conv_in.bias"],
}
)
# down_blocks
for down_block_idx, down_block in enumerate(model.encoder.down_blocks):
diffusers_down_block_prefix = f"encoder.down_blocks.{down_block_idx}"
down_block_prefix = f"encoder.down.{down_block_idx}"
# resnets
for resnet_idx, resnet in enumerate(down_block.resnets):
diffusers_resnet_prefix = f"{diffusers_down_block_prefix}.resnets.{resnet_idx}"
resnet_prefix = f"{down_block_prefix}.block.{resnet_idx}"
diffusers_checkpoint.update(
vqvae_resnet_to_diffusers_checkpoint(
resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix
)
)
# downsample
# do not include the downsample when on the last down block
# There is no downsample on the last down block
if down_block_idx != len(model.encoder.down_blocks) - 1:
# There's a single downsample in the original checkpoint but a list of downsamples
# in the diffusers model.
diffusers_downsample_prefix = f"{diffusers_down_block_prefix}.downsamplers.0.conv"
downsample_prefix = f"{down_block_prefix}.downsample.conv"
diffusers_checkpoint.update(
{
f"{diffusers_downsample_prefix}.weight": checkpoint[f"{downsample_prefix}.weight"],
f"{diffusers_downsample_prefix}.bias": checkpoint[f"{downsample_prefix}.bias"],
}
)
# attentions
if hasattr(down_block, "attentions"):
for attention_idx, _ in enumerate(down_block.attentions):
diffusers_attention_prefix = f"{diffusers_down_block_prefix}.attentions.{attention_idx}"
attention_prefix = f"{down_block_prefix}.attn.{attention_idx}"
diffusers_checkpoint.update(
vqvae_attention_to_diffusers_checkpoint(
checkpoint,
diffusers_attention_prefix=diffusers_attention_prefix,
attention_prefix=attention_prefix,
)
)
# mid block
# mid block attentions
# There is a single hardcoded attention block in the middle of the VQ-diffusion encoder
diffusers_attention_prefix = "encoder.mid_block.attentions.0"
attention_prefix = "encoder.mid.attn_1"
diffusers_checkpoint.update(
vqvae_attention_to_diffusers_checkpoint(
checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix
)
)
# mid block resnets
for diffusers_resnet_idx, resnet in enumerate(model.encoder.mid_block.resnets):
diffusers_resnet_prefix = f"encoder.mid_block.resnets.{diffusers_resnet_idx}"
# the hardcoded prefixes to `block_` are 1 and 2
orig_resnet_idx = diffusers_resnet_idx + 1
# There are two hardcoded resnets in the middle of the VQ-diffusion encoder
resnet_prefix = f"encoder.mid.block_{orig_resnet_idx}"
diffusers_checkpoint.update(
vqvae_resnet_to_diffusers_checkpoint(
resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix
)
)
diffusers_checkpoint.update(
{
# conv_norm_out
"encoder.conv_norm_out.weight": checkpoint["encoder.norm_out.weight"],
"encoder.conv_norm_out.bias": checkpoint["encoder.norm_out.bias"],
# conv_out
"encoder.conv_out.weight": checkpoint["encoder.conv_out.weight"],
"encoder.conv_out.bias": checkpoint["encoder.conv_out.bias"],
}
)
return diffusers_checkpoint
def vqvae_decoder_to_diffusers_checkpoint(model, checkpoint):
diffusers_checkpoint = {}
# conv in
diffusers_checkpoint.update(
{
"decoder.conv_in.weight": checkpoint["decoder.conv_in.weight"],
"decoder.conv_in.bias": checkpoint["decoder.conv_in.bias"],
}
)
# up_blocks
for diffusers_up_block_idx, up_block in enumerate(model.decoder.up_blocks):
# up_blocks are stored in reverse order in the VQ-diffusion checkpoint
orig_up_block_idx = len(model.decoder.up_blocks) - 1 - diffusers_up_block_idx
diffusers_up_block_prefix = f"decoder.up_blocks.{diffusers_up_block_idx}"
up_block_prefix = f"decoder.up.{orig_up_block_idx}"
# resnets
for resnet_idx, resnet in enumerate(up_block.resnets):
diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
resnet_prefix = f"{up_block_prefix}.block.{resnet_idx}"
diffusers_checkpoint.update(
vqvae_resnet_to_diffusers_checkpoint(
resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix
)
)
# upsample
# there is no up sample on the last up block
if diffusers_up_block_idx != len(model.decoder.up_blocks) - 1:
# There's a single upsample in the VQ-diffusion checkpoint but a list of downsamples
# in the diffusers model.
diffusers_downsample_prefix = f"{diffusers_up_block_prefix}.upsamplers.0.conv"
downsample_prefix = f"{up_block_prefix}.upsample.conv"
diffusers_checkpoint.update(
{
f"{diffusers_downsample_prefix}.weight": checkpoint[f"{downsample_prefix}.weight"],
f"{diffusers_downsample_prefix}.bias": checkpoint[f"{downsample_prefix}.bias"],
}
)
# attentions
if hasattr(up_block, "attentions"):
for attention_idx, _ in enumerate(up_block.attentions):
diffusers_attention_prefix = f"{diffusers_up_block_prefix}.attentions.{attention_idx}"
attention_prefix = f"{up_block_prefix}.attn.{attention_idx}"
diffusers_checkpoint.update(
vqvae_attention_to_diffusers_checkpoint(
checkpoint,
diffusers_attention_prefix=diffusers_attention_prefix,
attention_prefix=attention_prefix,
)
)
# mid block
# mid block attentions
# There is a single hardcoded attention block in the middle of the VQ-diffusion decoder
diffusers_attention_prefix = "decoder.mid_block.attentions.0"
attention_prefix = "decoder.mid.attn_1"
diffusers_checkpoint.update(
vqvae_attention_to_diffusers_checkpoint(
checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix
)
)
# mid block resnets
for diffusers_resnet_idx, resnet in enumerate(model.encoder.mid_block.resnets):
diffusers_resnet_prefix = f"decoder.mid_block.resnets.{diffusers_resnet_idx}"
# the hardcoded prefixes to `block_` are 1 and 2
orig_resnet_idx = diffusers_resnet_idx + 1
# There are two hardcoded resnets in the middle of the VQ-diffusion decoder
resnet_prefix = f"decoder.mid.block_{orig_resnet_idx}"
diffusers_checkpoint.update(
vqvae_resnet_to_diffusers_checkpoint(
resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix
)
)
diffusers_checkpoint.update(
{
# conv_norm_out
"decoder.conv_norm_out.weight": checkpoint["decoder.norm_out.weight"],
"decoder.conv_norm_out.bias": checkpoint["decoder.norm_out.bias"],
# conv_out
"decoder.conv_out.weight": checkpoint["decoder.conv_out.weight"],
"decoder.conv_out.bias": checkpoint["decoder.conv_out.bias"],
}
)
return diffusers_checkpoint
def vqvae_resnet_to_diffusers_checkpoint(resnet, checkpoint, *, diffusers_resnet_prefix, resnet_prefix):
rv = {
# norm1
f"{diffusers_resnet_prefix}.norm1.weight": checkpoint[f"{resnet_prefix}.norm1.weight"],
f"{diffusers_resnet_prefix}.norm1.bias": checkpoint[f"{resnet_prefix}.norm1.bias"],
# conv1
f"{diffusers_resnet_prefix}.conv1.weight": checkpoint[f"{resnet_prefix}.conv1.weight"],
f"{diffusers_resnet_prefix}.conv1.bias": checkpoint[f"{resnet_prefix}.conv1.bias"],
# norm2
f"{diffusers_resnet_prefix}.norm2.weight": checkpoint[f"{resnet_prefix}.norm2.weight"],
f"{diffusers_resnet_prefix}.norm2.bias": checkpoint[f"{resnet_prefix}.norm2.bias"],
# conv2
f"{diffusers_resnet_prefix}.conv2.weight": checkpoint[f"{resnet_prefix}.conv2.weight"],
f"{diffusers_resnet_prefix}.conv2.bias": checkpoint[f"{resnet_prefix}.conv2.bias"],
}
if resnet.conv_shortcut is not None:
rv.update(
{
f"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[f"{resnet_prefix}.nin_shortcut.weight"],
f"{diffusers_resnet_prefix}.conv_shortcut.bias": checkpoint[f"{resnet_prefix}.nin_shortcut.bias"],
}
)
return rv
def vqvae_attention_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix):
return {
# group_norm
f"{diffusers_attention_prefix}.group_norm.weight": checkpoint[f"{attention_prefix}.norm.weight"],
f"{diffusers_attention_prefix}.group_norm.bias": checkpoint[f"{attention_prefix}.norm.bias"],
# query
f"{diffusers_attention_prefix}.query.weight": checkpoint[f"{attention_prefix}.q.weight"][:, :, 0, 0],
f"{diffusers_attention_prefix}.query.bias": checkpoint[f"{attention_prefix}.q.bias"],
# key
f"{diffusers_attention_prefix}.key.weight": checkpoint[f"{attention_prefix}.k.weight"][:, :, 0, 0],
f"{diffusers_attention_prefix}.key.bias": checkpoint[f"{attention_prefix}.k.bias"],
# value
f"{diffusers_attention_prefix}.value.weight": checkpoint[f"{attention_prefix}.v.weight"][:, :, 0, 0],
f"{diffusers_attention_prefix}.value.bias": checkpoint[f"{attention_prefix}.v.bias"],
# proj_attn
f"{diffusers_attention_prefix}.proj_attn.weight": checkpoint[f"{attention_prefix}.proj_out.weight"][
:, :, 0, 0
],
f"{diffusers_attention_prefix}.proj_attn.bias": checkpoint[f"{attention_prefix}.proj_out.bias"],
}
# done vqvae checkpoint
# transformer model
PORTED_DIFFUSIONS = ["image_synthesis.modeling.transformers.diffusion_transformer.DiffusionTransformer"]
PORTED_TRANSFORMERS = ["image_synthesis.modeling.transformers.transformer_utils.Text2ImageTransformer"]
PORTED_CONTENT_EMBEDDINGS = ["image_synthesis.modeling.embeddings.dalle_mask_image_embedding.DalleMaskImageEmbedding"]
def transformer_model_from_original_config(
original_diffusion_config, original_transformer_config, original_content_embedding_config
):
assert (
original_diffusion_config.target in PORTED_DIFFUSIONS
), f"{original_diffusion_config.target} has not yet been ported to diffusers."
assert (
original_transformer_config.target in PORTED_TRANSFORMERS
), f"{original_transformer_config.target} has not yet been ported to diffusers."
assert (
original_content_embedding_config.target in PORTED_CONTENT_EMBEDDINGS
), f"{original_content_embedding_config.target} has not yet been ported to diffusers."
original_diffusion_config = original_diffusion_config.params
original_transformer_config = original_transformer_config.params
original_content_embedding_config = original_content_embedding_config.params
inner_dim = original_transformer_config["n_embd"]
n_heads = original_transformer_config["n_head"]
# VQ-Diffusion gives dimension of the multi-headed attention layers as the
# number of attention heads times the sequence length (the dimension) of a
# single head. We want to specify our attention blocks with those values
# specified separately
assert inner_dim % n_heads == 0
d_head = inner_dim // n_heads
depth = original_transformer_config["n_layer"]
context_dim = original_transformer_config["condition_dim"]
num_embed = original_content_embedding_config["num_embed"]
# the number of embeddings in the transformer includes the mask embedding.
# the content embedding (the vqvae) does not include the mask embedding.
num_embed = num_embed + 1
height = original_transformer_config["content_spatial_size"][0]
width = original_transformer_config["content_spatial_size"][1]
assert width == height, "width has to be equal to height"
dropout = original_transformer_config["resid_pdrop"]
num_embeds_ada_norm = original_diffusion_config["diffusion_step"]
model_kwargs = {
"attention_bias": True,
"cross_attention_dim": context_dim,
"attention_head_dim": d_head,
"num_layers": depth,
"dropout": dropout,
"num_attention_heads": n_heads,
"num_vector_embeds": num_embed,
"num_embeds_ada_norm": num_embeds_ada_norm,
"norm_num_groups": 32,
"sample_size": width,
"activation_fn": "geglu-approximate",
}
model = Transformer2DModel(**model_kwargs)
return model
# done transformer model
# transformer checkpoint
def transformer_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):
diffusers_checkpoint = {}
transformer_prefix = "transformer.transformer"
diffusers_latent_image_embedding_prefix = "latent_image_embedding"
latent_image_embedding_prefix = f"{transformer_prefix}.content_emb"
# DalleMaskImageEmbedding
diffusers_checkpoint.update(
{
f"{diffusers_latent_image_embedding_prefix}.emb.weight": checkpoint[
f"{latent_image_embedding_prefix}.emb.weight"
],
f"{diffusers_latent_image_embedding_prefix}.height_emb.weight": checkpoint[
f"{latent_image_embedding_prefix}.height_emb.weight"
],
f"{diffusers_latent_image_embedding_prefix}.width_emb.weight": checkpoint[
f"{latent_image_embedding_prefix}.width_emb.weight"
],
}
)
# transformer blocks
for transformer_block_idx, transformer_block in enumerate(model.transformer_blocks):
diffusers_transformer_block_prefix = f"transformer_blocks.{transformer_block_idx}"
transformer_block_prefix = f"{transformer_prefix}.blocks.{transformer_block_idx}"
# ada norm block
diffusers_ada_norm_prefix = f"{diffusers_transformer_block_prefix}.norm1"
ada_norm_prefix = f"{transformer_block_prefix}.ln1"
diffusers_checkpoint.update(
transformer_ada_norm_to_diffusers_checkpoint(
checkpoint, diffusers_ada_norm_prefix=diffusers_ada_norm_prefix, ada_norm_prefix=ada_norm_prefix
)
)
# attention block
diffusers_attention_prefix = f"{diffusers_transformer_block_prefix}.attn1"
attention_prefix = f"{transformer_block_prefix}.attn1"
diffusers_checkpoint.update(
transformer_attention_to_diffusers_checkpoint(
checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix
)
)
# ada norm block
diffusers_ada_norm_prefix = f"{diffusers_transformer_block_prefix}.norm2"
ada_norm_prefix = f"{transformer_block_prefix}.ln1_1"
diffusers_checkpoint.update(
transformer_ada_norm_to_diffusers_checkpoint(
checkpoint, diffusers_ada_norm_prefix=diffusers_ada_norm_prefix, ada_norm_prefix=ada_norm_prefix
)
)
# attention block
diffusers_attention_prefix = f"{diffusers_transformer_block_prefix}.attn2"
attention_prefix = f"{transformer_block_prefix}.attn2"
diffusers_checkpoint.update(
transformer_attention_to_diffusers_checkpoint(
checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix
)
)
# norm block
diffusers_norm_block_prefix = f"{diffusers_transformer_block_prefix}.norm3"
norm_block_prefix = f"{transformer_block_prefix}.ln2"
diffusers_checkpoint.update(
{
f"{diffusers_norm_block_prefix}.weight": checkpoint[f"{norm_block_prefix}.weight"],
f"{diffusers_norm_block_prefix}.bias": checkpoint[f"{norm_block_prefix}.bias"],
}
)
# feedforward block
diffusers_feedforward_prefix = f"{diffusers_transformer_block_prefix}.ff"
feedforward_prefix = f"{transformer_block_prefix}.mlp"
diffusers_checkpoint.update(
transformer_feedforward_to_diffusers_checkpoint(
checkpoint,
diffusers_feedforward_prefix=diffusers_feedforward_prefix,
feedforward_prefix=feedforward_prefix,
)
)
# to logits
diffusers_norm_out_prefix = "norm_out"
norm_out_prefix = f"{transformer_prefix}.to_logits.0"
diffusers_checkpoint.update(
{
f"{diffusers_norm_out_prefix}.weight": checkpoint[f"{norm_out_prefix}.weight"],
f"{diffusers_norm_out_prefix}.bias": checkpoint[f"{norm_out_prefix}.bias"],
}
)
diffusers_out_prefix = "out"
out_prefix = f"{transformer_prefix}.to_logits.1"
diffusers_checkpoint.update(
{
f"{diffusers_out_prefix}.weight": checkpoint[f"{out_prefix}.weight"],
f"{diffusers_out_prefix}.bias": checkpoint[f"{out_prefix}.bias"],
}
)
return diffusers_checkpoint
def transformer_ada_norm_to_diffusers_checkpoint(checkpoint, *, diffusers_ada_norm_prefix, ada_norm_prefix):
return {
f"{diffusers_ada_norm_prefix}.emb.weight": checkpoint[f"{ada_norm_prefix}.emb.weight"],
f"{diffusers_ada_norm_prefix}.linear.weight": checkpoint[f"{ada_norm_prefix}.linear.weight"],
f"{diffusers_ada_norm_prefix}.linear.bias": checkpoint[f"{ada_norm_prefix}.linear.bias"],
}
def transformer_attention_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix):
return {
# key
f"{diffusers_attention_prefix}.to_k.weight": checkpoint[f"{attention_prefix}.key.weight"],
f"{diffusers_attention_prefix}.to_k.bias": checkpoint[f"{attention_prefix}.key.bias"],
# query
f"{diffusers_attention_prefix}.to_q.weight": checkpoint[f"{attention_prefix}.query.weight"],
f"{diffusers_attention_prefix}.to_q.bias": checkpoint[f"{attention_prefix}.query.bias"],
# value
f"{diffusers_attention_prefix}.to_v.weight": checkpoint[f"{attention_prefix}.value.weight"],
f"{diffusers_attention_prefix}.to_v.bias": checkpoint[f"{attention_prefix}.value.bias"],
# linear out
f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[f"{attention_prefix}.proj.weight"],
f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[f"{attention_prefix}.proj.bias"],
}
def transformer_feedforward_to_diffusers_checkpoint(checkpoint, *, diffusers_feedforward_prefix, feedforward_prefix):
return {
f"{diffusers_feedforward_prefix}.net.0.proj.weight": checkpoint[f"{feedforward_prefix}.0.weight"],
f"{diffusers_feedforward_prefix}.net.0.proj.bias": checkpoint[f"{feedforward_prefix}.0.bias"],
f"{diffusers_feedforward_prefix}.net.2.weight": checkpoint[f"{feedforward_prefix}.2.weight"],
f"{diffusers_feedforward_prefix}.net.2.bias": checkpoint[f"{feedforward_prefix}.2.bias"],
}
# done transformer checkpoint
def read_config_file(filename):
# The yaml file contains annotations that certain values should
# loaded as tuples. By default, OmegaConf will panic when reading
# these. Instead, we can manually read the yaml with the FullLoader and then
# construct the OmegaConf object.
with open(filename) as f:
original_config = yaml.load(f, FullLoader)
return OmegaConf.create(original_config)
# We take separate arguments for the vqvae because the ITHQ vqvae config file
# is separate from the config file for the rest of the model.
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--vqvae_checkpoint_path",
default=None,
type=str,
required=True,
help="Path to the vqvae checkpoint to convert.",
)
parser.add_argument(
"--vqvae_original_config_file",
default=None,
type=str,
required=True,
help="The YAML config file corresponding to the original architecture for the vqvae.",
)
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
parser.add_argument(
"--original_config_file",
default=None,
type=str,
required=True,
help="The YAML config file corresponding to the original architecture.",
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--checkpoint_load_device",
default="cpu",
type=str,
required=False,
help="The device passed to `map_location` when loading checkpoints.",
)
# See link for how ema weights are always selected
# https://github.com/microsoft/VQ-Diffusion/blob/3c98e77f721db7c787b76304fa2c96a36c7b00af/inference_VQ_Diffusion.py#L65
parser.add_argument(
"--no_use_ema",
action="store_true",
required=False,
help=(
"Set to not use the ema weights from the original VQ-Diffusion checkpoint. You probably do not want to set"
" it as the original VQ-Diffusion always uses the ema weights when loading models."
),
)
args = parser.parse_args()
use_ema = not args.no_use_ema
print(f"loading checkpoints to {args.checkpoint_load_device}")
checkpoint_map_location = torch.device(args.checkpoint_load_device)
# vqvae_model
print(f"loading vqvae, config: {args.vqvae_original_config_file}, checkpoint: {args.vqvae_checkpoint_path}")
vqvae_original_config = read_config_file(args.vqvae_original_config_file).model
vqvae_checkpoint = torch.load(args.vqvae_checkpoint_path, map_location=checkpoint_map_location)["model"]
with init_empty_weights():
vqvae_model = vqvae_model_from_original_config(vqvae_original_config)
vqvae_diffusers_checkpoint = vqvae_original_checkpoint_to_diffusers_checkpoint(vqvae_model, vqvae_checkpoint)
with tempfile.NamedTemporaryFile() as vqvae_diffusers_checkpoint_file:
torch.save(vqvae_diffusers_checkpoint, vqvae_diffusers_checkpoint_file.name)
del vqvae_diffusers_checkpoint
del vqvae_checkpoint
load_checkpoint_and_dispatch(vqvae_model, vqvae_diffusers_checkpoint_file.name, device_map="auto")
print("done loading vqvae")
# done vqvae_model
# transformer_model
print(
f"loading transformer, config: {args.original_config_file}, checkpoint: {args.checkpoint_path}, use ema:"
f" {use_ema}"
)
original_config = read_config_file(args.original_config_file).model
diffusion_config = original_config.params.diffusion_config
transformer_config = original_config.params.diffusion_config.params.transformer_config
content_embedding_config = original_config.params.diffusion_config.params.content_emb_config
pre_checkpoint = torch.load(args.checkpoint_path, map_location=checkpoint_map_location)
if use_ema:
if "ema" in pre_checkpoint:
checkpoint = {}
for k, v in pre_checkpoint["model"].items():
checkpoint[k] = v
for k, v in pre_checkpoint["ema"].items():
# The ema weights are only used on the transformer. To mimic their key as if they came
# from the state_dict for the top level model, we prefix with an additional "transformer."
# See the source linked in the args.use_ema config for more information.
checkpoint[f"transformer.{k}"] = v
else:
print("attempted to load ema weights but no ema weights are specified in the loaded checkpoint.")
checkpoint = pre_checkpoint["model"]
else:
checkpoint = pre_checkpoint["model"]
del pre_checkpoint
with init_empty_weights():
transformer_model = transformer_model_from_original_config(
diffusion_config, transformer_config, content_embedding_config
)
diffusers_transformer_checkpoint = transformer_original_checkpoint_to_diffusers_checkpoint(
transformer_model, checkpoint
)
# classifier free sampling embeddings interlude
# The learned embeddings are stored on the transformer in the original VQ-diffusion. We store them on a separate
# model, so we pull them off the checkpoint before the checkpoint is deleted.
learnable_classifier_free_sampling_embeddings = diffusion_config.params.learnable_cf
if learnable_classifier_free_sampling_embeddings:
learned_classifier_free_sampling_embeddings_embeddings = checkpoint["transformer.empty_text_embed"]
else:
learned_classifier_free_sampling_embeddings_embeddings = None
# done classifier free sampling embeddings interlude
with tempfile.NamedTemporaryFile() as diffusers_transformer_checkpoint_file:
torch.save(diffusers_transformer_checkpoint, diffusers_transformer_checkpoint_file.name)
del diffusers_transformer_checkpoint
del checkpoint
load_checkpoint_and_dispatch(transformer_model, diffusers_transformer_checkpoint_file.name, device_map="auto")
print("done loading transformer")
# done transformer_model
# text encoder
print("loading CLIP text encoder")
clip_name = "openai/clip-vit-base-patch32"
# The original VQ-Diffusion specifies the pad value by the int used in the
# returned tokens. Each model uses `0` as the pad value. The transformers clip api
# specifies the pad value via the token before it has been tokenized. The `!` pad
# token is the same as padding with the `0` pad value.
pad_token = "!"
tokenizer_model = CLIPTokenizer.from_pretrained(clip_name, pad_token=pad_token, device_map="auto")
assert tokenizer_model.convert_tokens_to_ids(pad_token) == 0
text_encoder_model = CLIPTextModel.from_pretrained(
clip_name,
# `CLIPTextModel` does not support device_map="auto"
# device_map="auto"
)
print("done loading CLIP text encoder")
# done text encoder
# scheduler
scheduler_model = VQDiffusionScheduler(
# the scheduler has the same number of embeddings as the transformer
num_vec_classes=transformer_model.num_vector_embeds
)
# done scheduler
# learned classifier free sampling embeddings
with init_empty_weights():
learned_classifier_free_sampling_embeddings_model = LearnedClassifierFreeSamplingEmbeddings(
learnable_classifier_free_sampling_embeddings,
hidden_size=text_encoder_model.config.hidden_size,
length=tokenizer_model.model_max_length,
)
learned_classifier_free_sampling_checkpoint = {
"embeddings": learned_classifier_free_sampling_embeddings_embeddings.float()
}
with tempfile.NamedTemporaryFile() as learned_classifier_free_sampling_checkpoint_file:
torch.save(learned_classifier_free_sampling_checkpoint, learned_classifier_free_sampling_checkpoint_file.name)
del learned_classifier_free_sampling_checkpoint
del learned_classifier_free_sampling_embeddings_embeddings
load_checkpoint_and_dispatch(
learned_classifier_free_sampling_embeddings_model,
learned_classifier_free_sampling_checkpoint_file.name,
device_map="auto",
)
# done learned classifier free sampling embeddings
print(f"saving VQ diffusion model, path: {args.dump_path}")
pipe = VQDiffusionPipeline(
vqvae=vqvae_model,
transformer=transformer_model,
tokenizer=tokenizer_model,
text_encoder=text_encoder_model,
learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings_model,
scheduler=scheduler_model,
)
pipe.save_pretrained(args.dump_path)
print("done writing VQ diffusion model")
| diffusers-ft-main | scripts/convert_vq_diffusion_to_diffusers.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Conversion script for the Versatile Stable Diffusion checkpoints. """
import argparse
from argparse import Namespace
import torch
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNet2DConditionModel,
VersatileDiffusionPipeline,
)
from diffusers.pipelines.versatile_diffusion.modeling_text_unet import UNetFlatConditionModel
from transformers import (
CLIPFeatureExtractor,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionModelWithProjection,
)
SCHEDULER_CONFIG = Namespace(
**{
"beta_linear_start": 0.00085,
"beta_linear_end": 0.012,
"timesteps": 1000,
"scale_factor": 0.18215,
}
)
IMAGE_UNET_CONFIG = Namespace(
**{
"input_channels": 4,
"model_channels": 320,
"output_channels": 4,
"num_noattn_blocks": [2, 2, 2, 2],
"channel_mult": [1, 2, 4, 4],
"with_attn": [True, True, True, False],
"num_heads": 8,
"context_dim": 768,
"use_checkpoint": True,
}
)
TEXT_UNET_CONFIG = Namespace(
**{
"input_channels": 768,
"model_channels": 320,
"output_channels": 768,
"num_noattn_blocks": [2, 2, 2, 2],
"channel_mult": [1, 2, 4, 4],
"second_dim": [4, 4, 4, 4],
"with_attn": [True, True, True, False],
"num_heads": 8,
"context_dim": 768,
"use_checkpoint": True,
}
)
AUTOENCODER_CONFIG = Namespace(
**{
"double_z": True,
"z_channels": 4,
"resolution": 256,
"in_channels": 3,
"out_ch": 3,
"ch": 128,
"ch_mult": [1, 2, 4, 4],
"num_res_blocks": 2,
"attn_resolutions": [],
"dropout": 0.0,
}
)
def shave_segments(path, n_shave_prefix_segments=1):
"""
Removes segments. Positive values shave the first segments, negative shave the last segments.
"""
if n_shave_prefix_segments >= 0:
return ".".join(path.split(".")[n_shave_prefix_segments:])
else:
return ".".join(path.split(".")[:n_shave_prefix_segments])
def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside resnets to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item.replace("in_layers.0", "norm1")
new_item = new_item.replace("in_layers.2", "conv1")
new_item = new_item.replace("out_layers.0", "norm2")
new_item = new_item.replace("out_layers.3", "conv2")
new_item = new_item.replace("emb_layers.1", "time_emb_proj")
new_item = new_item.replace("skip_connection", "conv_shortcut")
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside resnets to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item
new_item = new_item.replace("nin_shortcut", "conv_shortcut")
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def renew_attention_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside attentions to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item
# new_item = new_item.replace('norm.weight', 'group_norm.weight')
# new_item = new_item.replace('norm.bias', 'group_norm.bias')
# new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
# new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
# new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside attentions to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item
new_item = new_item.replace("norm.weight", "group_norm.weight")
new_item = new_item.replace("norm.bias", "group_norm.bias")
new_item = new_item.replace("q.weight", "query.weight")
new_item = new_item.replace("q.bias", "query.bias")
new_item = new_item.replace("k.weight", "key.weight")
new_item = new_item.replace("k.bias", "key.bias")
new_item = new_item.replace("v.weight", "value.weight")
new_item = new_item.replace("v.bias", "value.bias")
new_item = new_item.replace("proj_out.weight", "proj_attn.weight")
new_item = new_item.replace("proj_out.bias", "proj_attn.bias")
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def assign_to_checkpoint(
paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None
):
"""
This does the final conversion step: take locally converted weights and apply a global renaming
to them. It splits attention layers, and takes into account additional replacements
that may arise.
Assigns the weights to the new checkpoint.
"""
assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
# Splits the attention layers into three variables.
if attention_paths_to_split is not None:
for path, path_map in attention_paths_to_split.items():
old_tensor = old_checkpoint[path]
channels = old_tensor.shape[0] // 3
target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)
num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
query, key, value = old_tensor.split(channels // num_heads, dim=1)
checkpoint[path_map["query"]] = query.reshape(target_shape)
checkpoint[path_map["key"]] = key.reshape(target_shape)
checkpoint[path_map["value"]] = value.reshape(target_shape)
for path in paths:
new_path = path["new"]
# These have already been assigned
if attention_paths_to_split is not None and new_path in attention_paths_to_split:
continue
# Global renaming happens here
new_path = new_path.replace("middle_block.0", "mid_block.resnets.0")
new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")
new_path = new_path.replace("middle_block.2", "mid_block.resnets.1")
if additional_replacements is not None:
for replacement in additional_replacements:
new_path = new_path.replace(replacement["old"], replacement["new"])
# proj_attn.weight has to be converted from conv 1D to linear
if "proj_attn.weight" in new_path:
checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]
elif path["old"] in old_checkpoint:
checkpoint[new_path] = old_checkpoint[path["old"]]
def conv_attn_to_linear(checkpoint):
keys = list(checkpoint.keys())
attn_keys = ["query.weight", "key.weight", "value.weight"]
for key in keys:
if ".".join(key.split(".")[-2:]) in attn_keys:
if checkpoint[key].ndim > 2:
checkpoint[key] = checkpoint[key][:, :, 0, 0]
elif "proj_attn.weight" in key:
if checkpoint[key].ndim > 2:
checkpoint[key] = checkpoint[key][:, :, 0]
def create_image_unet_diffusers_config(unet_params):
"""
Creates a config for the diffusers based on the config of the VD model.
"""
block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]
down_block_types = []
resolution = 1
for i in range(len(block_out_channels)):
block_type = "CrossAttnDownBlock2D" if unet_params.with_attn[i] else "DownBlock2D"
down_block_types.append(block_type)
if i != len(block_out_channels) - 1:
resolution *= 2
up_block_types = []
for i in range(len(block_out_channels)):
block_type = "CrossAttnUpBlock2D" if unet_params.with_attn[-i - 1] else "UpBlock2D"
up_block_types.append(block_type)
resolution //= 2
if not all(n == unet_params.num_noattn_blocks[0] for n in unet_params.num_noattn_blocks):
raise ValueError("Not all num_res_blocks are equal, which is not supported in this script.")
config = dict(
sample_size=None,
in_channels=unet_params.input_channels,
out_channels=unet_params.output_channels,
down_block_types=tuple(down_block_types),
up_block_types=tuple(up_block_types),
block_out_channels=tuple(block_out_channels),
layers_per_block=unet_params.num_noattn_blocks[0],
cross_attention_dim=unet_params.context_dim,
attention_head_dim=unet_params.num_heads,
)
return config
def create_text_unet_diffusers_config(unet_params):
"""
Creates a config for the diffusers based on the config of the VD model.
"""
block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]
down_block_types = []
resolution = 1
for i in range(len(block_out_channels)):
block_type = "CrossAttnDownBlockFlat" if unet_params.with_attn[i] else "DownBlockFlat"
down_block_types.append(block_type)
if i != len(block_out_channels) - 1:
resolution *= 2
up_block_types = []
for i in range(len(block_out_channels)):
block_type = "CrossAttnUpBlockFlat" if unet_params.with_attn[-i - 1] else "UpBlockFlat"
up_block_types.append(block_type)
resolution //= 2
if not all(n == unet_params.num_noattn_blocks[0] for n in unet_params.num_noattn_blocks):
raise ValueError("Not all num_res_blocks are equal, which is not supported in this script.")
config = dict(
sample_size=None,
in_channels=(unet_params.input_channels, 1, 1),
out_channels=(unet_params.output_channels, 1, 1),
down_block_types=tuple(down_block_types),
up_block_types=tuple(up_block_types),
block_out_channels=tuple(block_out_channels),
layers_per_block=unet_params.num_noattn_blocks[0],
cross_attention_dim=unet_params.context_dim,
attention_head_dim=unet_params.num_heads,
)
return config
def create_vae_diffusers_config(vae_params):
"""
Creates a config for the diffusers based on the config of the VD model.
"""
block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult]
down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels)
up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels)
config = dict(
sample_size=vae_params.resolution,
in_channels=vae_params.in_channels,
out_channels=vae_params.out_ch,
down_block_types=tuple(down_block_types),
up_block_types=tuple(up_block_types),
block_out_channels=tuple(block_out_channels),
latent_channels=vae_params.z_channels,
layers_per_block=vae_params.num_res_blocks,
)
return config
def create_diffusers_scheduler(original_config):
schedular = DDIMScheduler(
num_train_timesteps=original_config.model.params.timesteps,
beta_start=original_config.model.params.linear_start,
beta_end=original_config.model.params.linear_end,
beta_schedule="scaled_linear",
)
return schedular
def convert_vd_unet_checkpoint(checkpoint, config, unet_key, extract_ema=False):
"""
Takes a state dict and a config, and returns a converted checkpoint.
"""
# extract state_dict for UNet
unet_state_dict = {}
keys = list(checkpoint.keys())
# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
if sum(k.startswith("model_ema") for k in keys) > 100:
print("Checkpoint has both EMA and non-EMA weights.")
if extract_ema:
print(
"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."
)
for key in keys:
if key.startswith("model.diffusion_model"):
flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key)
else:
print(
"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
" weights (usually better for inference), please make sure to add the `--extract_ema` flag."
)
for key in keys:
if key.startswith(unet_key):
unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
new_checkpoint = {}
new_checkpoint["time_embedding.linear_1.weight"] = checkpoint["model.diffusion_model.time_embed.0.weight"]
new_checkpoint["time_embedding.linear_1.bias"] = checkpoint["model.diffusion_model.time_embed.0.bias"]
new_checkpoint["time_embedding.linear_2.weight"] = checkpoint["model.diffusion_model.time_embed.2.weight"]
new_checkpoint["time_embedding.linear_2.bias"] = checkpoint["model.diffusion_model.time_embed.2.bias"]
new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]
new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]
new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]
# Retrieves the keys for the input blocks only
num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer})
input_blocks = {
layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
for layer_id in range(num_input_blocks)
}
# Retrieves the keys for the middle blocks only
num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer})
middle_blocks = {
layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
for layer_id in range(num_middle_blocks)
}
# Retrieves the keys for the output blocks only
num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer})
output_blocks = {
layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
for layer_id in range(num_output_blocks)
}
for i in range(1, num_input_blocks):
block_id = (i - 1) // (config["layers_per_block"] + 1)
layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)
resnets = [
key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
]
attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop(
f"input_blocks.{i}.0.op.weight"
)
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(
f"input_blocks.{i}.0.op.bias"
)
elif f"input_blocks.{i}.0.weight" in unet_state_dict:
# text_unet uses linear layers in place of downsamplers
shape = unet_state_dict[f"input_blocks.{i}.0.weight"].shape
if shape[0] != shape[1]:
continue
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.weight"] = unet_state_dict.pop(
f"input_blocks.{i}.0.weight"
)
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.bias"] = unet_state_dict.pop(
f"input_blocks.{i}.0.bias"
)
paths = renew_resnet_paths(resnets)
meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
if len(attentions):
paths = renew_attention_paths(attentions)
meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
resnet_0 = middle_blocks[0]
attentions = middle_blocks[1]
resnet_1 = middle_blocks[2]
resnet_0_paths = renew_resnet_paths(resnet_0)
assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)
resnet_1_paths = renew_resnet_paths(resnet_1)
assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)
attentions_paths = renew_attention_paths(attentions)
meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(
attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
for i in range(num_output_blocks):
block_id = i // (config["layers_per_block"] + 1)
layer_in_block_id = i % (config["layers_per_block"] + 1)
output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
output_block_list = {}
for layer in output_block_layers:
layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)
if layer_id in output_block_list:
output_block_list[layer_id].append(layer_name)
else:
output_block_list[layer_id] = [layer_name]
if len(output_block_list) > 1:
resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
paths = renew_resnet_paths(resnets)
meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
if ["conv.weight", "conv.bias"] in output_block_list.values():
index = list(output_block_list.values()).index(["conv.weight", "conv.bias"])
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
f"output_blocks.{i}.{index}.conv.weight"
]
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
f"output_blocks.{i}.{index}.conv.bias"
]
# Clear attentions as they have been attributed above.
if len(attentions) == 2:
attentions = []
elif f"output_blocks.{i}.1.weight" in unet_state_dict:
# text_unet uses linear layers in place of upsamplers
shape = unet_state_dict[f"output_blocks.{i}.1.weight"].shape
if shape[0] != shape[1]:
continue
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.weight"] = unet_state_dict.pop(
f"output_blocks.{i}.1.weight"
)
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.bias"] = unet_state_dict.pop(
f"output_blocks.{i}.1.bias"
)
# Clear attentions as they have been attributed above.
if len(attentions) == 2:
attentions = []
elif f"output_blocks.{i}.2.weight" in unet_state_dict:
# text_unet uses linear layers in place of upsamplers
shape = unet_state_dict[f"output_blocks.{i}.2.weight"].shape
if shape[0] != shape[1]:
continue
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.weight"] = unet_state_dict.pop(
f"output_blocks.{i}.2.weight"
)
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.bias"] = unet_state_dict.pop(
f"output_blocks.{i}.2.bias"
)
if len(attentions):
paths = renew_attention_paths(attentions)
meta_path = {
"old": f"output_blocks.{i}.1",
"new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
else:
resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)
for path in resnet_0_paths:
old_path = ".".join(["output_blocks", str(i), path["old"]])
new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]])
new_checkpoint[new_path] = unet_state_dict[old_path]
return new_checkpoint
def convert_vd_vae_checkpoint(checkpoint, config):
# extract state dict for VAE
vae_state_dict = {}
keys = list(checkpoint.keys())
for key in keys:
vae_state_dict[key] = checkpoint.get(key)
new_checkpoint = {}
new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"]
new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"]
new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"]
new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"]
new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"]
new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"]
new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
# Retrieves the keys for the encoder down blocks only
num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
down_blocks = {
layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
}
# Retrieves the keys for the decoder up blocks only
num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
up_blocks = {
layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)
}
for i in range(num_down_blocks):
resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
f"encoder.down.{i}.downsample.conv.weight"
)
new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
f"encoder.down.{i}.downsample.conv.bias"
)
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
num_mid_res_blocks = 2
for i in range(1, num_mid_res_blocks + 1):
resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
paths = renew_vae_attention_paths(mid_attentions)
meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
conv_attn_to_linear(new_checkpoint)
for i in range(num_up_blocks):
block_id = num_up_blocks - 1 - i
resnets = [
key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
]
if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
f"decoder.up.{block_id}.upsample.conv.weight"
]
new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
f"decoder.up.{block_id}.upsample.conv.bias"
]
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
num_mid_res_blocks = 2
for i in range(1, num_mid_res_blocks + 1):
resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
paths = renew_vae_attention_paths(mid_attentions)
meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
conv_attn_to_linear(new_checkpoint)
return new_checkpoint
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--unet_checkpoint_path", default=None, type=str, required=False, help="Path to the checkpoint to convert."
)
parser.add_argument(
"--vae_checkpoint_path", default=None, type=str, required=False, help="Path to the checkpoint to convert."
)
parser.add_argument(
"--optimus_checkpoint_path", default=None, type=str, required=False, help="Path to the checkpoint to convert."
)
parser.add_argument(
"--scheduler_type",
default="pndm",
type=str,
help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancest', 'dpm']",
)
parser.add_argument(
"--extract_ema",
action="store_true",
help=(
"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
),
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
args = parser.parse_args()
scheduler_config = SCHEDULER_CONFIG
num_train_timesteps = scheduler_config.timesteps
beta_start = scheduler_config.beta_linear_start
beta_end = scheduler_config.beta_linear_end
if args.scheduler_type == "pndm":
scheduler = PNDMScheduler(
beta_end=beta_end,
beta_schedule="scaled_linear",
beta_start=beta_start,
num_train_timesteps=num_train_timesteps,
skip_prk_steps=True,
steps_offset=1,
)
elif args.scheduler_type == "lms":
scheduler = LMSDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear")
elif args.scheduler_type == "euler":
scheduler = EulerDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear")
elif args.scheduler_type == "euler-ancestral":
scheduler = EulerAncestralDiscreteScheduler(
beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear"
)
elif args.scheduler_type == "dpm":
scheduler = DPMSolverMultistepScheduler(
beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear"
)
elif args.scheduler_type == "ddim":
scheduler = DDIMScheduler(
beta_start=beta_start,
beta_end=beta_end,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
steps_offset=1,
)
else:
raise ValueError(f"Scheduler of type {args.scheduler_type} doesn't exist!")
# Convert the UNet2DConditionModel models.
if args.unet_checkpoint_path is not None:
# image UNet
image_unet_config = create_image_unet_diffusers_config(IMAGE_UNET_CONFIG)
checkpoint = torch.load(args.unet_checkpoint_path)
converted_image_unet_checkpoint = convert_vd_unet_checkpoint(
checkpoint, image_unet_config, unet_key="model.diffusion_model.unet_image.", extract_ema=args.extract_ema
)
image_unet = UNet2DConditionModel(**image_unet_config)
image_unet.load_state_dict(converted_image_unet_checkpoint)
# text UNet
text_unet_config = create_text_unet_diffusers_config(TEXT_UNET_CONFIG)
converted_text_unet_checkpoint = convert_vd_unet_checkpoint(
checkpoint, text_unet_config, unet_key="model.diffusion_model.unet_text.", extract_ema=args.extract_ema
)
text_unet = UNetFlatConditionModel(**text_unet_config)
text_unet.load_state_dict(converted_text_unet_checkpoint)
# Convert the VAE model.
if args.vae_checkpoint_path is not None:
vae_config = create_vae_diffusers_config(AUTOENCODER_CONFIG)
checkpoint = torch.load(args.vae_checkpoint_path)
converted_vae_checkpoint = convert_vd_vae_checkpoint(checkpoint, vae_config)
vae = AutoencoderKL(**vae_config)
vae.load_state_dict(converted_vae_checkpoint)
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
image_feature_extractor = CLIPFeatureExtractor.from_pretrained("openai/clip-vit-large-patch14")
text_encoder = CLIPTextModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
pipe = VersatileDiffusionPipeline(
scheduler=scheduler,
tokenizer=tokenizer,
image_feature_extractor=image_feature_extractor,
text_encoder=text_encoder,
image_encoder=image_encoder,
image_unet=image_unet,
text_unet=text_unet,
vae=vae,
)
pipe.save_pretrained(args.dump_path)
| diffusers-ft-main | scripts/convert_versatile_diffusion_to_diffusers.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Conversion script for the LDM checkpoints. """
import argparse
import os
import torch
try:
from omegaconf import OmegaConf
except ImportError:
raise ImportError(
"OmegaConf is required to convert the LDM checkpoints. Please install it with `pip install OmegaConf`."
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LDMTextToImagePipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNet2DConditionModel,
)
from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextModel, CLIPTokenizer
def shave_segments(path, n_shave_prefix_segments=1):
"""
Removes segments. Positive values shave the first segments, negative shave the last segments.
"""
if n_shave_prefix_segments >= 0:
return ".".join(path.split(".")[n_shave_prefix_segments:])
else:
return ".".join(path.split(".")[:n_shave_prefix_segments])
def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside resnets to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item.replace("in_layers.0", "norm1")
new_item = new_item.replace("in_layers.2", "conv1")
new_item = new_item.replace("out_layers.0", "norm2")
new_item = new_item.replace("out_layers.3", "conv2")
new_item = new_item.replace("emb_layers.1", "time_emb_proj")
new_item = new_item.replace("skip_connection", "conv_shortcut")
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside resnets to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item
new_item = new_item.replace("nin_shortcut", "conv_shortcut")
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def renew_attention_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside attentions to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item
# new_item = new_item.replace('norm.weight', 'group_norm.weight')
# new_item = new_item.replace('norm.bias', 'group_norm.bias')
# new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
# new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
# new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside attentions to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item
new_item = new_item.replace("norm.weight", "group_norm.weight")
new_item = new_item.replace("norm.bias", "group_norm.bias")
new_item = new_item.replace("q.weight", "query.weight")
new_item = new_item.replace("q.bias", "query.bias")
new_item = new_item.replace("k.weight", "key.weight")
new_item = new_item.replace("k.bias", "key.bias")
new_item = new_item.replace("v.weight", "value.weight")
new_item = new_item.replace("v.bias", "value.bias")
new_item = new_item.replace("proj_out.weight", "proj_attn.weight")
new_item = new_item.replace("proj_out.bias", "proj_attn.bias")
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def assign_to_checkpoint(
paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None
):
"""
This does the final conversion step: take locally converted weights and apply a global renaming
to them. It splits attention layers, and takes into account additional replacements
that may arise.
Assigns the weights to the new checkpoint.
"""
assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
# Splits the attention layers into three variables.
if attention_paths_to_split is not None:
for path, path_map in attention_paths_to_split.items():
old_tensor = old_checkpoint[path]
channels = old_tensor.shape[0] // 3
target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)
num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
query, key, value = old_tensor.split(channels // num_heads, dim=1)
checkpoint[path_map["query"]] = query.reshape(target_shape)
checkpoint[path_map["key"]] = key.reshape(target_shape)
checkpoint[path_map["value"]] = value.reshape(target_shape)
for path in paths:
new_path = path["new"]
# These have already been assigned
if attention_paths_to_split is not None and new_path in attention_paths_to_split:
continue
# Global renaming happens here
new_path = new_path.replace("middle_block.0", "mid_block.resnets.0")
new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")
new_path = new_path.replace("middle_block.2", "mid_block.resnets.1")
if additional_replacements is not None:
for replacement in additional_replacements:
new_path = new_path.replace(replacement["old"], replacement["new"])
# proj_attn.weight has to be converted from conv 1D to linear
if "proj_attn.weight" in new_path:
checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]
else:
checkpoint[new_path] = old_checkpoint[path["old"]]
def conv_attn_to_linear(checkpoint):
keys = list(checkpoint.keys())
attn_keys = ["query.weight", "key.weight", "value.weight"]
for key in keys:
if ".".join(key.split(".")[-2:]) in attn_keys:
if checkpoint[key].ndim > 2:
checkpoint[key] = checkpoint[key][:, :, 0, 0]
elif "proj_attn.weight" in key:
if checkpoint[key].ndim > 2:
checkpoint[key] = checkpoint[key][:, :, 0]
def create_unet_diffusers_config(original_config):
"""
Creates a config for the diffusers based on the config of the LDM model.
"""
model_params = original_config.model.params
unet_params = original_config.model.params.unet_config.params
block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]
down_block_types = []
resolution = 1
for i in range(len(block_out_channels)):
block_type = "CrossAttnDownBlock2D" if resolution in unet_params.attention_resolutions else "DownBlock2D"
down_block_types.append(block_type)
if i != len(block_out_channels) - 1:
resolution *= 2
up_block_types = []
for i in range(len(block_out_channels)):
block_type = "CrossAttnUpBlock2D" if resolution in unet_params.attention_resolutions else "UpBlock2D"
up_block_types.append(block_type)
resolution //= 2
config = dict(
sample_size=model_params.image_size,
in_channels=unet_params.in_channels,
out_channels=unet_params.out_channels,
down_block_types=tuple(down_block_types),
up_block_types=tuple(up_block_types),
block_out_channels=tuple(block_out_channels),
layers_per_block=unet_params.num_res_blocks,
cross_attention_dim=unet_params.context_dim,
attention_head_dim=unet_params.num_heads,
)
return config
def create_vae_diffusers_config(original_config):
"""
Creates a config for the diffusers based on the config of the LDM model.
"""
vae_params = original_config.model.params.first_stage_config.params.ddconfig
_ = original_config.model.params.first_stage_config.params.embed_dim
block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult]
down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels)
up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels)
config = dict(
sample_size=vae_params.resolution,
in_channels=vae_params.in_channels,
out_channels=vae_params.out_ch,
down_block_types=tuple(down_block_types),
up_block_types=tuple(up_block_types),
block_out_channels=tuple(block_out_channels),
latent_channels=vae_params.z_channels,
layers_per_block=vae_params.num_res_blocks,
)
return config
def create_diffusers_schedular(original_config):
schedular = DDIMScheduler(
num_train_timesteps=original_config.model.params.timesteps,
beta_start=original_config.model.params.linear_start,
beta_end=original_config.model.params.linear_end,
beta_schedule="scaled_linear",
)
return schedular
def create_ldm_bert_config(original_config):
bert_params = original_config.model.parms.cond_stage_config.params
config = LDMBertConfig(
d_model=bert_params.n_embed,
encoder_layers=bert_params.n_layer,
encoder_ffn_dim=bert_params.n_embed * 4,
)
return config
def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False):
"""
Takes a state dict and a config, and returns a converted checkpoint.
"""
# extract state_dict for UNet
unet_state_dict = {}
keys = list(checkpoint.keys())
unet_key = "model.diffusion_model."
# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
if sum(k.startswith("model_ema") for k in keys) > 100:
print(f"Checkpoint {path} has both EMA and non-EMA weights.")
if extract_ema:
print(
"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."
)
for key in keys:
if key.startswith("model.diffusion_model"):
flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key)
else:
print(
"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
" weights (usually better for inference), please make sure to add the `--extract_ema` flag."
)
for key in keys:
if key.startswith(unet_key):
unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
new_checkpoint = {}
new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"]
new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"]
new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"]
new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"]
new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]
new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]
new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]
# Retrieves the keys for the input blocks only
num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer})
input_blocks = {
layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
for layer_id in range(num_input_blocks)
}
# Retrieves the keys for the middle blocks only
num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer})
middle_blocks = {
layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
for layer_id in range(num_middle_blocks)
}
# Retrieves the keys for the output blocks only
num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer})
output_blocks = {
layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
for layer_id in range(num_output_blocks)
}
for i in range(1, num_input_blocks):
block_id = (i - 1) // (config["layers_per_block"] + 1)
layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)
resnets = [
key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
]
attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop(
f"input_blocks.{i}.0.op.weight"
)
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(
f"input_blocks.{i}.0.op.bias"
)
paths = renew_resnet_paths(resnets)
meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
if len(attentions):
paths = renew_attention_paths(attentions)
meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
resnet_0 = middle_blocks[0]
attentions = middle_blocks[1]
resnet_1 = middle_blocks[2]
resnet_0_paths = renew_resnet_paths(resnet_0)
assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)
resnet_1_paths = renew_resnet_paths(resnet_1)
assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)
attentions_paths = renew_attention_paths(attentions)
meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(
attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
for i in range(num_output_blocks):
block_id = i // (config["layers_per_block"] + 1)
layer_in_block_id = i % (config["layers_per_block"] + 1)
output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
output_block_list = {}
for layer in output_block_layers:
layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)
if layer_id in output_block_list:
output_block_list[layer_id].append(layer_name)
else:
output_block_list[layer_id] = [layer_name]
if len(output_block_list) > 1:
resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
resnet_0_paths = renew_resnet_paths(resnets)
paths = renew_resnet_paths(resnets)
meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
if ["conv.weight", "conv.bias"] in output_block_list.values():
index = list(output_block_list.values()).index(["conv.weight", "conv.bias"])
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
f"output_blocks.{i}.{index}.conv.weight"
]
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
f"output_blocks.{i}.{index}.conv.bias"
]
# Clear attentions as they have been attributed above.
if len(attentions) == 2:
attentions = []
if len(attentions):
paths = renew_attention_paths(attentions)
meta_path = {
"old": f"output_blocks.{i}.1",
"new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
else:
resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)
for path in resnet_0_paths:
old_path = ".".join(["output_blocks", str(i), path["old"]])
new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]])
new_checkpoint[new_path] = unet_state_dict[old_path]
return new_checkpoint
def convert_ldm_vae_checkpoint(checkpoint, config):
# extract state dict for VAE
vae_state_dict = {}
vae_key = "first_stage_model."
keys = list(checkpoint.keys())
for key in keys:
if key.startswith(vae_key):
vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
new_checkpoint = {}
new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"]
new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"]
new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"]
new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"]
new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"]
new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"]
new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
# Retrieves the keys for the encoder down blocks only
num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
down_blocks = {
layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
}
# Retrieves the keys for the decoder up blocks only
num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
up_blocks = {
layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)
}
for i in range(num_down_blocks):
resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
f"encoder.down.{i}.downsample.conv.weight"
)
new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
f"encoder.down.{i}.downsample.conv.bias"
)
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
num_mid_res_blocks = 2
for i in range(1, num_mid_res_blocks + 1):
resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
paths = renew_vae_attention_paths(mid_attentions)
meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
conv_attn_to_linear(new_checkpoint)
for i in range(num_up_blocks):
block_id = num_up_blocks - 1 - i
resnets = [
key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
]
if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
f"decoder.up.{block_id}.upsample.conv.weight"
]
new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
f"decoder.up.{block_id}.upsample.conv.bias"
]
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
num_mid_res_blocks = 2
for i in range(1, num_mid_res_blocks + 1):
resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
paths = renew_vae_attention_paths(mid_attentions)
meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
conv_attn_to_linear(new_checkpoint)
return new_checkpoint
def convert_ldm_bert_checkpoint(checkpoint, config):
def _copy_attn_layer(hf_attn_layer, pt_attn_layer):
hf_attn_layer.q_proj.weight.data = pt_attn_layer.to_q.weight
hf_attn_layer.k_proj.weight.data = pt_attn_layer.to_k.weight
hf_attn_layer.v_proj.weight.data = pt_attn_layer.to_v.weight
hf_attn_layer.out_proj.weight = pt_attn_layer.to_out.weight
hf_attn_layer.out_proj.bias = pt_attn_layer.to_out.bias
def _copy_linear(hf_linear, pt_linear):
hf_linear.weight = pt_linear.weight
hf_linear.bias = pt_linear.bias
def _copy_layer(hf_layer, pt_layer):
# copy layer norms
_copy_linear(hf_layer.self_attn_layer_norm, pt_layer[0][0])
_copy_linear(hf_layer.final_layer_norm, pt_layer[1][0])
# copy attn
_copy_attn_layer(hf_layer.self_attn, pt_layer[0][1])
# copy MLP
pt_mlp = pt_layer[1][1]
_copy_linear(hf_layer.fc1, pt_mlp.net[0][0])
_copy_linear(hf_layer.fc2, pt_mlp.net[2])
def _copy_layers(hf_layers, pt_layers):
for i, hf_layer in enumerate(hf_layers):
if i != 0:
i += i
pt_layer = pt_layers[i : i + 2]
_copy_layer(hf_layer, pt_layer)
hf_model = LDMBertModel(config).eval()
# copy embeds
hf_model.model.embed_tokens.weight = checkpoint.transformer.token_emb.weight
hf_model.model.embed_positions.weight.data = checkpoint.transformer.pos_emb.emb.weight
# copy layer norm
_copy_linear(hf_model.model.layer_norm, checkpoint.transformer.norm)
# copy hidden layers
_copy_layers(hf_model.model.layers, checkpoint.transformer.attn_layers.layers)
_copy_linear(hf_model.to_logits, checkpoint.transformer.to_logits)
return hf_model
def convert_ldm_clip_checkpoint(checkpoint):
text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14")
keys = list(checkpoint.keys())
text_model_dict = {}
for key in keys:
if key.startswith("cond_stage_model.transformer"):
text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key]
text_model.load_state_dict(text_model_dict)
return text_model
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
parser.add_argument(
"--original_config_file",
default=None,
type=str,
help="The YAML config file corresponding to the original architecture.",
)
parser.add_argument(
"--scheduler_type",
default="pndm",
type=str,
help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancest', 'dpm']",
)
parser.add_argument(
"--extract_ema",
action="store_true",
help=(
"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
),
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
args = parser.parse_args()
if args.original_config_file is None:
os.system(
"wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
)
args.original_config_file = "./v1-inference.yaml"
original_config = OmegaConf.load(args.original_config_file)
checkpoint = torch.load(args.checkpoint_path)
checkpoint = checkpoint["state_dict"]
num_train_timesteps = original_config.model.params.timesteps
beta_start = original_config.model.params.linear_start
beta_end = original_config.model.params.linear_end
if args.scheduler_type == "pndm":
scheduler = PNDMScheduler(
beta_end=beta_end,
beta_schedule="scaled_linear",
beta_start=beta_start,
num_train_timesteps=num_train_timesteps,
skip_prk_steps=True,
)
elif args.scheduler_type == "lms":
scheduler = LMSDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear")
elif args.scheduler_type == "euler":
scheduler = EulerDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear")
elif args.scheduler_type == "euler-ancestral":
scheduler = EulerAncestralDiscreteScheduler(
beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear"
)
elif args.scheduler_type == "dpm":
scheduler = DPMSolverMultistepScheduler(
beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear"
)
elif args.scheduler_type == "ddim":
scheduler = DDIMScheduler(
beta_start=beta_start,
beta_end=beta_end,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
else:
raise ValueError(f"Scheduler of type {args.scheduler_type} doesn't exist!")
# Convert the UNet2DConditionModel model.
unet_config = create_unet_diffusers_config(original_config)
converted_unet_checkpoint = convert_ldm_unet_checkpoint(
checkpoint, unet_config, path=args.checkpoint_path, extract_ema=args.extract_ema
)
unet = UNet2DConditionModel(**unet_config)
unet.load_state_dict(converted_unet_checkpoint)
# Convert the VAE model.
vae_config = create_vae_diffusers_config(original_config)
converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
vae = AutoencoderKL(**vae_config)
vae.load_state_dict(converted_vae_checkpoint)
# Convert the text model.
text_model_type = original_config.model.params.cond_stage_config.target.split(".")[-1]
if text_model_type == "FrozenCLIPEmbedder":
text_model = convert_ldm_clip_checkpoint(checkpoint)
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")
feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker")
pipe = StableDiffusionPipeline(
vae=vae,
text_encoder=text_model,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
else:
text_config = create_ldm_bert_config(original_config)
text_model = convert_ldm_bert_checkpoint(checkpoint, text_config)
tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
pipe.save_pretrained(args.dump_path)
| diffusers-ft-main | scripts/convert_original_stable_diffusion_to_diffusers.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Conversion script for the LDM checkpoints. """
import argparse
import json
import os
import torch
from diffusers import UNet2DConditionModel, UNet2DModel
from transformers.file_utils import has_file
do_only_config = False
do_only_weights = True
do_only_renaming = False
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--repo_path",
default=None,
type=str,
required=True,
help="The config json file corresponding to the architecture.",
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
args = parser.parse_args()
config_parameters_to_change = {
"image_size": "sample_size",
"num_res_blocks": "layers_per_block",
"block_channels": "block_out_channels",
"down_blocks": "down_block_types",
"up_blocks": "up_block_types",
"downscale_freq_shift": "freq_shift",
"resnet_num_groups": "norm_num_groups",
"resnet_act_fn": "act_fn",
"resnet_eps": "norm_eps",
"num_head_channels": "attention_head_dim",
}
key_parameters_to_change = {
"time_steps": "time_proj",
"mid": "mid_block",
"downsample_blocks": "down_blocks",
"upsample_blocks": "up_blocks",
}
subfolder = "" if has_file(args.repo_path, "config.json") else "unet"
with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
text = reader.read()
config = json.loads(text)
if do_only_config:
for key in config_parameters_to_change.keys():
config.pop(key, None)
if has_file(args.repo_path, "config.json"):
model = UNet2DModel(**config)
else:
class_name = UNet2DConditionModel if "ldm-text2im-large-256" in args.repo_path else UNet2DModel
model = class_name(**config)
if do_only_config:
model.save_config(os.path.join(args.repo_path, subfolder))
config = dict(model.config)
if do_only_renaming:
for key, value in config_parameters_to_change.items():
if key in config:
config[value] = config[key]
del config[key]
config["down_block_types"] = [k.replace("UNetRes", "") for k in config["down_block_types"]]
config["up_block_types"] = [k.replace("UNetRes", "") for k in config["up_block_types"]]
if do_only_weights:
state_dict = torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))
new_state_dict = {}
for param_key, param_value in state_dict.items():
if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
continue
has_changed = False
for key, new_key in key_parameters_to_change.items():
if not has_changed and param_key.split(".")[0] == key:
new_state_dict[".".join([new_key] + param_key.split(".")[1:])] = param_value
has_changed = True
if not has_changed:
new_state_dict[param_key] = param_value
model.load_state_dict(new_state_dict)
model.save_pretrained(os.path.join(args.repo_path, subfolder))
| diffusers-ft-main | scripts/change_naming_configs_and_checkpoints.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" ConfigMixin base class and utilities."""
import dataclasses
import functools
import importlib
import inspect
import json
import os
import re
from collections import OrderedDict
from typing import Any, Dict, Tuple, Union
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
from requests import HTTPError
from . import __version__
from .utils import DIFFUSERS_CACHE, HUGGINGFACE_CO_RESOLVE_ENDPOINT, DummyObject, deprecate, logging
logger = logging.get_logger(__name__)
_re_configuration_file = re.compile(r"config\.(.*)\.json")
class FrozenDict(OrderedDict):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
for key, value in self.items():
setattr(self, key, value)
self.__frozen = True
def __delitem__(self, *args, **kwargs):
raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")
def setdefault(self, *args, **kwargs):
raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.")
def pop(self, *args, **kwargs):
raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.")
def update(self, *args, **kwargs):
raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.")
def __setattr__(self, name, value):
if hasattr(self, "__frozen") and self.__frozen:
raise Exception(f"You cannot use ``__setattr__`` on a {self.__class__.__name__} instance.")
super().__setattr__(name, value)
def __setitem__(self, name, value):
if hasattr(self, "__frozen") and self.__frozen:
raise Exception(f"You cannot use ``__setattr__`` on a {self.__class__.__name__} instance.")
super().__setitem__(name, value)
class ConfigMixin:
r"""
Base class for all configuration classes. Stores all configuration parameters under `self.config` Also handles all
methods for loading/downloading/saving classes inheriting from [`ConfigMixin`] with
- [`~ConfigMixin.from_config`]
- [`~ConfigMixin.save_config`]
Class attributes:
- **config_name** (`str`) -- A filename under which the config should stored when calling
[`~ConfigMixin.save_config`] (should be overridden by parent class).
- **ignore_for_config** (`List[str]`) -- A list of attributes that should not be saved in the config (should be
overridden by subclass).
- **has_compatibles** (`bool`) -- Whether the class has compatible classes (should be overridden by subclass).
- **_deprecated_kwargs** (`List[str]`) -- Keyword arguments that are deprecated. Note that the init function
should only have a `kwargs` argument if at least one argument is deprecated (should be overridden by
subclass).
"""
config_name = None
ignore_for_config = []
has_compatibles = False
_deprecated_kwargs = []
def register_to_config(self, **kwargs):
if self.config_name is None:
raise NotImplementedError(f"Make sure that {self.__class__} has defined a class name `config_name`")
# Special case for `kwargs` used in deprecation warning added to schedulers
# TODO: remove this when we remove the deprecation warning, and the `kwargs` argument,
# or solve in a more general way.
kwargs.pop("kwargs", None)
for key, value in kwargs.items():
try:
setattr(self, key, value)
except AttributeError as err:
logger.error(f"Can't set {key} with value {value} for {self}")
raise err
if not hasattr(self, "_internal_dict"):
internal_dict = kwargs
else:
previous_dict = dict(self._internal_dict)
internal_dict = {**self._internal_dict, **kwargs}
logger.debug(f"Updating config from {previous_dict} to {internal_dict}")
self._internal_dict = FrozenDict(internal_dict)
def save_config(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):
"""
Save a configuration object to the directory `save_directory`, so that it can be re-loaded using the
[`~ConfigMixin.from_config`] class method.
Args:
save_directory (`str` or `os.PathLike`):
Directory where the configuration JSON file will be saved (will be created if it does not exist).
"""
if os.path.isfile(save_directory):
raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")
os.makedirs(save_directory, exist_ok=True)
# If we save using the predefined names, we can load using `from_config`
output_config_file = os.path.join(save_directory, self.config_name)
self.to_json_file(output_config_file)
logger.info(f"Configuration saved in {output_config_file}")
@classmethod
def from_config(cls, config: Union[FrozenDict, Dict[str, Any]] = None, return_unused_kwargs=False, **kwargs):
r"""
Instantiate a Python class from a config dictionary
Parameters:
config (`Dict[str, Any]`):
A config dictionary from which the Python class will be instantiated. Make sure to only load
configuration files of compatible classes.
return_unused_kwargs (`bool`, *optional*, defaults to `False`):
Whether kwargs that are not consumed by the Python class should be returned or not.
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to update the configuration object (after it being loaded) and initiate the Python class.
`**kwargs` will be directly passed to the underlying scheduler/model's `__init__` method and eventually
overwrite same named arguments of `config`.
Examples:
```python
>>> from diffusers import DDPMScheduler, DDIMScheduler, PNDMScheduler
>>> # Download scheduler from huggingface.co and cache.
>>> scheduler = DDPMScheduler.from_pretrained("google/ddpm-cifar10-32")
>>> # Instantiate DDIM scheduler class with same config as DDPM
>>> scheduler = DDIMScheduler.from_config(scheduler.config)
>>> # Instantiate PNDM scheduler class with same config as DDPM
>>> scheduler = PNDMScheduler.from_config(scheduler.config)
```
"""
# <===== TO BE REMOVED WITH DEPRECATION
# TODO(Patrick) - make sure to remove the following lines when config=="model_path" is deprecated
if "pretrained_model_name_or_path" in kwargs:
config = kwargs.pop("pretrained_model_name_or_path")
if config is None:
raise ValueError("Please make sure to provide a config as the first positional argument.")
# ======>
if not isinstance(config, dict):
deprecation_message = "It is deprecated to pass a pretrained model name or path to `from_config`."
if "Scheduler" in cls.__name__:
deprecation_message += (
f"If you were trying to load a scheduler, please use {cls}.from_pretrained(...) instead."
" Otherwise, please make sure to pass a configuration dictionary instead. This functionality will"
" be removed in v1.0.0."
)
elif "Model" in cls.__name__:
deprecation_message += (
f"If you were trying to load a model, please use {cls}.load_config(...) followed by"
f" {cls}.from_config(...) instead. Otherwise, please make sure to pass a configuration dictionary"
" instead. This functionality will be removed in v1.0.0."
)
deprecate("config-passed-as-path", "1.0.0", deprecation_message, standard_warn=False)
config, kwargs = cls.load_config(pretrained_model_name_or_path=config, return_unused_kwargs=True, **kwargs)
init_dict, unused_kwargs, hidden_dict = cls.extract_init_dict(config, **kwargs)
# Allow dtype to be specified on initialization
if "dtype" in unused_kwargs:
init_dict["dtype"] = unused_kwargs.pop("dtype")
# add possible deprecated kwargs
for deprecated_kwarg in cls._deprecated_kwargs:
if deprecated_kwarg in unused_kwargs:
init_dict[deprecated_kwarg] = unused_kwargs.pop(deprecated_kwarg)
# Return model and optionally state and/or unused_kwargs
model = cls(**init_dict)
# make sure to also save config parameters that might be used for compatible classes
model.register_to_config(**hidden_dict)
# add hidden kwargs of compatible classes to unused_kwargs
unused_kwargs = {**unused_kwargs, **hidden_dict}
if return_unused_kwargs:
return (model, unused_kwargs)
else:
return model
@classmethod
def get_config_dict(cls, *args, **kwargs):
deprecation_message = (
f" The function get_config_dict is deprecated. Please use {cls}.load_config instead. This function will be"
" removed in version v1.0.0"
)
deprecate("get_config_dict", "1.0.0", deprecation_message, standard_warn=False)
return cls.load_config(*args, **kwargs)
@classmethod
def load_config(
cls, pretrained_model_name_or_path: Union[str, os.PathLike], return_unused_kwargs=False, **kwargs
) -> Tuple[Dict[str, Any], Dict[str, Any]]:
r"""
Instantiate a Python class from a config dictionary
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
Can be either:
- A string, the *model id* of a model repo on huggingface.co. Valid model ids should have an
organization name, like `google/ddpm-celebahq-256`.
- A path to a *directory* containing model weights saved using [`~ConfigMixin.save_config`], e.g.,
`./my_model_directory/`.
cache_dir (`Union[str, os.PathLike]`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
output_loading_info(`bool`, *optional*, defaults to `False`):
Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
local_files_only(`bool`, *optional*, defaults to `False`):
Whether or not to only look at local files (i.e., do not try to download the model).
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `transformers-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
subfolder (`str`, *optional*, defaults to `""`):
In case the relevant files are located inside a subfolder of the model repo (either remote in
huggingface.co or downloaded locally), you can specify the folder name here.
<Tip>
It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
models](https://huggingface.co/docs/hub/models-gated#gated-models).
</Tip>
<Tip>
Activate the special ["offline-mode"](https://huggingface.co/transformers/installation.html#offline-mode) to
use this method in a firewalled environment.
</Tip>
"""
cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
use_auth_token = kwargs.pop("use_auth_token", None)
local_files_only = kwargs.pop("local_files_only", False)
revision = kwargs.pop("revision", None)
_ = kwargs.pop("mirror", None)
subfolder = kwargs.pop("subfolder", None)
user_agent = {"file_type": "config"}
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
if cls.config_name is None:
raise ValueError(
"`self.config_name` is not defined. Note that one should not load a config from "
"`ConfigMixin`. Please make sure to define `config_name` in a class inheriting from `ConfigMixin`"
)
if os.path.isfile(pretrained_model_name_or_path):
config_file = pretrained_model_name_or_path
elif os.path.isdir(pretrained_model_name_or_path):
if os.path.isfile(os.path.join(pretrained_model_name_or_path, cls.config_name)):
# Load from a PyTorch checkpoint
config_file = os.path.join(pretrained_model_name_or_path, cls.config_name)
elif subfolder is not None and os.path.isfile(
os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)
):
config_file = os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)
else:
raise EnvironmentError(
f"Error no file named {cls.config_name} found in directory {pretrained_model_name_or_path}."
)
else:
try:
# Load from URL or cache if already cached
config_file = hf_hub_download(
pretrained_model_name_or_path,
filename=cls.config_name,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
user_agent=user_agent,
subfolder=subfolder,
revision=revision,
)
except RepositoryNotFoundError:
raise EnvironmentError(
f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier"
" listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a"
" token having permission to this repo with `use_auth_token` or log in with `huggingface-cli"
" login`."
)
except RevisionNotFoundError:
raise EnvironmentError(
f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for"
" this model name. Check the model page at"
f" 'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."
)
except EntryNotFoundError:
raise EnvironmentError(
f"{pretrained_model_name_or_path} does not appear to have a file named {cls.config_name}."
)
except HTTPError as err:
raise EnvironmentError(
"There was a specific connection error when trying to load"
f" {pretrained_model_name_or_path}:\n{err}"
)
except ValueError:
raise EnvironmentError(
f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
f" directory containing a {cls.config_name} file.\nCheckout your internet connection or see how to"
" run the library in offline mode at"
" 'https://huggingface.co/docs/diffusers/installation#offline-mode'."
)
except EnvironmentError:
raise EnvironmentError(
f"Can't load config for '{pretrained_model_name_or_path}'. If you were trying to load it from "
"'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
f"containing a {cls.config_name} file"
)
try:
# Load config dict
config_dict = cls._dict_from_json_file(config_file)
except (json.JSONDecodeError, UnicodeDecodeError):
raise EnvironmentError(f"It looks like the config file at '{config_file}' is not a valid JSON file.")
if return_unused_kwargs:
return config_dict, kwargs
return config_dict
@staticmethod
def _get_init_keys(cls):
return set(dict(inspect.signature(cls.__init__).parameters).keys())
@classmethod
def extract_init_dict(cls, config_dict, **kwargs):
# 0. Copy origin config dict
original_dict = {k: v for k, v in config_dict.items()}
# 1. Retrieve expected config attributes from __init__ signature
expected_keys = cls._get_init_keys(cls)
expected_keys.remove("self")
# remove general kwargs if present in dict
if "kwargs" in expected_keys:
expected_keys.remove("kwargs")
# remove flax internal keys
if hasattr(cls, "_flax_internal_args"):
for arg in cls._flax_internal_args:
expected_keys.remove(arg)
# 2. Remove attributes that cannot be expected from expected config attributes
# remove keys to be ignored
if len(cls.ignore_for_config) > 0:
expected_keys = expected_keys - set(cls.ignore_for_config)
# load diffusers library to import compatible and original scheduler
diffusers_library = importlib.import_module(__name__.split(".")[0])
if cls.has_compatibles:
compatible_classes = [c for c in cls._get_compatibles() if not isinstance(c, DummyObject)]
else:
compatible_classes = []
expected_keys_comp_cls = set()
for c in compatible_classes:
expected_keys_c = cls._get_init_keys(c)
expected_keys_comp_cls = expected_keys_comp_cls.union(expected_keys_c)
expected_keys_comp_cls = expected_keys_comp_cls - cls._get_init_keys(cls)
config_dict = {k: v for k, v in config_dict.items() if k not in expected_keys_comp_cls}
# remove attributes from orig class that cannot be expected
orig_cls_name = config_dict.pop("_class_name", cls.__name__)
if orig_cls_name != cls.__name__ and hasattr(diffusers_library, orig_cls_name):
orig_cls = getattr(diffusers_library, orig_cls_name)
unexpected_keys_from_orig = cls._get_init_keys(orig_cls) - expected_keys
config_dict = {k: v for k, v in config_dict.items() if k not in unexpected_keys_from_orig}
# remove private attributes
config_dict = {k: v for k, v in config_dict.items() if not k.startswith("_")}
# 3. Create keyword arguments that will be passed to __init__ from expected keyword arguments
init_dict = {}
for key in expected_keys:
# if config param is passed to kwarg and is present in config dict
# it should overwrite existing config dict key
if key in kwargs and key in config_dict:
config_dict[key] = kwargs.pop(key)
if key in kwargs:
# overwrite key
init_dict[key] = kwargs.pop(key)
elif key in config_dict:
# use value from config dict
init_dict[key] = config_dict.pop(key)
# 4. Give nice warning if unexpected values have been passed
if len(config_dict) > 0:
logger.warning(
f"The config attributes {config_dict} were passed to {cls.__name__}, "
"but are not expected and will be ignored. Please verify your "
f"{cls.config_name} configuration file."
)
# 5. Give nice info if config attributes are initiliazed to default because they have not been passed
passed_keys = set(init_dict.keys())
if len(expected_keys - passed_keys) > 0:
logger.info(
f"{expected_keys - passed_keys} was not found in config. Values will be initialized to default values."
)
# 6. Define unused keyword arguments
unused_kwargs = {**config_dict, **kwargs}
# 7. Define "hidden" config parameters that were saved for compatible classes
hidden_config_dict = {k: v for k, v in original_dict.items() if k not in init_dict}
return init_dict, unused_kwargs, hidden_config_dict
@classmethod
def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]):
with open(json_file, "r", encoding="utf-8") as reader:
text = reader.read()
return json.loads(text)
def __repr__(self):
return f"{self.__class__.__name__} {self.to_json_string()}"
@property
def config(self) -> Dict[str, Any]:
"""
Returns the config of the class as a frozen dictionary
Returns:
`Dict[str, Any]`: Config of the class.
"""
return self._internal_dict
def to_json_string(self) -> str:
"""
Serializes this instance to a JSON string.
Returns:
`str`: String containing all the attributes that make up this configuration instance in JSON format.
"""
config_dict = self._internal_dict if hasattr(self, "_internal_dict") else {}
config_dict["_class_name"] = self.__class__.__name__
config_dict["_diffusers_version"] = __version__
return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
def to_json_file(self, json_file_path: Union[str, os.PathLike]):
"""
Save this instance to a JSON file.
Args:
json_file_path (`str` or `os.PathLike`):
Path to the JSON file in which this configuration instance's parameters will be saved.
"""
with open(json_file_path, "w", encoding="utf-8") as writer:
writer.write(self.to_json_string())
def register_to_config(init):
r"""
Decorator to apply on the init of classes inheriting from [`ConfigMixin`] so that all the arguments are
automatically sent to `self.register_for_config`. To ignore a specific argument accepted by the init but that
shouldn't be registered in the config, use the `ignore_for_config` class variable
Warning: Once decorated, all private arguments (beginning with an underscore) are trashed and not sent to the init!
"""
@functools.wraps(init)
def inner_init(self, *args, **kwargs):
# Ignore private kwargs in the init.
init_kwargs = {k: v for k, v in kwargs.items() if not k.startswith("_")}
config_init_kwargs = {k: v for k, v in kwargs.items() if k.startswith("_")}
if not isinstance(self, ConfigMixin):
raise RuntimeError(
f"`@register_for_config` was applied to {self.__class__.__name__} init method, but this class does "
"not inherit from `ConfigMixin`."
)
ignore = getattr(self, "ignore_for_config", [])
# Get positional arguments aligned with kwargs
new_kwargs = {}
signature = inspect.signature(init)
parameters = {
name: p.default for i, (name, p) in enumerate(signature.parameters.items()) if i > 0 and name not in ignore
}
for arg, name in zip(args, parameters.keys()):
new_kwargs[name] = arg
# Then add all kwargs
new_kwargs.update(
{
k: init_kwargs.get(k, default)
for k, default in parameters.items()
if k not in ignore and k not in new_kwargs
}
)
new_kwargs = {**config_init_kwargs, **new_kwargs}
getattr(self, "register_to_config")(**new_kwargs)
init(self, *args, **init_kwargs)
return inner_init
def flax_register_to_config(cls):
original_init = cls.__init__
@functools.wraps(original_init)
def init(self, *args, **kwargs):
if not isinstance(self, ConfigMixin):
raise RuntimeError(
f"`@register_for_config` was applied to {self.__class__.__name__} init method, but this class does "
"not inherit from `ConfigMixin`."
)
# Ignore private kwargs in the init. Retrieve all passed attributes
init_kwargs = {k: v for k, v in kwargs.items()}
# Retrieve default values
fields = dataclasses.fields(self)
default_kwargs = {}
for field in fields:
# ignore flax specific attributes
if field.name in self._flax_internal_args:
continue
if type(field.default) == dataclasses._MISSING_TYPE:
default_kwargs[field.name] = None
else:
default_kwargs[field.name] = getattr(self, field.name)
# Make sure init_kwargs override default kwargs
new_kwargs = {**default_kwargs, **init_kwargs}
# dtype should be part of `init_kwargs`, but not `new_kwargs`
if "dtype" in new_kwargs:
new_kwargs.pop("dtype")
# Get positional arguments aligned with kwargs
for i, arg in enumerate(args):
name = fields[i].name
new_kwargs[name] = arg
getattr(self, "register_to_config")(**new_kwargs)
original_init(self, *args, **kwargs)
cls.__init__ = init
return cls
| diffusers-ft-main | src/diffusers/configuration_utils.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
import inspect
import os
from typing import Dict, List, Optional, Union
import numpy as np
import flax
import PIL
from flax.core.frozen_dict import FrozenDict
from huggingface_hub import snapshot_download
from PIL import Image
from tqdm.auto import tqdm
from .configuration_utils import ConfigMixin
from .hub_utils import http_user_agent
from .modeling_flax_utils import FLAX_WEIGHTS_NAME, FlaxModelMixin
from .schedulers.scheduling_utils_flax import SCHEDULER_CONFIG_NAME, FlaxSchedulerMixin
from .utils import CONFIG_NAME, DIFFUSERS_CACHE, BaseOutput, is_transformers_available, logging
if is_transformers_available():
from transformers import FlaxPreTrainedModel
INDEX_FILE = "diffusion_flax_model.bin"
logger = logging.get_logger(__name__)
LOADABLE_CLASSES = {
"diffusers": {
"FlaxModelMixin": ["save_pretrained", "from_pretrained"],
"FlaxSchedulerMixin": ["save_pretrained", "from_pretrained"],
"FlaxDiffusionPipeline": ["save_pretrained", "from_pretrained"],
},
"transformers": {
"PreTrainedTokenizer": ["save_pretrained", "from_pretrained"],
"PreTrainedTokenizerFast": ["save_pretrained", "from_pretrained"],
"FlaxPreTrainedModel": ["save_pretrained", "from_pretrained"],
"FeatureExtractionMixin": ["save_pretrained", "from_pretrained"],
"ProcessorMixin": ["save_pretrained", "from_pretrained"],
"ImageProcessingMixin": ["save_pretrained", "from_pretrained"],
},
}
ALL_IMPORTABLE_CLASSES = {}
for library in LOADABLE_CLASSES:
ALL_IMPORTABLE_CLASSES.update(LOADABLE_CLASSES[library])
def import_flax_or_no_model(module, class_name):
try:
# 1. First make sure that if a Flax object is present, import this one
class_obj = getattr(module, "Flax" + class_name)
except AttributeError:
# 2. If this doesn't work, it's not a model and we don't append "Flax"
class_obj = getattr(module, class_name)
except AttributeError:
raise ValueError(f"Neither Flax{class_name} nor {class_name} exist in {module}")
return class_obj
@flax.struct.dataclass
class FlaxImagePipelineOutput(BaseOutput):
"""
Output class for image pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
"""
images: Union[List[PIL.Image.Image], np.ndarray]
class FlaxDiffusionPipeline(ConfigMixin):
r"""
Base class for all models.
[`FlaxDiffusionPipeline`] takes care of storing all components (models, schedulers, processors) for diffusion
pipelines and handles methods for loading, downloading and saving models as well as a few methods common to all
pipelines to:
- enabling/disabling the progress bar for the denoising iteration
Class attributes:
- **config_name** ([`str`]) -- name of the config file that will store the class and module names of all
components of the diffusion pipeline.
"""
config_name = "model_index.json"
def register_modules(self, **kwargs):
# import it here to avoid circular import
from diffusers import pipelines
for name, module in kwargs.items():
if module is None:
register_dict = {name: (None, None)}
else:
# retrieve library
library = module.__module__.split(".")[0]
# check if the module is a pipeline module
pipeline_dir = module.__module__.split(".")[-2]
path = module.__module__.split(".")
is_pipeline_module = pipeline_dir in path and hasattr(pipelines, pipeline_dir)
# if library is not in LOADABLE_CLASSES, then it is a custom module.
# Or if it's a pipeline module, then the module is inside the pipeline
# folder so we set the library to module name.
if library not in LOADABLE_CLASSES or is_pipeline_module:
library = pipeline_dir
# retrieve class_name
class_name = module.__class__.__name__
register_dict = {name: (library, class_name)}
# save model index config
self.register_to_config(**register_dict)
# set models
setattr(self, name, module)
def save_pretrained(self, save_directory: Union[str, os.PathLike], params: Union[Dict, FrozenDict]):
# TODO: handle inference_state
"""
Save all variables of the pipeline that can be saved and loaded as well as the pipelines configuration file to
a directory. A pipeline variable can be saved and loaded if its class implements both a save and loading
method. The pipeline can easily be re-loaded using the `[`~FlaxDiffusionPipeline.from_pretrained`]` class
method.
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to which to save. Will be created if it doesn't exist.
"""
self.save_config(save_directory)
model_index_dict = dict(self.config)
model_index_dict.pop("_class_name")
model_index_dict.pop("_diffusers_version")
model_index_dict.pop("_module", None)
for pipeline_component_name in model_index_dict.keys():
sub_model = getattr(self, pipeline_component_name)
if sub_model is None:
# edge case for saving a pipeline with safety_checker=None
continue
model_cls = sub_model.__class__
save_method_name = None
# search for the model's base class in LOADABLE_CLASSES
for library_name, library_classes in LOADABLE_CLASSES.items():
library = importlib.import_module(library_name)
for base_class, save_load_methods in library_classes.items():
class_candidate = getattr(library, base_class, None)
if class_candidate is not None and issubclass(model_cls, class_candidate):
# if we found a suitable base class in LOADABLE_CLASSES then grab its save method
save_method_name = save_load_methods[0]
break
if save_method_name is not None:
break
save_method = getattr(sub_model, save_method_name)
expects_params = "params" in set(inspect.signature(save_method).parameters.keys())
if expects_params:
save_method(
os.path.join(save_directory, pipeline_component_name), params=params[pipeline_component_name]
)
else:
save_method(os.path.join(save_directory, pipeline_component_name))
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
r"""
Instantiate a Flax diffusion pipeline from pre-trained pipeline weights.
The pipeline is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated).
The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come
pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
task.
The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those
weights are discarded.
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
Can be either:
- A string, the *repo id* of a pretrained pipeline hosted inside a model repo on
https://huggingface.co/ Valid repo ids have to be located under a user or organization name, like
`CompVis/ldm-text2im-large-256`.
- A path to a *directory* containing pipeline weights saved using
[`~FlaxDiffusionPipeline.save_pretrained`], e.g., `./my_pipeline_directory/`.
dtype (`str` or `jnp.dtype`, *optional*):
Override the default `jnp.dtype` and load the model under this dtype. If `"auto"` is passed the dtype
will be automatically derived from the model's weights.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
output_loading_info(`bool`, *optional*, defaults to `False`):
Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
local_files_only(`bool`, *optional*, defaults to `False`):
Whether or not to only look at local files (i.e., do not try to download the model).
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `huggingface-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
mirror (`str`, *optional*):
Mirror source to accelerate downloads in China. If you are from China and have an accessibility
problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
Please refer to the mirror site for more information. specify the folder name here.
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to overwrite load - and saveable variables - *i.e.* the pipeline components - of the
specific pipeline class. The overwritten components are then directly passed to the pipelines
`__init__` method. See example below for more information.
<Tip>
It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
models](https://huggingface.co/docs/hub/models-gated#gated-models), *e.g.* `"runwayml/stable-diffusion-v1-5"`
</Tip>
<Tip>
Activate the special ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use
this method in a firewalled environment.
</Tip>
Examples:
```py
>>> from diffusers import FlaxDiffusionPipeline
>>> # Download pipeline from huggingface.co and cache.
>>> # Requires to be logged in to Hugging Face hub,
>>> # see more in [the documentation](https://huggingface.co/docs/hub/security-tokens)
>>> pipeline, params = FlaxDiffusionPipeline.from_pretrained(
... "runwayml/stable-diffusion-v1-5",
... revision="bf16",
... dtype=jnp.bfloat16,
... )
>>> # Download pipeline, but use a different scheduler
>>> from diffusers import FlaxDPMSolverMultistepScheduler
>>> model_id = "runwayml/stable-diffusion-v1-5"
>>> sched, sched_state = FlaxDPMSolverMultistepScheduler.from_pretrained(
... model_id,
... subfolder="scheduler",
... )
>>> dpm_pipe, dpm_params = FlaxStableDiffusionPipeline.from_pretrained(
... model_id, revision="bf16", dtype=jnp.bfloat16, scheduler=dpmpp
... )
>>> dpm_params["scheduler"] = dpmpp_state
```
"""
cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", False)
use_auth_token = kwargs.pop("use_auth_token", None)
revision = kwargs.pop("revision", None)
from_pt = kwargs.pop("from_pt", False)
dtype = kwargs.pop("dtype", None)
# 1. Download the checkpoints and configs
# use snapshot download here to get it working from from_pretrained
if not os.path.isdir(pretrained_model_name_or_path):
config_dict = cls.load_config(
pretrained_model_name_or_path,
cache_dir=cache_dir,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
)
# make sure we only download sub-folders and `diffusers` filenames
folder_names = [k for k in config_dict.keys() if not k.startswith("_")]
allow_patterns = [os.path.join(k, "*") for k in folder_names]
allow_patterns += [FLAX_WEIGHTS_NAME, SCHEDULER_CONFIG_NAME, CONFIG_NAME, cls.config_name]
# make sure we don't download PyTorch weights, unless when using from_pt
ignore_patterns = "*.bin" if not from_pt else []
if cls != FlaxDiffusionPipeline:
requested_pipeline_class = cls.__name__
else:
requested_pipeline_class = config_dict.get("_class_name", cls.__name__)
requested_pipeline_class = (
requested_pipeline_class
if requested_pipeline_class.startswith("Flax")
else "Flax" + requested_pipeline_class
)
user_agent = {"pipeline_class": requested_pipeline_class}
user_agent = http_user_agent(user_agent)
# download all allow_patterns
cached_folder = snapshot_download(
pretrained_model_name_or_path,
cache_dir=cache_dir,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
allow_patterns=allow_patterns,
ignore_patterns=ignore_patterns,
user_agent=user_agent,
)
else:
cached_folder = pretrained_model_name_or_path
config_dict = cls.load_config(cached_folder)
# 2. Load the pipeline class, if using custom module then load it from the hub
# if we load from explicit class, let's use it
if cls != FlaxDiffusionPipeline:
pipeline_class = cls
else:
diffusers_module = importlib.import_module(cls.__module__.split(".")[0])
class_name = (
config_dict["_class_name"]
if config_dict["_class_name"].startswith("Flax")
else "Flax" + config_dict["_class_name"]
)
pipeline_class = getattr(diffusers_module, class_name)
# some modules can be passed directly to the init
# in this case they are already instantiated in `kwargs`
# extract them here
expected_modules = set(inspect.signature(pipeline_class.__init__).parameters.keys())
passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
init_dict, _, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)
init_kwargs = {}
# inference_params
params = {}
# import it here to avoid circular import
from diffusers import pipelines
# 3. Load each module in the pipeline
for name, (library_name, class_name) in init_dict.items():
if class_name is None:
# edge case for when the pipeline was saved with safety_checker=None
init_kwargs[name] = None
continue
is_pipeline_module = hasattr(pipelines, library_name)
loaded_sub_model = None
sub_model_should_be_defined = True
# if the model is in a pipeline module, then we load it from the pipeline
if name in passed_class_obj:
# 1. check that passed_class_obj has correct parent class
if not is_pipeline_module:
library = importlib.import_module(library_name)
class_obj = getattr(library, class_name)
importable_classes = LOADABLE_CLASSES[library_name]
class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()}
expected_class_obj = None
for class_name, class_candidate in class_candidates.items():
if class_candidate is not None and issubclass(class_obj, class_candidate):
expected_class_obj = class_candidate
if not issubclass(passed_class_obj[name].__class__, expected_class_obj):
raise ValueError(
f"{passed_class_obj[name]} is of type: {type(passed_class_obj[name])}, but should be"
f" {expected_class_obj}"
)
elif passed_class_obj[name] is None:
logger.warning(
f"You have passed `None` for {name} to disable its functionality in {pipeline_class}. Note"
f" that this might lead to problems when using {pipeline_class} and is not recommended."
)
sub_model_should_be_defined = False
else:
logger.warning(
f"You have passed a non-standard module {passed_class_obj[name]}. We cannot verify whether it"
" has the correct type"
)
# set passed class object
loaded_sub_model = passed_class_obj[name]
elif is_pipeline_module:
pipeline_module = getattr(pipelines, library_name)
class_obj = import_flax_or_no_model(pipeline_module, class_name)
importable_classes = ALL_IMPORTABLE_CLASSES
class_candidates = {c: class_obj for c in importable_classes.keys()}
else:
# else we just import it from the library.
library = importlib.import_module(library_name)
class_obj = import_flax_or_no_model(library, class_name)
importable_classes = LOADABLE_CLASSES[library_name]
class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()}
if loaded_sub_model is None and sub_model_should_be_defined:
load_method_name = None
for class_name, class_candidate in class_candidates.items():
if class_candidate is not None and issubclass(class_obj, class_candidate):
load_method_name = importable_classes[class_name][1]
load_method = getattr(class_obj, load_method_name)
# check if the module is in a subdirectory
if os.path.isdir(os.path.join(cached_folder, name)):
loadable_folder = os.path.join(cached_folder, name)
else:
loaded_sub_model = cached_folder
if issubclass(class_obj, FlaxModelMixin):
loaded_sub_model, loaded_params = load_method(loadable_folder, from_pt=from_pt, dtype=dtype)
params[name] = loaded_params
elif is_transformers_available() and issubclass(class_obj, FlaxPreTrainedModel):
if from_pt:
# TODO(Suraj): Fix this in Transformers. We should be able to use `_do_init=False` here
loaded_sub_model = load_method(loadable_folder, from_pt=from_pt)
loaded_params = loaded_sub_model.params
del loaded_sub_model._params
else:
loaded_sub_model, loaded_params = load_method(loadable_folder, _do_init=False)
params[name] = loaded_params
elif issubclass(class_obj, FlaxSchedulerMixin):
loaded_sub_model, scheduler_state = load_method(loadable_folder)
params[name] = scheduler_state
else:
loaded_sub_model = load_method(loadable_folder)
init_kwargs[name] = loaded_sub_model # UNet(...), # DiffusionSchedule(...)
model = pipeline_class(**init_kwargs, dtype=dtype)
return model, params
@staticmethod
def numpy_to_pil(images):
"""
Convert a numpy image or a batch of images to a PIL image.
"""
if images.ndim == 3:
images = images[None, ...]
images = (images * 255).round().astype("uint8")
if images.shape[-1] == 1:
# special case for grayscale (single channel) images
pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images]
else:
pil_images = [Image.fromarray(image) for image in images]
return pil_images
# TODO: make it compatible with jax.lax
def progress_bar(self, iterable):
if not hasattr(self, "_progress_bar_config"):
self._progress_bar_config = {}
elif not isinstance(self._progress_bar_config, dict):
raise ValueError(
f"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}."
)
return tqdm(iterable, **self._progress_bar_config)
def set_progress_bar_config(self, **kwargs):
self._progress_bar_config = kwargs
| diffusers-ft-main | src/diffusers/pipeline_flax_utils.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from pickle import UnpicklingError
from typing import Any, Dict, Union
import jax
import jax.numpy as jnp
import msgpack.exceptions
from flax.core.frozen_dict import FrozenDict, unfreeze
from flax.serialization import from_bytes, to_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
from requests import HTTPError
from . import __version__, is_torch_available
from .modeling_flax_pytorch_utils import convert_pytorch_state_dict_to_flax
from .utils import (
CONFIG_NAME,
DIFFUSERS_CACHE,
FLAX_WEIGHTS_NAME,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
WEIGHTS_NAME,
logging,
)
logger = logging.get_logger(__name__)
class FlaxModelMixin:
r"""
Base class for all flax models.
[`FlaxModelMixin`] takes care of storing the configuration of the models and handles methods for loading,
downloading and saving models.
"""
config_name = CONFIG_NAME
_automatically_saved_args = ["_diffusers_version", "_class_name", "_name_or_path"]
_flax_internal_args = ["name", "parent", "dtype"]
@classmethod
def _from_config(cls, config, **kwargs):
"""
All context managers that the model should be initialized under go here.
"""
return cls(config, **kwargs)
def _cast_floating_to(self, params: Union[Dict, FrozenDict], dtype: jnp.dtype, mask: Any = None) -> Any:
"""
Helper method to cast floating-point values of given parameter `PyTree` to given `dtype`.
"""
# taken from https://github.com/deepmind/jmp/blob/3a8318abc3292be38582794dbf7b094e6583b192/jmp/_src/policy.py#L27
def conditional_cast(param):
if isinstance(param, jnp.ndarray) and jnp.issubdtype(param.dtype, jnp.floating):
param = param.astype(dtype)
return param
if mask is None:
return jax.tree_map(conditional_cast, params)
flat_params = flatten_dict(params)
flat_mask, _ = jax.tree_flatten(mask)
for masked, key in zip(flat_mask, flat_params.keys()):
if masked:
param = flat_params[key]
flat_params[key] = conditional_cast(param)
return unflatten_dict(flat_params)
def to_bf16(self, params: Union[Dict, FrozenDict], mask: Any = None):
r"""
Cast the floating-point `params` to `jax.numpy.bfloat16`. This returns a new `params` tree and does not cast
the `params` in place.
This method can be used on TPU to explicitly convert the model parameters to bfloat16 precision to do full
half-precision training or to save weights in bfloat16 for inference in order to save memory and improve speed.
Arguments:
params (`Union[Dict, FrozenDict]`):
A `PyTree` of model parameters.
mask (`Union[Dict, FrozenDict]`):
A `PyTree` with same structure as the `params` tree. The leaves should be booleans, `True` for params
you want to cast, and should be `False` for those you want to skip.
Examples:
```python
>>> from diffusers import FlaxUNet2DConditionModel
>>> # load model
>>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> # By default, the model parameters will be in fp32 precision, to cast these to bfloat16 precision
>>> params = model.to_bf16(params)
>>> # If you don't want to cast certain parameters (for example layer norm bias and scale)
>>> # then pass the mask as follows
>>> from flax import traverse_util
>>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> flat_params = traverse_util.flatten_dict(params)
>>> mask = {
... path: (path[-2] != ("LayerNorm", "bias") and path[-2:] != ("LayerNorm", "scale"))
... for path in flat_params
... }
>>> mask = traverse_util.unflatten_dict(mask)
>>> params = model.to_bf16(params, mask)
```"""
return self._cast_floating_to(params, jnp.bfloat16, mask)
def to_fp32(self, params: Union[Dict, FrozenDict], mask: Any = None):
r"""
Cast the floating-point `params` to `jax.numpy.float32`. This method can be used to explicitly convert the
model parameters to fp32 precision. This returns a new `params` tree and does not cast the `params` in place.
Arguments:
params (`Union[Dict, FrozenDict]`):
A `PyTree` of model parameters.
mask (`Union[Dict, FrozenDict]`):
A `PyTree` with same structure as the `params` tree. The leaves should be booleans, `True` for params
you want to cast, and should be `False` for those you want to skip
Examples:
```python
>>> from diffusers import FlaxUNet2DConditionModel
>>> # Download model and configuration from huggingface.co
>>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> # By default, the model params will be in fp32, to illustrate the use of this method,
>>> # we'll first cast to fp16 and back to fp32
>>> params = model.to_f16(params)
>>> # now cast back to fp32
>>> params = model.to_fp32(params)
```"""
return self._cast_floating_to(params, jnp.float32, mask)
def to_fp16(self, params: Union[Dict, FrozenDict], mask: Any = None):
r"""
Cast the floating-point `params` to `jax.numpy.float16`. This returns a new `params` tree and does not cast the
`params` in place.
This method can be used on GPU to explicitly convert the model parameters to float16 precision to do full
half-precision training or to save weights in float16 for inference in order to save memory and improve speed.
Arguments:
params (`Union[Dict, FrozenDict]`):
A `PyTree` of model parameters.
mask (`Union[Dict, FrozenDict]`):
A `PyTree` with same structure as the `params` tree. The leaves should be booleans, `True` for params
you want to cast, and should be `False` for those you want to skip
Examples:
```python
>>> from diffusers import FlaxUNet2DConditionModel
>>> # load model
>>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> # By default, the model params will be in fp32, to cast these to float16
>>> params = model.to_fp16(params)
>>> # If you want don't want to cast certain parameters (for example layer norm bias and scale)
>>> # then pass the mask as follows
>>> from flax import traverse_util
>>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> flat_params = traverse_util.flatten_dict(params)
>>> mask = {
... path: (path[-2] != ("LayerNorm", "bias") and path[-2:] != ("LayerNorm", "scale"))
... for path in flat_params
... }
>>> mask = traverse_util.unflatten_dict(mask)
>>> params = model.to_fp16(params, mask)
```"""
return self._cast_floating_to(params, jnp.float16, mask)
def init_weights(self, rng: jax.random.PRNGKey) -> Dict:
raise NotImplementedError(f"init_weights method has to be implemented for {self}")
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: Union[str, os.PathLike],
dtype: jnp.dtype = jnp.float32,
*model_args,
**kwargs,
):
r"""
Instantiate a pretrained flax model from a pre-trained model configuration.
The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come
pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
task.
The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those
weights are discarded.
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`):
Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids are namespaced under a user or organization name, like
`runwayml/stable-diffusion-v1-5`.
- A path to a *directory* containing model weights saved using [`~ModelMixin.save_pretrained`],
e.g., `./my_model_directory/`.
dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
`jax.numpy.bfloat16` (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given `dtype`.
**Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.**
If you wish to change the dtype of the model parameters, see [`~ModelMixin.to_fp16`] and
[`~ModelMixin.to_bf16`].
model_args (sequence of positional arguments, *optional*):
All remaining positional arguments will be passed to the underlying model's `__init__` method.
cache_dir (`Union[str, os.PathLike]`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
local_files_only(`bool`, *optional*, defaults to `False`):
Whether or not to only look at local files (i.e., do not try to download the model).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
from_pt (`bool`, *optional*, defaults to `False`):
Load the model weights from a PyTorch checkpoint save file.
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
`output_attentions=True`). Behaves differently depending on whether a `config` is provided or
automatically loaded:
- If a configuration is provided with `config`, `**kwargs` will be directly passed to the
underlying model's `__init__` method (we assume all relevant updates to the configuration have
already been done)
- If a configuration is not provided, `kwargs` will be first passed to the configuration class
initialization function ([`~ConfigMixin.from_config`]). Each key of `kwargs` that corresponds to
a configuration attribute will be used to override said attribute with the supplied `kwargs`
value. Remaining keys that do not correspond to any configuration attribute will be passed to the
underlying model's `__init__` function.
Examples:
```python
>>> from diffusers import FlaxUNet2DConditionModel
>>> # Download model and configuration from huggingface.co and cache.
>>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> # Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).
>>> model, params = FlaxUNet2DConditionModel.from_pretrained("./test/saved_model/")
```"""
config = kwargs.pop("config", None)
cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
force_download = kwargs.pop("force_download", False)
from_pt = kwargs.pop("from_pt", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", False)
use_auth_token = kwargs.pop("use_auth_token", None)
revision = kwargs.pop("revision", None)
subfolder = kwargs.pop("subfolder", None)
user_agent = {
"diffusers": __version__,
"file_type": "model",
"framework": "flax",
}
# Load config if we don't provide a configuration
config_path = config if config is not None else pretrained_model_name_or_path
model, model_kwargs = cls.from_config(
config_path,
cache_dir=cache_dir,
return_unused_kwargs=True,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
subfolder=subfolder,
# model args
dtype=dtype,
**kwargs,
)
# Load model
pretrained_path_with_subfolder = (
pretrained_model_name_or_path
if subfolder is None
else os.path.join(pretrained_model_name_or_path, subfolder)
)
if os.path.isdir(pretrained_path_with_subfolder):
if from_pt:
if not os.path.isfile(os.path.join(pretrained_path_with_subfolder, WEIGHTS_NAME)):
raise EnvironmentError(
f"Error no file named {WEIGHTS_NAME} found in directory {pretrained_path_with_subfolder} "
)
model_file = os.path.join(pretrained_path_with_subfolder, WEIGHTS_NAME)
elif os.path.isfile(os.path.join(pretrained_path_with_subfolder, FLAX_WEIGHTS_NAME)):
# Load from a Flax checkpoint
model_file = os.path.join(pretrained_path_with_subfolder, FLAX_WEIGHTS_NAME)
# Check if pytorch weights exist instead
elif os.path.isfile(os.path.join(pretrained_path_with_subfolder, WEIGHTS_NAME)):
raise EnvironmentError(
f"{WEIGHTS_NAME} file found in directory {pretrained_path_with_subfolder}. Please load the model"
" using `from_pt=True`."
)
else:
raise EnvironmentError(
f"Error no file named {FLAX_WEIGHTS_NAME} or {WEIGHTS_NAME} found in directory "
f"{pretrained_path_with_subfolder}."
)
else:
try:
model_file = hf_hub_download(
pretrained_model_name_or_path,
filename=FLAX_WEIGHTS_NAME if not from_pt else WEIGHTS_NAME,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
user_agent=user_agent,
subfolder=subfolder,
revision=revision,
)
except RepositoryNotFoundError:
raise EnvironmentError(
f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
"listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
"token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
"login`."
)
except RevisionNotFoundError:
raise EnvironmentError(
f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for "
"this model name. Check the model page at "
f"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."
)
except EntryNotFoundError:
raise EnvironmentError(
f"{pretrained_model_name_or_path} does not appear to have a file named {FLAX_WEIGHTS_NAME}."
)
except HTTPError as err:
raise EnvironmentError(
f"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n"
f"{err}"
)
except ValueError:
raise EnvironmentError(
f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
f" directory containing a file named {FLAX_WEIGHTS_NAME} or {WEIGHTS_NAME}.\nCheckout your"
" internet connection or see how to run the library in offline mode at"
" 'https://huggingface.co/docs/transformers/installation#offline-mode'."
)
except EnvironmentError:
raise EnvironmentError(
f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from "
"'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
f"containing a file named {FLAX_WEIGHTS_NAME} or {WEIGHTS_NAME}."
)
if from_pt:
if is_torch_available():
from .modeling_utils import load_state_dict
else:
raise EnvironmentError(
"Can't load the model in PyTorch format because PyTorch is not installed. "
"Please, install PyTorch or use native Flax weights."
)
# Step 1: Get the pytorch file
pytorch_model_file = load_state_dict(model_file)
# Step 2: Convert the weights
state = convert_pytorch_state_dict_to_flax(pytorch_model_file, model)
else:
try:
with open(model_file, "rb") as state_f:
state = from_bytes(cls, state_f.read())
except (UnpicklingError, msgpack.exceptions.ExtraData) as e:
try:
with open(model_file) as f:
if f.read().startswith("version"):
raise OSError(
"You seem to have cloned a repository without having git-lfs installed. Please"
" install git-lfs and run `git lfs install` followed by `git lfs pull` in the"
" folder you cloned."
)
else:
raise ValueError from e
except (UnicodeDecodeError, ValueError):
raise EnvironmentError(f"Unable to convert {model_file} to Flax deserializable object. ")
# make sure all arrays are stored as jnp.ndarray
# NOTE: This is to prevent a bug this will be fixed in Flax >= v0.3.4:
# https://github.com/google/flax/issues/1261
state = jax.tree_util.tree_map(lambda x: jax.device_put(x, jax.devices("cpu")[0]), state)
# flatten dicts
state = flatten_dict(state)
params_shape_tree = jax.eval_shape(model.init_weights, rng=jax.random.PRNGKey(0))
required_params = set(flatten_dict(unfreeze(params_shape_tree)).keys())
shape_state = flatten_dict(unfreeze(params_shape_tree))
missing_keys = required_params - set(state.keys())
unexpected_keys = set(state.keys()) - required_params
if missing_keys:
logger.warning(
f"The checkpoint {pretrained_model_name_or_path} is missing required keys: {missing_keys}. "
"Make sure to call model.init_weights to initialize the missing weights."
)
cls._missing_keys = missing_keys
for key in state.keys():
if key in shape_state and state[key].shape != shape_state[key].shape:
raise ValueError(
f"Trying to load the pretrained weight for {key} failed: checkpoint has shape "
f"{state[key].shape} which is incompatible with the model shape {shape_state[key].shape}. "
)
# remove unexpected keys to not be saved again
for unexpected_key in unexpected_keys:
del state[unexpected_key]
if len(unexpected_keys) > 0:
logger.warning(
f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when"
f" initializing {model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are"
f" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task or"
" with another architecture."
)
else:
logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n")
if len(missing_keys) > 0:
logger.warning(
f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
f" {pretrained_model_name_or_path} and are newly initialized: {missing_keys}\nYou should probably"
" TRAIN this model on a down-stream task to be able to use it for predictions and inference."
)
else:
logger.info(
f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at"
f" {pretrained_model_name_or_path}.\nIf your task is similar to the task the model of the checkpoint"
f" was trained on, you can already use {model.__class__.__name__} for predictions without further"
" training."
)
return model, unflatten_dict(state)
def save_pretrained(
self,
save_directory: Union[str, os.PathLike],
params: Union[Dict, FrozenDict],
is_main_process: bool = True,
):
"""
Save a model and its configuration file to a directory, so that it can be re-loaded using the
`[`~FlaxModelMixin.from_pretrained`]` class method
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to which to save. Will be created if it doesn't exist.
params (`Union[Dict, FrozenDict]`):
A `PyTree` of model parameters.
is_main_process (`bool`, *optional*, defaults to `True`):
Whether the process calling this is the main process or not. Useful when in distributed training like
TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on
the main process to avoid race conditions.
"""
if os.path.isfile(save_directory):
logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
return
os.makedirs(save_directory, exist_ok=True)
model_to_save = self
# Attach architecture to the config
# Save the config
if is_main_process:
model_to_save.save_config(save_directory)
# save model
output_model_file = os.path.join(save_directory, FLAX_WEIGHTS_NAME)
with open(output_model_file, "wb") as f:
model_bytes = to_bytes(params)
f.write(model_bytes)
logger.info(f"Model weights saved in {output_model_file}")
| diffusers-ft-main | src/diffusers/modeling_flax_utils.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch - Flax general utilities."""
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from .utils import logging
logger = logging.get_logger(__name__)
def rename_key(key):
regex = r"\w+[.]\d+"
pats = re.findall(regex, key)
for pat in pats:
key = key.replace(pat, "_".join(pat.split(".")))
return key
#####################
# PyTorch => Flax #
#####################
# Adapted from https://github.com/huggingface/transformers/blob/c603c80f46881ae18b2ca50770ef65fa4033eacd/src/transformers/modeling_flax_pytorch_utils.py#L69
# and https://github.com/patil-suraj/stable-diffusion-jax/blob/main/stable_diffusion_jax/convert_diffusers_to_jax.py
def rename_key_and_reshape_tensor(pt_tuple_key, pt_tensor, random_flax_state_dict):
"""Rename PT weight names to corresponding Flax weight names and reshape tensor if necessary"""
# conv norm or layer norm
renamed_pt_tuple_key = pt_tuple_key[:-1] + ("scale",)
if (
any("norm" in str_ for str_ in pt_tuple_key)
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
renamed_pt_tuple_key = pt_tuple_key[:-1] + ("scale",)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
renamed_pt_tuple_key = pt_tuple_key[:-1] + ("scale",)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
pt_tuple_key = pt_tuple_key[:-1] + ("embedding",)
return renamed_pt_tuple_key, pt_tensor
# conv layer
renamed_pt_tuple_key = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
pt_tensor = pt_tensor.transpose(2, 3, 1, 0)
return renamed_pt_tuple_key, pt_tensor
# linear layer
renamed_pt_tuple_key = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight":
pt_tensor = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
renamed_pt_tuple_key = pt_tuple_key[:-1] + ("weight",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
renamed_pt_tuple_key = pt_tuple_key[:-1] + ("bias",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def convert_pytorch_state_dict_to_flax(pt_state_dict, flax_model, init_key=42):
# Step 1: Convert pytorch tensor to numpy
pt_state_dict = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
random_flax_params = flax_model.init_weights(PRNGKey(init_key))
random_flax_state_dict = flatten_dict(random_flax_params)
flax_state_dict = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
renamed_pt_key = rename_key(pt_key)
pt_tuple_key = tuple(renamed_pt_key.split("."))
# Correctly rename weight parameters
flax_key, flax_tensor = rename_key_and_reshape_tensor(pt_tuple_key, pt_tensor, random_flax_state_dict)
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
f"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."
)
# also add unexpected weight so that warning is thrown
flax_state_dict[flax_key] = jnp.asarray(flax_tensor)
return unflatten_dict(flax_state_dict)
| diffusers-ft-main | src/diffusers/modeling_flax_pytorch_utils.py |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
pkgs_to_check_at_runtime = "python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
pkgs_to_check_at_runtime.append("importlib_metadata")
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")
def dep_version_check(pkg, hint=None):
require_version(deps[pkg], hint)
| diffusers-ft-main | src/diffusers/dependency_versions_check.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch optimization for diffusion models."""
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
logger = logging.get_logger(__name__)
class SchedulerType(Enum):
LINEAR = "linear"
COSINE = "cosine"
COSINE_WITH_RESTARTS = "cosine_with_restarts"
POLYNOMIAL = "polynomial"
CONSTANT = "constant"
CONSTANT_WITH_WARMUP = "constant_with_warmup"
def get_constant_schedule(optimizer: Optimizer, last_epoch: int = -1):
"""
Create a schedule with a constant learning rate, using the learning rate set in optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
return LambdaLR(optimizer, lambda _: 1, last_epoch=last_epoch)
def get_constant_schedule_with_warmup(optimizer: Optimizer, num_warmup_steps: int, last_epoch: int = -1):
"""
Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate
increases linearly between 0 and the initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
def lr_lambda(current_step: int):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1.0, num_warmup_steps))
return 1.0
return LambdaLR(optimizer, lr_lambda, last_epoch=last_epoch)
def get_linear_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, last_epoch=-1):
"""
Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after
a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
num_training_steps (`int`):
The total number of training steps.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
def lr_lambda(current_step: int):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
return max(
0.0, float(num_training_steps - current_step) / float(max(1, num_training_steps - num_warmup_steps))
)
return LambdaLR(optimizer, lr_lambda, last_epoch)
def get_cosine_schedule_with_warmup(
optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: float = 0.5, last_epoch: int = -1
):
"""
Create a schedule with a learning rate that decreases following the values of the cosine function between the
initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the
initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
num_training_steps (`int`):
The total number of training steps.
num_cycles (`float`, *optional*, defaults to 0.5):
The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0
following a half-cosine).
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
def lr_lambda(current_step):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))
return LambdaLR(optimizer, lr_lambda, last_epoch)
def get_cosine_with_hard_restarts_schedule_with_warmup(
optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: int = 1, last_epoch: int = -1
):
"""
Create a schedule with a learning rate that decreases following the values of the cosine function between the
initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases
linearly between 0 and the initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
num_training_steps (`int`):
The total number of training steps.
num_cycles (`int`, *optional*, defaults to 1):
The number of hard restarts to use.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
def lr_lambda(current_step):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
if progress >= 1.0:
return 0.0
return max(0.0, 0.5 * (1.0 + math.cos(math.pi * ((float(num_cycles) * progress) % 1.0))))
return LambdaLR(optimizer, lr_lambda, last_epoch)
def get_polynomial_decay_schedule_with_warmup(
optimizer, num_warmup_steps, num_training_steps, lr_end=1e-7, power=1.0, last_epoch=-1
):
"""
Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the
optimizer to end lr defined by *lr_end*, after a warmup period during which it increases linearly from 0 to the
initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
num_training_steps (`int`):
The total number of training steps.
lr_end (`float`, *optional*, defaults to 1e-7):
The end LR.
power (`float`, *optional*, defaults to 1.0):
Power factor.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Note: *power* defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT
implementation at
https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
lr_init = optimizer.defaults["lr"]
if not (lr_init > lr_end):
raise ValueError(f"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})")
def lr_lambda(current_step: int):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
lr_range = lr_init - lr_end
decay_steps = num_training_steps - num_warmup_steps
pct_remaining = 1 - (current_step - num_warmup_steps) / decay_steps
decay = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaLR(optimizer, lr_lambda, last_epoch)
TYPE_TO_SCHEDULER_FUNCTION = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
}
def get_scheduler(
name: Union[str, SchedulerType],
optimizer: Optimizer,
num_warmup_steps: Optional[int] = None,
num_training_steps: Optional[int] = None,
):
"""
Unified API to get any scheduler from its name.
Args:
name (`str` or `SchedulerType`):
The name of the scheduler to use.
optimizer (`torch.optim.Optimizer`):
The optimizer that will be used during training.
num_warmup_steps (`int`, *optional*):
The number of warmup steps to do. This is not required by all schedulers (hence the argument being
optional), the function will raise an error if it's unset and the scheduler type requires it.
num_training_steps (`int``, *optional*):
The number of training steps to do. This is not required by all schedulers (hence the argument being
optional), the function will raise an error if it's unset and the scheduler type requires it.
"""
name = SchedulerType(name)
schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(optimizer)
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument.")
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(optimizer, num_warmup_steps=num_warmup_steps)
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(f"{name} requires `num_training_steps`, please provide that argument.")
return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps)
| diffusers-ft-main | src/diffusers/optimization.py |
from .utils import (
is_flax_available,
is_inflect_available,
is_onnx_available,
is_scipy_available,
is_torch_available,
is_transformers_available,
is_unidecode_available,
)
__version__ = "0.9.0"
from .configuration_utils import ConfigMixin
from .onnx_utils import OnnxRuntimeModel
from .utils import logging
if is_torch_available():
from .modeling_utils import ModelMixin
from .models import AutoencoderKL, Transformer2DModel, UNet1DModel, UNet2DConditionModel, UNet2DModel, VQModel
from .optimization import (
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
get_scheduler,
)
from .pipeline_utils import DiffusionPipeline
from .pipelines import (
DanceDiffusionPipeline,
DDIMPipeline,
DDPMPipeline,
KarrasVePipeline,
LDMPipeline,
LDMSuperResolutionPipeline,
PNDMPipeline,
RePaintPipeline,
ScoreSdeVePipeline,
)
from .schedulers import (
DDIMScheduler,
DDPMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
IPNDMScheduler,
KarrasVeScheduler,
PNDMScheduler,
RePaintScheduler,
SchedulerMixin,
ScoreSdeVeScheduler,
VQDiffusionScheduler,
)
from .training_utils import EMAModel
else:
from .utils.dummy_pt_objects import * # noqa F403
if is_torch_available() and is_scipy_available():
from .schedulers import LMSDiscreteScheduler
else:
from .utils.dummy_torch_and_scipy_objects import * # noqa F403
if is_torch_available() and is_transformers_available():
from .pipelines import (
AltDiffusionImg2ImgPipeline,
AltDiffusionPipeline,
CycleDiffusionPipeline,
LDMTextToImagePipeline,
StableDiffusionImageVariationPipeline,
StableDiffusionImg2ImgPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionPipeline,
StableDiffusionPipelineSafe,
StableDiffusionUpscalePipeline,
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
VQDiffusionPipeline,
)
else:
from .utils.dummy_torch_and_transformers_objects import * # noqa F403
if is_torch_available() and is_transformers_available() and is_onnx_available():
from .pipelines import (
OnnxStableDiffusionImg2ImgPipeline,
OnnxStableDiffusionInpaintPipeline,
OnnxStableDiffusionInpaintPipelineLegacy,
OnnxStableDiffusionPipeline,
StableDiffusionOnnxPipeline,
)
else:
from .utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403
if is_flax_available():
from .modeling_flax_utils import FlaxModelMixin
from .models.unet_2d_condition_flax import FlaxUNet2DConditionModel
from .models.vae_flax import FlaxAutoencoderKL
from .pipeline_flax_utils import FlaxDiffusionPipeline
from .schedulers import (
FlaxDDIMScheduler,
FlaxDDPMScheduler,
FlaxDPMSolverMultistepScheduler,
FlaxKarrasVeScheduler,
FlaxLMSDiscreteScheduler,
FlaxPNDMScheduler,
FlaxSchedulerMixin,
FlaxScoreSdeVeScheduler,
)
else:
from .utils.dummy_flax_objects import * # noqa F403
if is_flax_available() and is_transformers_available():
from .pipelines import FlaxStableDiffusionPipeline
else:
from .utils.dummy_flax_and_transformers_objects import * # noqa F403
| diffusers-ft-main | src/diffusers/__init__.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuid4
from huggingface_hub import HfFolder, Repository, whoami
from . import __version__
from .utils import ENV_VARS_TRUE_VALUES, deprecate, logging
from .utils.import_utils import (
_flax_version,
_jax_version,
_onnxruntime_version,
_torch_version,
is_flax_available,
is_modelcards_available,
is_onnx_available,
is_torch_available,
)
if is_modelcards_available():
from modelcards import CardData, ModelCard
logger = logging.get_logger(__name__)
MODEL_CARD_TEMPLATE_PATH = Path(__file__).parent / "utils" / "model_card_template.md"
SESSION_ID = uuid4().hex
DISABLE_TELEMETRY = os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
def http_user_agent(user_agent: Union[Dict, str, None] = None) -> str:
"""
Formats a user-agent string with basic info about a request.
"""
ua = f"diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"
if DISABLE_TELEMETRY:
return ua + "; telemetry/off"
if is_torch_available():
ua += f"; torch/{_torch_version}"
if is_flax_available():
ua += f"; jax/{_jax_version}"
ua += f"; flax/{_flax_version}"
if is_onnx_available():
ua += f"; onnxruntime/{_onnxruntime_version}"
# CI will set this value to True
if os.environ.get("DIFFUSERS_IS_CI", "").upper() in ENV_VARS_TRUE_VALUES:
ua += "; is_ci/true"
if isinstance(user_agent, dict):
ua += "; " + "; ".join(f"{k}/{v}" for k, v in user_agent.items())
elif isinstance(user_agent, str):
ua += "; " + user_agent
return ua
def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
if token is None:
token = HfFolder.get_token()
if organization is None:
username = whoami(token)["name"]
return f"{username}/{model_id}"
else:
return f"{organization}/{model_id}"
def init_git_repo(args, at_init: bool = False):
"""
Args:
Initializes a git repo in `args.hub_model_id`.
at_init (`bool`, *optional*, defaults to `False`):
Whether this function is called before any training or not. If `self.args.overwrite_output_dir` is `True`
and `at_init` is `True`, the path to the repo (which is `self.args.output_dir`) might be wiped out.
"""
deprecation_message = (
"Please use `huggingface_hub.Repository`. "
"See `examples/unconditional_image_generation/train_unconditional.py` for an example."
)
deprecate("init_git_repo()", "0.10.0", deprecation_message)
if hasattr(args, "local_rank") and args.local_rank not in [-1, 0]:
return
hub_token = args.hub_token if hasattr(args, "hub_token") else None
use_auth_token = True if hub_token is None else hub_token
if not hasattr(args, "hub_model_id") or args.hub_model_id is None:
repo_name = Path(args.output_dir).absolute().name
else:
repo_name = args.hub_model_id
if "/" not in repo_name:
repo_name = get_full_repo_name(repo_name, token=hub_token)
try:
repo = Repository(
args.output_dir,
clone_from=repo_name,
use_auth_token=use_auth_token,
private=args.hub_private_repo,
)
except EnvironmentError:
if args.overwrite_output_dir and at_init:
# Try again after wiping output_dir
shutil.rmtree(args.output_dir)
repo = Repository(
args.output_dir,
clone_from=repo_name,
use_auth_token=use_auth_token,
)
else:
raise
repo.git_pull()
# By default, ignore the checkpoint folders
if not os.path.exists(os.path.join(args.output_dir, ".gitignore")):
with open(os.path.join(args.output_dir, ".gitignore"), "w", encoding="utf-8") as writer:
writer.writelines(["checkpoint-*/"])
return repo
def push_to_hub(
args,
pipeline,
repo: Repository,
commit_message: Optional[str] = "End of training",
blocking: bool = True,
**kwargs,
) -> str:
"""
Parameters:
Upload *self.model* and *self.tokenizer* to the 🤗 model hub on the repo *self.args.hub_model_id*.
commit_message (`str`, *optional*, defaults to `"End of training"`):
Message to commit while pushing.
blocking (`bool`, *optional*, defaults to `True`):
Whether the function should return only when the `git push` has finished.
kwargs:
Additional keyword arguments passed along to [`create_model_card`].
Returns:
The url of the commit of your model in the given repository if `blocking=False`, a tuple with the url of the
commit and an object to track the progress of the commit if `blocking=True`
"""
deprecation_message = (
"Please use `huggingface_hub.Repository` and `Repository.push_to_hub()`. "
"See `examples/unconditional_image_generation/train_unconditional.py` for an example."
)
deprecate("push_to_hub()", "0.10.0", deprecation_message)
if not hasattr(args, "hub_model_id") or args.hub_model_id is None:
model_name = Path(args.output_dir).name
else:
model_name = args.hub_model_id.split("/")[-1]
output_dir = args.output_dir
os.makedirs(output_dir, exist_ok=True)
logger.info(f"Saving pipeline checkpoint to {output_dir}")
pipeline.save_pretrained(output_dir)
# Only push from one node.
if hasattr(args, "local_rank") and args.local_rank not in [-1, 0]:
return
# Cancel any async push in progress if blocking=True. The commits will all be pushed together.
if (
blocking
and len(repo.command_queue) > 0
and repo.command_queue[-1] is not None
and not repo.command_queue[-1].is_done
):
repo.command_queue[-1]._process.kill()
git_head_commit_url = repo.push_to_hub(commit_message=commit_message, blocking=blocking, auto_lfs_prune=True)
# push separately the model card to be independent from the rest of the model
create_model_card(args, model_name=model_name)
try:
repo.push_to_hub(commit_message="update model card README.md", blocking=blocking, auto_lfs_prune=True)
except EnvironmentError as exc:
logger.error(f"Error pushing update to the model card. Please read logs and retry.\n${exc}")
return git_head_commit_url
def create_model_card(args, model_name):
if not is_modelcards_available:
raise ValueError(
"Please make sure to have `modelcards` installed when using the `create_model_card` function. You can"
" install the package with `pip install modelcards`."
)
if hasattr(args, "local_rank") and args.local_rank not in [-1, 0]:
return
hub_token = args.hub_token if hasattr(args, "hub_token") else None
repo_name = get_full_repo_name(model_name, token=hub_token)
model_card = ModelCard.from_template(
card_data=CardData( # Card metadata object that will be converted to YAML block
language="en",
license="apache-2.0",
library_name="diffusers",
tags=[],
datasets=args.dataset_name,
metrics=[],
),
template_path=MODEL_CARD_TEMPLATE_PATH,
model_name=model_name,
repo_name=repo_name,
dataset_name=args.dataset_name if hasattr(args, "dataset_name") else None,
learning_rate=args.learning_rate,
train_batch_size=args.train_batch_size,
eval_batch_size=args.eval_batch_size,
gradient_accumulation_steps=args.gradient_accumulation_steps
if hasattr(args, "gradient_accumulation_steps")
else None,
adam_beta1=args.adam_beta1 if hasattr(args, "adam_beta1") else None,
adam_beta2=args.adam_beta2 if hasattr(args, "adam_beta2") else None,
adam_weight_decay=args.adam_weight_decay if hasattr(args, "adam_weight_decay") else None,
adam_epsilon=args.adam_epsilon if hasattr(args, "adam_epsilon") else None,
lr_scheduler=args.lr_scheduler if hasattr(args, "lr_scheduler") else None,
lr_warmup_steps=args.lr_warmup_steps if hasattr(args, "lr_warmup_steps") else None,
ema_inv_gamma=args.ema_inv_gamma if hasattr(args, "ema_inv_gamma") else None,
ema_power=args.ema_power if hasattr(args, "ema_power") else None,
ema_max_decay=args.ema_max_decay if hasattr(args, "ema_max_decay") else None,
mixed_precision=args.mixed_precision,
)
card_path = os.path.join(args.output_dir, "README.md")
model_card.save(card_path)
| diffusers-ft-main | src/diffusers/hub_utils.py |
import copy
import os
import random
import numpy as np
import torch
def enable_full_determinism(seed: int):
"""
Helper function for reproducible behavior during distributed training. See
- https://pytorch.org/docs/stable/notes/randomness.html for pytorch
"""
# set seed first
set_seed(seed)
# Enable PyTorch deterministic mode. This potentially requires either the environment
# variable 'CUDA_LAUNCH_BLOCKING' or 'CUBLAS_WORKSPACE_CONFIG' to be set,
# depending on the CUDA version, so we set them both here
os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"
torch.use_deterministic_algorithms(True)
# Enable CUDNN deterministic mode
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def set_seed(seed: int):
"""
Args:
Helper function for reproducible behavior to set the seed in `random`, `numpy`, `torch`.
seed (`int`): The seed to set.
"""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# ^^ safe to call this function even if cuda is not available
class EMAModel:
"""
Exponential Moving Average of models weights
"""
def __init__(
self,
model,
update_after_step=0,
inv_gamma=1.0,
power=2 / 3,
min_value=0.0,
max_value=0.9999,
device=None,
):
"""
@crowsonkb's notes on EMA Warmup:
If gamma=1 and power=1, implements a simple average. gamma=1, power=2/3 are good values for models you plan
to train for a million or more steps (reaches decay factor 0.999 at 31.6K steps, 0.9999 at 1M steps),
gamma=1, power=3/4 for models you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999
at 215.4k steps).
Args:
inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1.
power (float): Exponential factor of EMA warmup. Default: 2/3.
min_value (float): The minimum EMA decay rate. Default: 0.
"""
self.averaged_model = copy.deepcopy(model).eval()
self.averaged_model.requires_grad_(False)
self.update_after_step = update_after_step
self.inv_gamma = inv_gamma
self.power = power
self.min_value = min_value
self.max_value = max_value
if device is not None:
self.averaged_model = self.averaged_model.to(device=device)
self.decay = 0.0
self.optimization_step = 0
def get_decay(self, optimization_step):
"""
Compute the decay factor for the exponential moving average.
"""
step = max(0, optimization_step - self.update_after_step - 1)
value = 1 - (1 + step / self.inv_gamma) ** -self.power
if step <= 0:
return 0.0
return max(self.min_value, min(value, self.max_value))
@torch.no_grad()
def step(self, new_model):
ema_state_dict = {}
ema_params = self.averaged_model.state_dict()
self.decay = self.get_decay(self.optimization_step)
for key, param in new_model.named_parameters():
if isinstance(param, dict):
continue
try:
ema_param = ema_params[key]
except KeyError:
ema_param = param.float().clone() if param.ndim == 1 else copy.deepcopy(param)
ema_params[key] = ema_param
if not param.requires_grad:
ema_params[key].copy_(param.to(dtype=ema_param.dtype).data)
ema_param = ema_params[key]
else:
ema_param.mul_(self.decay)
ema_param.add_(param.data.to(dtype=ema_param.dtype), alpha=1 - self.decay)
ema_state_dict[key] = ema_param
for key, param in new_model.named_buffers():
ema_state_dict[key] = param
self.averaged_model.load_state_dict(ema_state_dict, strict=False)
self.optimization_step += 1
| diffusers-ft-main | src/diffusers/training_utils.py |
# THIS FILE HAS BEEN AUTOGENERATED. To update:
# 1. modify the `_deps` dict in setup.py
# 2. run `make deps_table_update``
deps = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"black": "black==22.8",
"datasets": "datasets",
"filelock": "filelock",
"flake8": "flake8>=3.8.3",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-hub": "huggingface-hub>=0.10.0",
"importlib_metadata": "importlib_metadata",
"isort": "isort>=5.5.4",
"jax": "jax>=0.2.8,!=0.3.2",
"jaxlib": "jaxlib>=0.1.65",
"modelcards": "modelcards>=0.1.4",
"numpy": "numpy",
"parameterized": "parameterized",
"pytest": "pytest",
"pytest-timeout": "pytest-timeout",
"pytest-xdist": "pytest-xdist",
"safetensors": "safetensors",
"sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
"scipy": "scipy",
"regex": "regex!=2019.12.17",
"requests": "requests",
"tensorboard": "tensorboard",
"torch": "torch>=1.4",
"torchvision": "torchvision",
"transformers": "transformers>=4.21.0",
}
| diffusers-ft-main | src/diffusers/dependency_versions_table.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from functools import partial
from typing import Callable, List, Optional, Tuple, Union
import torch
from torch import Tensor, device
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
from requests import HTTPError
from . import __version__
from .utils import (
CONFIG_NAME,
DIFFUSERS_CACHE,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_NAME,
is_accelerate_available,
is_safetensors_available,
is_torch_version,
logging,
)
logger = logging.get_logger(__name__)
if is_torch_version(">=", "1.9.0"):
_LOW_CPU_MEM_USAGE_DEFAULT = True
else:
_LOW_CPU_MEM_USAGE_DEFAULT = False
if is_accelerate_available():
import accelerate
from accelerate.utils import set_module_tensor_to_device
from accelerate.utils.versions import is_torch_version
if is_safetensors_available():
import safetensors
def get_parameter_device(parameter: torch.nn.Module):
try:
return next(parameter.parameters()).device
except StopIteration:
# For torch.nn.DataParallel compatibility in PyTorch 1.5
def find_tensor_attributes(module: torch.nn.Module) -> List[Tuple[str, Tensor]]:
tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
return tuples
gen = parameter._named_members(get_members_fn=find_tensor_attributes)
first_tuple = next(gen)
return first_tuple[1].device
def get_parameter_dtype(parameter: torch.nn.Module):
try:
return next(parameter.parameters()).dtype
except StopIteration:
# For torch.nn.DataParallel compatibility in PyTorch 1.5
def find_tensor_attributes(module: torch.nn.Module) -> List[Tuple[str, Tensor]]:
tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
return tuples
gen = parameter._named_members(get_members_fn=find_tensor_attributes)
first_tuple = next(gen)
return first_tuple[1].dtype
def load_state_dict(checkpoint_file: Union[str, os.PathLike]):
"""
Reads a checkpoint file, returning properly formatted errors if they arise.
"""
try:
if os.path.basename(checkpoint_file) == WEIGHTS_NAME:
return torch.load(checkpoint_file, map_location="cpu")
else:
return safetensors.torch.load_file(checkpoint_file, device="cpu")
except Exception as e:
try:
with open(checkpoint_file) as f:
if f.read().startswith("version"):
raise OSError(
"You seem to have cloned a repository without having git-lfs installed. Please install "
"git-lfs and run `git lfs install` followed by `git lfs pull` in the folder "
"you cloned."
)
else:
raise ValueError(
f"Unable to locate the file {checkpoint_file} which is necessary to load this pretrained "
"model. Make sure you have saved the model properly."
) from e
except (UnicodeDecodeError, ValueError):
raise OSError(
f"Unable to load weights from checkpoint file for '{checkpoint_file}' "
f"at '{checkpoint_file}'. "
"If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True."
)
def _load_state_dict_into_model(model_to_load, state_dict):
# Convert old format to new format if needed from a PyTorch state_dict
# copy state_dict so _load_from_state_dict can modify it
state_dict = state_dict.copy()
error_msgs = []
# PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants
# so we need to apply the function recursively.
def load(module: torch.nn.Module, prefix=""):
args = (state_dict, prefix, {}, True, [], [], error_msgs)
module._load_from_state_dict(*args)
for name, child in module._modules.items():
if child is not None:
load(child, prefix + name + ".")
load(model_to_load)
return error_msgs
class ModelMixin(torch.nn.Module):
r"""
Base class for all models.
[`ModelMixin`] takes care of storing the configuration of the models and handles methods for loading, downloading
and saving models.
- **config_name** ([`str`]) -- A filename under which the model should be stored when calling
[`~modeling_utils.ModelMixin.save_pretrained`].
"""
config_name = CONFIG_NAME
_automatically_saved_args = ["_diffusers_version", "_class_name", "_name_or_path"]
_supports_gradient_checkpointing = False
def __init__(self):
super().__init__()
@property
def is_gradient_checkpointing(self) -> bool:
"""
Whether gradient checkpointing is activated for this model or not.
Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint
activations".
"""
return any(hasattr(m, "gradient_checkpointing") and m.gradient_checkpointing for m in self.modules())
def enable_gradient_checkpointing(self):
"""
Activates gradient checkpointing for the current model.
Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint
activations".
"""
if not self._supports_gradient_checkpointing:
raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.")
self.apply(partial(self._set_gradient_checkpointing, value=True))
def disable_gradient_checkpointing(self):
"""
Deactivates gradient checkpointing for the current model.
Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint
activations".
"""
if self._supports_gradient_checkpointing:
self.apply(partial(self._set_gradient_checkpointing, value=False))
def save_pretrained(
self,
save_directory: Union[str, os.PathLike],
is_main_process: bool = True,
save_function: Callable = torch.save,
):
"""
Save a model and its configuration file to a directory, so that it can be re-loaded using the
`[`~modeling_utils.ModelMixin.from_pretrained`]` class method.
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to which to save. Will be created if it doesn't exist.
is_main_process (`bool`, *optional*, defaults to `True`):
Whether the process calling this is the main process or not. Useful when in distributed training like
TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on
the main process to avoid race conditions.
save_function (`Callable`):
The function to use to save the state dictionary. Useful on distributed training like TPUs when one
need to replace `torch.save` by another method.
"""
if os.path.isfile(save_directory):
logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
return
os.makedirs(save_directory, exist_ok=True)
model_to_save = self
# Attach architecture to the config
# Save the config
if is_main_process:
model_to_save.save_config(save_directory)
# Save the model
state_dict = model_to_save.state_dict()
# Clean the folder from a previous save
for filename in os.listdir(save_directory):
full_filename = os.path.join(save_directory, filename)
# If we have a shard file that is not going to be replaced, we delete it, but only from the main process
# in distributed settings to avoid race conditions.
if filename.startswith(WEIGHTS_NAME[:-4]) and os.path.isfile(full_filename) and is_main_process:
os.remove(full_filename)
# Save the model
save_function(state_dict, os.path.join(save_directory, WEIGHTS_NAME))
logger.info(f"Model weights saved in {os.path.join(save_directory, WEIGHTS_NAME)}")
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
r"""
Instantiate a pretrained pytorch model from a pre-trained model configuration.
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
the model, you should first set it back in training mode with `model.train()`.
The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come
pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
task.
The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those
weights are discarded.
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids should have an organization name, like `google/ddpm-celebahq-256`.
- A path to a *directory* containing model weights saved using [`~ModelMixin.save_config`], e.g.,
`./my_model_directory/`.
cache_dir (`Union[str, os.PathLike]`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
torch_dtype (`str` or `torch.dtype`, *optional*):
Override the default `torch.dtype` and load the model under this dtype. If `"auto"` is passed the dtype
will be automatically derived from the model's weights.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
output_loading_info(`bool`, *optional*, defaults to `False`):
Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
local_files_only(`bool`, *optional*, defaults to `False`):
Whether or not to only look at local files (i.e., do not try to download the model).
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `diffusers-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
subfolder (`str`, *optional*, defaults to `""`):
In case the relevant files are located inside a subfolder of the model repo (either remote in
huggingface.co or downloaded locally), you can specify the folder name here.
mirror (`str`, *optional*):
Mirror source to accelerate downloads in China. If you are from China and have an accessibility
problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
Please refer to the mirror site for more information.
device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
A map that specifies where each submodule should go. It doesn't need to be refined to each
parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the
same device.
To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For
more information about each option see [designing a device
map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
Speed up model loading by not initializing the weights and only loading the pre-trained weights. This
also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the
model. This is only supported when torch version >= 1.9.0. If you are using an older version of torch,
setting this argument to `True` will raise an error.
<Tip>
It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
models](https://huggingface.co/docs/hub/models-gated#gated-models).
</Tip>
<Tip>
Activate the special ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use
this method in a firewalled environment.
</Tip>
"""
cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
output_loading_info = kwargs.pop("output_loading_info", False)
local_files_only = kwargs.pop("local_files_only", False)
use_auth_token = kwargs.pop("use_auth_token", None)
revision = kwargs.pop("revision", None)
torch_dtype = kwargs.pop("torch_dtype", None)
subfolder = kwargs.pop("subfolder", None)
device_map = kwargs.pop("device_map", None)
low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
if low_cpu_mem_usage and not is_accelerate_available():
low_cpu_mem_usage = False
logger.warning(
"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
" environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
" `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
" install accelerate\n```\n."
)
if device_map is not None and not is_accelerate_available():
raise NotImplementedError(
"Loading and dispatching requires `accelerate`. Please make sure to install accelerate or set"
" `device_map=None`. You can install accelerate with `pip install accelerate`."
)
# Check if we can handle device_map and dispatching the weights
if device_map is not None and not is_torch_version(">=", "1.9.0"):
raise NotImplementedError(
"Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set"
" `device_map=None`."
)
if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
raise NotImplementedError(
"Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
" `low_cpu_mem_usage=False`."
)
if low_cpu_mem_usage is False and device_map is not None:
raise ValueError(
f"You cannot set `low_cpu_mem_usage` to `False` while using device_map={device_map} for loading and"
" dispatching. Please make sure to set `low_cpu_mem_usage=True`."
)
user_agent = {
"diffusers": __version__,
"file_type": "model",
"framework": "pytorch",
}
# Load config if we don't provide a configuration
config_path = pretrained_model_name_or_path
# This variable will flag if we're loading a sharded checkpoint. In this case the archive file is just the
# Load model
model_file = None
if is_safetensors_available():
try:
model_file = _get_model_file(
pretrained_model_name_or_path,
weights_name=SAFETENSORS_WEIGHTS_NAME,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
subfolder=subfolder,
user_agent=user_agent,
)
except:
pass
if model_file is None:
model_file = _get_model_file(
pretrained_model_name_or_path,
weights_name=WEIGHTS_NAME,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
subfolder=subfolder,
user_agent=user_agent,
)
if low_cpu_mem_usage:
# Instantiate model with empty weights
with accelerate.init_empty_weights():
config, unused_kwargs = cls.load_config(
config_path,
cache_dir=cache_dir,
return_unused_kwargs=True,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
subfolder=subfolder,
device_map=device_map,
**kwargs,
)
model = cls.from_config(config, **unused_kwargs)
# if device_map is Non,e load the state dict on move the params from meta device to the cpu
if device_map is None:
param_device = "cpu"
state_dict = load_state_dict(model_file)
# move the parms from meta device to cpu
for param_name, param in state_dict.items():
set_module_tensor_to_device(model, param_name, param_device, value=param)
else: # else let accelerate handle loading and dispatching.
# Load weights and dispatch according to the device_map
# by deafult the device_map is None and the weights are loaded on the CPU
accelerate.load_checkpoint_and_dispatch(model, model_file, device_map)
loading_info = {
"missing_keys": [],
"unexpected_keys": [],
"mismatched_keys": [],
"error_msgs": [],
}
else:
config, unused_kwargs = cls.load_config(
config_path,
cache_dir=cache_dir,
return_unused_kwargs=True,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
subfolder=subfolder,
device_map=device_map,
**kwargs,
)
model = cls.from_config(config, **unused_kwargs)
state_dict = load_state_dict(model_file)
model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model(
model,
state_dict,
model_file,
pretrained_model_name_or_path,
ignore_mismatched_sizes=ignore_mismatched_sizes,
)
loading_info = {
"missing_keys": missing_keys,
"unexpected_keys": unexpected_keys,
"mismatched_keys": mismatched_keys,
"error_msgs": error_msgs,
}
if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):
raise ValueError(
f"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}."
)
elif torch_dtype is not None:
model = model.to(torch_dtype)
model.register_to_config(_name_or_path=pretrained_model_name_or_path)
# Set model in evaluation mode to deactivate DropOut modules by default
model.eval()
if output_loading_info:
return model, loading_info
return model
@classmethod
def _load_pretrained_model(
cls,
model,
state_dict,
resolved_archive_file,
pretrained_model_name_or_path,
ignore_mismatched_sizes=False,
):
# Retrieve missing & unexpected_keys
model_state_dict = model.state_dict()
loaded_keys = [k for k in state_dict.keys()]
expected_keys = list(model_state_dict.keys())
original_loaded_keys = loaded_keys
missing_keys = list(set(expected_keys) - set(loaded_keys))
unexpected_keys = list(set(loaded_keys) - set(expected_keys))
# Make sure we are able to load base models as well as derived models (with heads)
model_to_load = model
def _find_mismatched_keys(
state_dict,
model_state_dict,
loaded_keys,
ignore_mismatched_sizes,
):
mismatched_keys = []
if ignore_mismatched_sizes:
for checkpoint_key in loaded_keys:
model_key = checkpoint_key
if (
model_key in model_state_dict
and state_dict[checkpoint_key].shape != model_state_dict[model_key].shape
):
mismatched_keys.append(
(checkpoint_key, state_dict[checkpoint_key].shape, model_state_dict[model_key].shape)
)
del state_dict[checkpoint_key]
return mismatched_keys
if state_dict is not None:
# Whole checkpoint
mismatched_keys = _find_mismatched_keys(
state_dict,
model_state_dict,
original_loaded_keys,
ignore_mismatched_sizes,
)
error_msgs = _load_state_dict_into_model(model_to_load, state_dict)
if len(error_msgs) > 0:
error_msg = "\n\t".join(error_msgs)
if "size mismatch" in error_msg:
error_msg += (
"\n\tYou may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method."
)
raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}")
if len(unexpected_keys) > 0:
logger.warning(
f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when"
f" initializing {model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are"
f" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task"
" or with another architecture (e.g. initializing a BertForSequenceClassification model from a"
" BertForPreTraining model).\n- This IS NOT expected if you are initializing"
f" {model.__class__.__name__} from the checkpoint of a model that you expect to be exactly"
" identical (initializing a BertForSequenceClassification model from a"
" BertForSequenceClassification model)."
)
else:
logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n")
if len(missing_keys) > 0:
logger.warning(
f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
f" {pretrained_model_name_or_path} and are newly initialized: {missing_keys}\nYou should probably"
" TRAIN this model on a down-stream task to be able to use it for predictions and inference."
)
elif len(mismatched_keys) == 0:
logger.info(
f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at"
f" {pretrained_model_name_or_path}.\nIf your task is similar to the task the model of the"
f" checkpoint was trained on, you can already use {model.__class__.__name__} for predictions"
" without further training."
)
if len(mismatched_keys) > 0:
mismatched_warning = "\n".join(
[
f"- {key}: found shape {shape1} in the checkpoint and {shape2} in the model instantiated"
for key, shape1, shape2 in mismatched_keys
]
)
logger.warning(
f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
f" {pretrained_model_name_or_path} and are newly initialized because the shapes did not"
f" match:\n{mismatched_warning}\nYou should probably TRAIN this model on a down-stream task to be"
" able to use it for predictions and inference."
)
return model, missing_keys, unexpected_keys, mismatched_keys, error_msgs
@property
def device(self) -> device:
"""
`torch.device`: The device on which the module is (assuming that all the module parameters are on the same
device).
"""
return get_parameter_device(self)
@property
def dtype(self) -> torch.dtype:
"""
`torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype).
"""
return get_parameter_dtype(self)
def num_parameters(self, only_trainable: bool = False, exclude_embeddings: bool = False) -> int:
"""
Get number of (optionally, trainable or non-embeddings) parameters in the module.
Args:
only_trainable (`bool`, *optional*, defaults to `False`):
Whether or not to return only the number of trainable parameters
exclude_embeddings (`bool`, *optional*, defaults to `False`):
Whether or not to return only the number of non-embeddings parameters
Returns:
`int`: The number of parameters.
"""
if exclude_embeddings:
embedding_param_names = [
f"{name}.weight"
for name, module_type in self.named_modules()
if isinstance(module_type, torch.nn.Embedding)
]
non_embedding_parameters = [
parameter for name, parameter in self.named_parameters() if name not in embedding_param_names
]
return sum(p.numel() for p in non_embedding_parameters if p.requires_grad or not only_trainable)
else:
return sum(p.numel() for p in self.parameters() if p.requires_grad or not only_trainable)
def unwrap_model(model: torch.nn.Module) -> torch.nn.Module:
"""
Recursively unwraps a model from potential containers (as used in distributed training).
Args:
model (`torch.nn.Module`): The model to unwrap.
"""
# since there could be multiple levels of wrapping, unwrap recursively
if hasattr(model, "module"):
return unwrap_model(model.module)
else:
return model
def _get_model_file(
pretrained_model_name_or_path,
*,
weights_name,
subfolder,
cache_dir,
force_download,
proxies,
resume_download,
local_files_only,
use_auth_token,
user_agent,
revision,
):
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
if os.path.isdir(pretrained_model_name_or_path):
if os.path.isfile(os.path.join(pretrained_model_name_or_path, weights_name)):
# Load from a PyTorch checkpoint
model_file = os.path.join(pretrained_model_name_or_path, weights_name)
return model_file
elif subfolder is not None and os.path.isfile(
os.path.join(pretrained_model_name_or_path, subfolder, weights_name)
):
model_file = os.path.join(pretrained_model_name_or_path, subfolder, weights_name)
return model_file
else:
raise EnvironmentError(
f"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."
)
else:
try:
# Load from URL or cache if already cached
model_file = hf_hub_download(
pretrained_model_name_or_path,
filename=weights_name,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
user_agent=user_agent,
subfolder=subfolder,
revision=revision,
)
return model_file
except RepositoryNotFoundError:
raise EnvironmentError(
f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
"listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
"token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
"login`."
)
except RevisionNotFoundError:
raise EnvironmentError(
f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for "
"this model name. Check the model page at "
f"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."
)
except EntryNotFoundError:
raise EnvironmentError(
f"{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."
)
except HTTPError as err:
raise EnvironmentError(
f"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}"
)
except ValueError:
raise EnvironmentError(
f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
f" directory containing a file named {weights_name} or"
" \nCheckout your internet connection or see how to run the library in"
" offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'."
)
except EnvironmentError:
raise EnvironmentError(
f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from "
"'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
f"containing a file named {weights_name}"
)
| diffusers-ft-main | src/diffusers/modeling_utils.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utilities to dynamically load objects from the Hub."""
import importlib
import inspect
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from .utils import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
COMMUNITY_PIPELINES_URL = (
"https://raw.githubusercontent.com/huggingface/diffusers/main/examples/community/{pipeline}.py"
)
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def init_hf_modules():
"""
Creates the cache directory for modules with an init, and adds it to the Python path.
"""
# This function has already been executed if HF_MODULES_CACHE already is in the Python path.
if HF_MODULES_CACHE in sys.path:
return
sys.path.append(HF_MODULES_CACHE)
os.makedirs(HF_MODULES_CACHE, exist_ok=True)
init_path = Path(HF_MODULES_CACHE) / "__init__.py"
if not init_path.exists():
init_path.touch()
def create_dynamic_module(name: Union[str, os.PathLike]):
"""
Creates a dynamic module in the cache directory for modules.
"""
init_hf_modules()
dynamic_module_path = Path(HF_MODULES_CACHE) / name
# If the parent module does not exist yet, recursively create it.
if not dynamic_module_path.parent.exists():
create_dynamic_module(dynamic_module_path.parent)
os.makedirs(dynamic_module_path, exist_ok=True)
init_path = dynamic_module_path / "__init__.py"
if not init_path.exists():
init_path.touch()
def get_relative_imports(module_file):
"""
Get the list of modules that are relatively imported in a module file.
Args:
module_file (`str` or `os.PathLike`): The module file to inspect.
"""
with open(module_file, "r", encoding="utf-8") as f:
content = f.read()
# Imports of the form `import .xxx`
relative_imports = re.findall("^\s*import\s+\.(\S+)\s*$", content, flags=re.MULTILINE)
# Imports of the form `from .xxx import yyy`
relative_imports += re.findall("^\s*from\s+\.(\S+)\s+import", content, flags=re.MULTILINE)
# Unique-ify
return list(set(relative_imports))
def get_relative_import_files(module_file):
"""
Get the list of all files that are needed for a given module. Note that this function recurses through the relative
imports (if a imports b and b imports c, it will return module files for b and c).
Args:
module_file (`str` or `os.PathLike`): The module file to inspect.
"""
no_change = False
files_to_check = [module_file]
all_relative_imports = []
# Let's recurse through all relative imports
while not no_change:
new_imports = []
for f in files_to_check:
new_imports.extend(get_relative_imports(f))
module_path = Path(module_file).parent
new_import_files = [str(module_path / m) for m in new_imports]
new_import_files = [f for f in new_import_files if f not in all_relative_imports]
files_to_check = [f"{f}.py" for f in new_import_files]
no_change = len(new_import_files) == 0
all_relative_imports.extend(files_to_check)
return all_relative_imports
def check_imports(filename):
"""
Check if the current Python environment contains all the libraries that are imported in a file.
"""
with open(filename, "r", encoding="utf-8") as f:
content = f.read()
# Imports of the form `import xxx`
imports = re.findall("^\s*import\s+(\S+)\s*$", content, flags=re.MULTILINE)
# Imports of the form `from xxx import yyy`
imports += re.findall("^\s*from\s+(\S+)\s+import", content, flags=re.MULTILINE)
# Only keep the top-level module
imports = [imp.split(".")[0] for imp in imports if not imp.startswith(".")]
# Unique-ify and test we got them all
imports = list(set(imports))
missing_packages = []
for imp in imports:
try:
importlib.import_module(imp)
except ImportError:
missing_packages.append(imp)
if len(missing_packages) > 0:
raise ImportError(
"This modeling file requires the following packages that were not found in your environment: "
f"{', '.join(missing_packages)}. Run `pip install {' '.join(missing_packages)}`"
)
return get_relative_imports(filename)
def get_class_in_module(class_name, module_path):
"""
Import a module on the cache directory for modules and extract a class from it.
"""
module_path = module_path.replace(os.path.sep, ".")
module = importlib.import_module(module_path)
if class_name is None:
return find_pipeline_class(module)
return getattr(module, class_name)
def find_pipeline_class(loaded_module):
"""
Retrieve pipeline class that inherits from `DiffusionPipeline`. Note that there has to be exactly one class
inheriting from `DiffusionPipeline`.
"""
from .pipeline_utils import DiffusionPipeline
cls_members = dict(inspect.getmembers(loaded_module, inspect.isclass))
pipeline_class = None
for cls_name, cls in cls_members.items():
if (
cls_name != DiffusionPipeline.__name__
and issubclass(cls, DiffusionPipeline)
and cls.__module__.split(".")[0] != "diffusers"
):
if pipeline_class is not None:
raise ValueError(
f"Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:"
f" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in"
f" {loaded_module}."
)
pipeline_class = cls
return pipeline_class
def get_cached_module_file(
pretrained_model_name_or_path: Union[str, os.PathLike],
module_file: str,
cache_dir: Optional[Union[str, os.PathLike]] = None,
force_download: bool = False,
resume_download: bool = False,
proxies: Optional[Dict[str, str]] = None,
use_auth_token: Optional[Union[bool, str]] = None,
revision: Optional[str] = None,
local_files_only: bool = False,
):
"""
Prepares Downloads a module from a local folder or a distant repo and returns its path inside the cached
Transformers module.
Args:
pretrained_model_name_or_path (`str` or `os.PathLike`):
This can be either:
- a string, the *model id* of a pretrained model configuration hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced
under a user or organization name, like `dbmdz/bert-base-german-cased`.
- a path to a *directory* containing a configuration file saved using the
[`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.
module_file (`str`):
The name of the module file containing the class to look for.
cache_dir (`str` or `os.PathLike`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
cache should not be used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force to (re-)download the configuration files and override the cached versions if they
exist.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `transformers-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
local_files_only (`bool`, *optional*, defaults to `False`):
If `True`, will only try to load the tokenizer configuration from local files.
<Tip>
You may pass a token in `use_auth_token` if you are not logged in (`huggingface-cli long`) and want to use private
or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models).
</Tip>
Returns:
`str`: The path to the module inside the cache.
"""
# Download and cache module_file from the repo `pretrained_model_name_or_path` of grab it if it's a local file.
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
module_file_or_url = os.path.join(pretrained_model_name_or_path, module_file)
if os.path.isfile(module_file_or_url):
resolved_module_file = module_file_or_url
submodule = "local"
elif pretrained_model_name_or_path.count("/") == 0:
# community pipeline on GitHub
github_url = COMMUNITY_PIPELINES_URL.format(pipeline=pretrained_model_name_or_path)
try:
resolved_module_file = cached_download(
github_url,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
use_auth_token=False,
)
submodule = "git"
module_file = pretrained_model_name_or_path + ".py"
except EnvironmentError:
logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}.")
raise
else:
try:
# Load from URL or cache if already cached
resolved_module_file = hf_hub_download(
pretrained_model_name_or_path,
module_file,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
)
submodule = os.path.join("local", "--".join(pretrained_model_name_or_path.split("/")))
except EnvironmentError:
logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}.")
raise
# Check we have all the requirements in our environment
modules_needed = check_imports(resolved_module_file)
# Now we move the module inside our cached dynamic modules.
full_submodule = DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
create_dynamic_module(full_submodule)
submodule_path = Path(HF_MODULES_CACHE) / full_submodule
if submodule == "local" or submodule == "git":
# We always copy local files (we could hash the file to see if there was a change, and give them the name of
# that hash, to only copy when there is a modification but it seems overkill for now).
# The only reason we do the copy is to avoid putting too many folders in sys.path.
shutil.copy(resolved_module_file, submodule_path / module_file)
for module_needed in modules_needed:
module_needed = f"{module_needed}.py"
shutil.copy(os.path.join(pretrained_model_name_or_path, module_needed), submodule_path / module_needed)
else:
# Get the commit hash
# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
if isinstance(use_auth_token, str):
token = use_auth_token
elif use_auth_token is True:
token = HfFolder.get_token()
else:
token = None
commit_hash = model_info(pretrained_model_name_or_path, revision=revision, token=token).sha
# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
# benefit of versioning.
submodule_path = submodule_path / commit_hash
full_submodule = full_submodule + os.path.sep + commit_hash
create_dynamic_module(full_submodule)
if not (submodule_path / module_file).exists():
shutil.copy(resolved_module_file, submodule_path / module_file)
# Make sure we also have every file with relative
for module_needed in modules_needed:
if not (submodule_path / module_needed).exists():
get_cached_module_file(
pretrained_model_name_or_path,
f"{module_needed}.py",
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
use_auth_token=use_auth_token,
revision=revision,
local_files_only=local_files_only,
)
return os.path.join(full_submodule, module_file)
def get_class_from_dynamic_module(
pretrained_model_name_or_path: Union[str, os.PathLike],
module_file: str,
class_name: Optional[str] = None,
cache_dir: Optional[Union[str, os.PathLike]] = None,
force_download: bool = False,
resume_download: bool = False,
proxies: Optional[Dict[str, str]] = None,
use_auth_token: Optional[Union[bool, str]] = None,
revision: Optional[str] = None,
local_files_only: bool = False,
**kwargs,
):
"""
Extracts a class from a module file, present in the local folder or repository of a model.
<Tip warning={true}>
Calling this function will execute the code in the module file found locally or downloaded from the Hub. It should
therefore only be called on trusted repos.
</Tip>
Args:
pretrained_model_name_or_path (`str` or `os.PathLike`):
This can be either:
- a string, the *model id* of a pretrained model configuration hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced
under a user or organization name, like `dbmdz/bert-base-german-cased`.
- a path to a *directory* containing a configuration file saved using the
[`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.
module_file (`str`):
The name of the module file containing the class to look for.
class_name (`str`):
The name of the class to import in the module.
cache_dir (`str` or `os.PathLike`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
cache should not be used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force to (re-)download the configuration files and override the cached versions if they
exist.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
use_auth_token (`str` or `bool`, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `transformers-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
local_files_only (`bool`, *optional*, defaults to `False`):
If `True`, will only try to load the tokenizer configuration from local files.
<Tip>
You may pass a token in `use_auth_token` if you are not logged in (`huggingface-cli long`) and want to use private
or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models).
</Tip>
Returns:
`type`: The class, dynamically imported from the module.
Examples:
```python
# Download module `modeling.py` from huggingface.co and cache then extract the class `MyBertModel` from this
# module.
cls = get_class_from_dynamic_module("sgugger/my-bert-model", "modeling.py", "MyBertModel")
```"""
# And lastly we get the class inside our newly created module
final_module = get_cached_module_file(
pretrained_model_name_or_path,
module_file,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
use_auth_token=use_auth_token,
revision=revision,
local_files_only=local_files_only,
)
return get_class_in_module(class_name, final_module.replace(".py", ""))
| diffusers-ft-main | src/diffusers/dynamic_modules_utils.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from .utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
logger = logging.get_logger(__name__)
ORT_TO_NP_TYPE = {
"tensor(bool)": np.bool_,
"tensor(int8)": np.int8,
"tensor(uint8)": np.uint8,
"tensor(int16)": np.int16,
"tensor(uint16)": np.uint16,
"tensor(int32)": np.int32,
"tensor(uint32)": np.uint32,
"tensor(int64)": np.int64,
"tensor(uint64)": np.uint64,
"tensor(float16)": np.float16,
"tensor(float)": np.float32,
"tensor(double)": np.float64,
}
class OnnxRuntimeModel:
def __init__(self, model=None, **kwargs):
logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future.")
self.model = model
self.model_save_dir = kwargs.get("model_save_dir", None)
self.latest_model_name = kwargs.get("latest_model_name", ONNX_WEIGHTS_NAME)
def __call__(self, **kwargs):
inputs = {k: np.array(v) for k, v in kwargs.items()}
return self.model.run(None, inputs)
@staticmethod
def load_model(path: Union[str, Path], provider=None, sess_options=None):
"""
Loads an ONNX Inference session with an ExecutionProvider. Default provider is `CPUExecutionProvider`
Arguments:
path (`str` or `Path`):
Directory from which to load
provider(`str`, *optional*):
Onnxruntime execution provider to use for loading the model, defaults to `CPUExecutionProvider`
"""
if provider is None:
logger.info("No onnxruntime provider specified, using CPUExecutionProvider")
provider = "CPUExecutionProvider"
return ort.InferenceSession(path, providers=[provider], sess_options=sess_options)
def _save_pretrained(self, save_directory: Union[str, Path], file_name: Optional[str] = None, **kwargs):
"""
Save a model and its configuration file to a directory, so that it can be re-loaded using the
[`~optimum.onnxruntime.modeling_ort.ORTModel.from_pretrained`] class method. It will always save the
latest_model_name.
Arguments:
save_directory (`str` or `Path`):
Directory where to save the model file.
file_name(`str`, *optional*):
Overwrites the default model file name from `"model.onnx"` to `file_name`. This allows you to save the
model with a different name.
"""
model_file_name = file_name if file_name is not None else ONNX_WEIGHTS_NAME
src_path = self.model_save_dir.joinpath(self.latest_model_name)
dst_path = Path(save_directory).joinpath(model_file_name)
try:
shutil.copyfile(src_path, dst_path)
except shutil.SameFileError:
pass
# copy external weights (for models >2GB)
src_path = self.model_save_dir.joinpath(ONNX_EXTERNAL_WEIGHTS_NAME)
if src_path.exists():
dst_path = Path(save_directory).joinpath(ONNX_EXTERNAL_WEIGHTS_NAME)
try:
shutil.copyfile(src_path, dst_path)
except shutil.SameFileError:
pass
def save_pretrained(
self,
save_directory: Union[str, os.PathLike],
**kwargs,
):
"""
Save a model to a directory, so that it can be re-loaded using the [`~OnnxModel.from_pretrained`] class
method.:
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to which to save. Will be created if it doesn't exist.
"""
if os.path.isfile(save_directory):
logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
return
os.makedirs(save_directory, exist_ok=True)
# saving model weights/files
self._save_pretrained(save_directory, **kwargs)
@classmethod
def _from_pretrained(
cls,
model_id: Union[str, Path],
use_auth_token: Optional[Union[bool, str, None]] = None,
revision: Optional[Union[str, None]] = None,
force_download: bool = False,
cache_dir: Optional[str] = None,
file_name: Optional[str] = None,
provider: Optional[str] = None,
sess_options: Optional["ort.SessionOptions"] = None,
**kwargs,
):
"""
Load a model from a directory or the HF Hub.
Arguments:
model_id (`str` or `Path`):
Directory from which to load
use_auth_token (`str` or `bool`):
Is needed to load models from a private or gated repository
revision (`str`):
Revision is the specific model version to use. It can be a branch name, a tag name, or a commit id
cache_dir (`Union[str, Path]`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
file_name(`str`):
Overwrites the default model file name from `"model.onnx"` to `file_name`. This allows you to load
different model files from the same repository or directory.
provider(`str`):
The ONNX runtime provider, e.g. `CPUExecutionProvider` or `CUDAExecutionProvider`.
kwargs (`Dict`, *optional*):
kwargs will be passed to the model during initialization
"""
model_file_name = file_name if file_name is not None else ONNX_WEIGHTS_NAME
# load model from local directory
if os.path.isdir(model_id):
model = OnnxRuntimeModel.load_model(
os.path.join(model_id, model_file_name), provider=provider, sess_options=sess_options
)
kwargs["model_save_dir"] = Path(model_id)
# load model from hub
else:
# download model
model_cache_path = hf_hub_download(
repo_id=model_id,
filename=model_file_name,
use_auth_token=use_auth_token,
revision=revision,
cache_dir=cache_dir,
force_download=force_download,
)
kwargs["model_save_dir"] = Path(model_cache_path).parent
kwargs["latest_model_name"] = Path(model_cache_path).name
model = OnnxRuntimeModel.load_model(model_cache_path, provider=provider, sess_options=sess_options)
return cls(model=model, **kwargs)
@classmethod
def from_pretrained(
cls,
model_id: Union[str, Path],
force_download: bool = True,
use_auth_token: Optional[str] = None,
cache_dir: Optional[str] = None,
**model_kwargs,
):
revision = None
if len(str(model_id).split("@")) == 2:
model_id, revision = model_id.split("@")
return cls._from_pretrained(
model_id=model_id,
revision=revision,
cache_dir=cache_dir,
force_download=force_download,
use_auth_token=use_auth_token,
**model_kwargs,
)
| diffusers-ft-main | src/diffusers/onnx_utils.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
import inspect
import os
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
import diffusers
import PIL
from huggingface_hub import model_info, snapshot_download
from packaging import version
from PIL import Image
from tqdm.auto import tqdm
from .configuration_utils import ConfigMixin
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import http_user_agent
from .modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT
from .schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from .utils import (
CONFIG_NAME,
DIFFUSERS_CACHE,
ONNX_WEIGHTS_NAME,
WEIGHTS_NAME,
BaseOutput,
deprecate,
is_accelerate_available,
is_safetensors_available,
is_torch_version,
is_transformers_available,
logging,
)
if is_transformers_available():
import transformers
from transformers import PreTrainedModel
INDEX_FILE = "diffusion_pytorch_model.bin"
CUSTOM_PIPELINE_FILE_NAME = "pipeline.py"
DUMMY_MODULES_FOLDER = "diffusers.utils"
TRANSFORMERS_DUMMY_MODULES_FOLDER = "transformers.utils"
logger = logging.get_logger(__name__)
LOADABLE_CLASSES = {
"diffusers": {
"ModelMixin": ["save_pretrained", "from_pretrained"],
"SchedulerMixin": ["save_pretrained", "from_pretrained"],
"DiffusionPipeline": ["save_pretrained", "from_pretrained"],
"OnnxRuntimeModel": ["save_pretrained", "from_pretrained"],
},
"transformers": {
"PreTrainedTokenizer": ["save_pretrained", "from_pretrained"],
"PreTrainedTokenizerFast": ["save_pretrained", "from_pretrained"],
"PreTrainedModel": ["save_pretrained", "from_pretrained"],
"FeatureExtractionMixin": ["save_pretrained", "from_pretrained"],
"ProcessorMixin": ["save_pretrained", "from_pretrained"],
"ImageProcessingMixin": ["save_pretrained", "from_pretrained"],
},
"onnxruntime.training": {
"ORTModule": ["save_pretrained", "from_pretrained"],
},
}
ALL_IMPORTABLE_CLASSES = {}
for library in LOADABLE_CLASSES:
ALL_IMPORTABLE_CLASSES.update(LOADABLE_CLASSES[library])
@dataclass
class ImagePipelineOutput(BaseOutput):
"""
Output class for image pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
"""
images: Union[List[PIL.Image.Image], np.ndarray]
@dataclass
class AudioPipelineOutput(BaseOutput):
"""
Output class for audio pipelines.
Args:
audios (`np.ndarray`)
List of denoised samples of shape `(batch_size, num_channels, sample_rate)`. Numpy array present the
denoised audio samples of the diffusion pipeline.
"""
audios: np.ndarray
def is_safetensors_compatible(info) -> bool:
filenames = set(sibling.rfilename for sibling in info.siblings)
pt_filenames = set(filename for filename in filenames if filename.endswith(".bin"))
is_safetensors_compatible = any(file.endswith(".safetensors") for file in filenames)
for pt_filename in pt_filenames:
prefix, raw = os.path.split(pt_filename)
if raw == "pytorch_model.bin":
# transformers specific
sf_filename = os.path.join(prefix, "model.safetensors")
else:
sf_filename = pt_filename[: -len(".bin")] + ".safetensors"
if is_safetensors_compatible and sf_filename not in filenames:
logger.warning(f"{sf_filename} not found")
is_safetensors_compatible = False
return is_safetensors_compatible
class DiffusionPipeline(ConfigMixin):
r"""
Base class for all models.
[`DiffusionPipeline`] takes care of storing all components (models, schedulers, processors) for diffusion pipelines
and handles methods for loading, downloading and saving models as well as a few methods common to all pipelines to:
- move all PyTorch modules to the device of your choice
- enabling/disabling the progress bar for the denoising iteration
Class attributes:
- **config_name** (`str`) -- name of the config file that will store the class and module names of all
components of the diffusion pipeline.
- **_optional_components** (List[`str`]) -- list of all components that are optional so they don't have to be
passed for the pipeline to function (should be overridden by subclasses).
"""
config_name = "model_index.json"
_optional_components = []
def register_modules(self, **kwargs):
# import it here to avoid circular import
from diffusers import pipelines
for name, module in kwargs.items():
# retrieve library
if module is None:
register_dict = {name: (None, None)}
else:
library = module.__module__.split(".")[0]
# check if the module is a pipeline module
pipeline_dir = module.__module__.split(".")[-2] if len(module.__module__.split(".")) > 2 else None
path = module.__module__.split(".")
is_pipeline_module = pipeline_dir in path and hasattr(pipelines, pipeline_dir)
# if library is not in LOADABLE_CLASSES, then it is a custom module.
# Or if it's a pipeline module, then the module is inside the pipeline
# folder so we set the library to module name.
if library not in LOADABLE_CLASSES or is_pipeline_module:
library = pipeline_dir
# retrieve class_name
class_name = module.__class__.__name__
register_dict = {name: (library, class_name)}
# save model index config
self.register_to_config(**register_dict)
# set models
setattr(self, name, module)
def save_pretrained(self, save_directory: Union[str, os.PathLike]):
"""
Save all variables of the pipeline that can be saved and loaded as well as the pipelines configuration file to
a directory. A pipeline variable can be saved and loaded if its class implements both a save and loading
method. The pipeline can easily be re-loaded using the `[`~DiffusionPipeline.from_pretrained`]` class method.
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to which to save. Will be created if it doesn't exist.
"""
self.save_config(save_directory)
model_index_dict = dict(self.config)
model_index_dict.pop("_class_name")
model_index_dict.pop("_diffusers_version")
model_index_dict.pop("_module", None)
expected_modules, optional_kwargs = self._get_signature_keys(self)
def is_saveable_module(name, value):
if name not in expected_modules:
return False
if name in self._optional_components and value[0] is None:
return False
return True
model_index_dict = {k: v for k, v in model_index_dict.items() if is_saveable_module(k, v)}
for pipeline_component_name in model_index_dict.keys():
sub_model = getattr(self, pipeline_component_name)
model_cls = sub_model.__class__
save_method_name = None
# search for the model's base class in LOADABLE_CLASSES
for library_name, library_classes in LOADABLE_CLASSES.items():
library = importlib.import_module(library_name)
for base_class, save_load_methods in library_classes.items():
class_candidate = getattr(library, base_class, None)
if class_candidate is not None and issubclass(model_cls, class_candidate):
# if we found a suitable base class in LOADABLE_CLASSES then grab its save method
save_method_name = save_load_methods[0]
break
if save_method_name is not None:
break
save_method = getattr(sub_model, save_method_name)
save_method(os.path.join(save_directory, pipeline_component_name))
def to(self, torch_device: Optional[Union[str, torch.device]] = None):
if torch_device is None:
return self
module_names, _, _ = self.extract_init_dict(dict(self.config))
for name in module_names.keys():
module = getattr(self, name)
if isinstance(module, torch.nn.Module):
if module.dtype == torch.float16 and str(torch_device) in ["cpu"]:
logger.warning(
"Pipelines loaded with `torch_dtype=torch.float16` cannot run with `cpu` device. It"
" is not recommended to move them to `cpu` as running them will fail. Please make"
" sure to use an accelerator to run the pipeline in inference, due to the lack of"
" support for`float16` operations on this device in PyTorch. Please, remove the"
" `torch_dtype=torch.float16` argument, or use another device for inference."
)
module.to(torch_device)
return self
@property
def device(self) -> torch.device:
r"""
Returns:
`torch.device`: The torch device on which the pipeline is located.
"""
module_names, _, _ = self.extract_init_dict(dict(self.config))
for name in module_names.keys():
module = getattr(self, name)
if isinstance(module, torch.nn.Module):
return module.device
return torch.device("cpu")
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
r"""
Instantiate a PyTorch diffusion pipeline from pre-trained pipeline weights.
The pipeline is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated).
The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come
pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
task.
The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those
weights are discarded.
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
Can be either:
- A string, the *repo id* of a pretrained pipeline hosted inside a model repo on
https://huggingface.co/ Valid repo ids have to be located under a user or organization name, like
`CompVis/ldm-text2im-large-256`.
- A path to a *directory* containing pipeline weights saved using
[`~DiffusionPipeline.save_pretrained`], e.g., `./my_pipeline_directory/`.
torch_dtype (`str` or `torch.dtype`, *optional*):
Override the default `torch.dtype` and load the model under this dtype. If `"auto"` is passed the dtype
will be automatically derived from the model's weights.
custom_pipeline (`str`, *optional*):
<Tip warning={true}>
This is an experimental feature and is likely to change in the future.
</Tip>
Can be either:
- A string, the *repo id* of a custom pipeline hosted inside a model repo on
https://huggingface.co/. Valid repo ids have to be located under a user or organization name,
like `hf-internal-testing/diffusers-dummy-pipeline`.
<Tip>
It is required that the model repo has a file, called `pipeline.py` that defines the custom
pipeline.
</Tip>
- A string, the *file name* of a community pipeline hosted on GitHub under
https://github.com/huggingface/diffusers/tree/main/examples/community. Valid file names have to
match exactly the file name without `.py` located under the above link, *e.g.*
`clip_guided_stable_diffusion`.
<Tip>
Community pipelines are always loaded from the current `main` branch of GitHub.
</Tip>
- A path to a *directory* containing a custom pipeline, e.g., `./my_pipeline_directory/`.
<Tip>
It is required that the directory has a file, called `pipeline.py` that defines the custom
pipeline.
</Tip>
For more information on how to load and create custom pipelines, please have a look at [Loading and
Adding Custom
Pipelines](https://huggingface.co/docs/diffusers/using-diffusers/custom_pipeline_overview)
torch_dtype (`str` or `torch.dtype`, *optional*):
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
output_loading_info(`bool`, *optional*, defaults to `False`):
Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
local_files_only(`bool`, *optional*, defaults to `False`):
Whether or not to only look at local files (i.e., do not try to download the model).
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `huggingface-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
mirror (`str`, *optional*):
Mirror source to accelerate downloads in China. If you are from China and have an accessibility
problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
Please refer to the mirror site for more information. specify the folder name here.
device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
A map that specifies where each submodule should go. It doesn't need to be refined to each
parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the
same device.
To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For
more information about each option see [designing a device
map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
Speed up model loading by not initializing the weights and only loading the pre-trained weights. This
also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the
model. This is only supported when torch version >= 1.9.0. If you are using an older version of torch,
setting this argument to `True` will raise an error.
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to overwrite load - and saveable variables - *i.e.* the pipeline components - of the
specific pipeline class. The overwritten components are then directly passed to the pipelines
`__init__` method. See example below for more information.
<Tip>
It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
models](https://huggingface.co/docs/hub/models-gated#gated-models), *e.g.* `"runwayml/stable-diffusion-v1-5"`
</Tip>
<Tip>
Activate the special ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use
this method in a firewalled environment.
</Tip>
Examples:
```py
>>> from diffusers import DiffusionPipeline
>>> # Download pipeline from huggingface.co and cache.
>>> pipeline = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256")
>>> # Download pipeline that requires an authorization token
>>> # For more information on access tokens, please refer to this section
>>> # of the documentation](https://huggingface.co/docs/hub/security-tokens)
>>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> # Use a different scheduler
>>> from diffusers import LMSDiscreteScheduler
>>> scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config)
>>> pipeline.scheduler = scheduler
```
"""
cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
resume_download = kwargs.pop("resume_download", False)
force_download = kwargs.pop("force_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", False)
use_auth_token = kwargs.pop("use_auth_token", None)
revision = kwargs.pop("revision", None)
torch_dtype = kwargs.pop("torch_dtype", None)
custom_pipeline = kwargs.pop("custom_pipeline", None)
provider = kwargs.pop("provider", None)
sess_options = kwargs.pop("sess_options", None)
device_map = kwargs.pop("device_map", None)
low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
if low_cpu_mem_usage and not is_accelerate_available():
low_cpu_mem_usage = False
logger.warning(
"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
" environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
" `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
" install accelerate\n```\n."
)
if device_map is not None and not is_torch_version(">=", "1.9.0"):
raise NotImplementedError(
"Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set"
" `device_map=None`."
)
if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
raise NotImplementedError(
"Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
" `low_cpu_mem_usage=False`."
)
if low_cpu_mem_usage is False and device_map is not None:
raise ValueError(
f"You cannot set `low_cpu_mem_usage` to False while using device_map={device_map} for loading and"
" dispatching. Please make sure to set `low_cpu_mem_usage=True`."
)
# 1. Download the checkpoints and configs
# use snapshot download here to get it working from from_pretrained
if not os.path.isdir(pretrained_model_name_or_path):
config_dict = cls.load_config(
pretrained_model_name_or_path,
cache_dir=cache_dir,
resume_download=resume_download,
force_download=force_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
)
# make sure we only download sub-folders and `diffusers` filenames
folder_names = [k for k in config_dict.keys() if not k.startswith("_")]
allow_patterns = [os.path.join(k, "*") for k in folder_names]
allow_patterns += [WEIGHTS_NAME, SCHEDULER_CONFIG_NAME, CONFIG_NAME, ONNX_WEIGHTS_NAME, cls.config_name]
# make sure we don't download flax weights
ignore_patterns = ["*.msgpack"]
if custom_pipeline is not None:
allow_patterns += [CUSTOM_PIPELINE_FILE_NAME]
if cls != DiffusionPipeline:
requested_pipeline_class = cls.__name__
else:
requested_pipeline_class = config_dict.get("_class_name", cls.__name__)
user_agent = {"pipeline_class": requested_pipeline_class}
if custom_pipeline is not None:
user_agent["custom_pipeline"] = custom_pipeline
user_agent = http_user_agent(user_agent)
if is_safetensors_available():
info = model_info(
pretrained_model_name_or_path,
use_auth_token=use_auth_token,
revision=revision,
)
if is_safetensors_compatible(info):
ignore_patterns.append("*.bin")
# download all allow_patterns
cached_folder = snapshot_download(
pretrained_model_name_or_path,
cache_dir=cache_dir,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
allow_patterns=allow_patterns,
ignore_patterns=ignore_patterns,
user_agent=user_agent,
)
else:
cached_folder = pretrained_model_name_or_path
config_dict = cls.load_config(cached_folder)
# 2. Load the pipeline class, if using custom module then load it from the hub
# if we load from explicit class, let's use it
if custom_pipeline is not None:
if custom_pipeline.endswith(".py"):
path = Path(custom_pipeline)
# decompose into folder & file
file_name = path.name
custom_pipeline = path.parent.absolute()
else:
file_name = CUSTOM_PIPELINE_FILE_NAME
pipeline_class = get_class_from_dynamic_module(
custom_pipeline, module_file=file_name, cache_dir=custom_pipeline
)
elif cls != DiffusionPipeline:
pipeline_class = cls
else:
diffusers_module = importlib.import_module(cls.__module__.split(".")[0])
pipeline_class = getattr(diffusers_module, config_dict["_class_name"])
# To be removed in 1.0.0
if pipeline_class.__name__ == "StableDiffusionInpaintPipeline" and version.parse(
version.parse(config_dict["_diffusers_version"]).base_version
) <= version.parse("0.5.1"):
from diffusers import StableDiffusionInpaintPipeline, StableDiffusionInpaintPipelineLegacy
pipeline_class = StableDiffusionInpaintPipelineLegacy
deprecation_message = (
"You are using a legacy checkpoint for inpainting with Stable Diffusion, therefore we are loading the"
f" {StableDiffusionInpaintPipelineLegacy} class instead of {StableDiffusionInpaintPipeline}. For"
" better inpainting results, we strongly suggest using Stable Diffusion's official inpainting"
" checkpoint: https://huggingface.co/runwayml/stable-diffusion-inpainting instead or adapting your"
f" checkpoint {pretrained_model_name_or_path} to the format of"
" https://huggingface.co/runwayml/stable-diffusion-inpainting. Note that we do not actively maintain"
" the {StableDiffusionInpaintPipelineLegacy} class and will likely remove it in version 1.0.0."
)
deprecate("StableDiffusionInpaintPipelineLegacy", "1.0.0", deprecation_message, standard_warn=False)
# some modules can be passed directly to the init
# in this case they are already instantiated in `kwargs`
# extract them here
expected_modules, optional_kwargs = cls._get_signature_keys(pipeline_class)
passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}
init_dict, unused_kwargs, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)
# define init kwargs
init_kwargs = {k: init_dict.pop(k) for k in optional_kwargs if k in init_dict}
init_kwargs = {**init_kwargs, **passed_pipe_kwargs}
# remove `null` components
def load_module(name, value):
if value[0] is None:
return False
if name in passed_class_obj and passed_class_obj[name] is None:
return False
return True
init_dict = {k: v for k, v in init_dict.items() if load_module(k, v)}
if len(unused_kwargs) > 0:
logger.warning(
f"Keyword arguments {unused_kwargs} are not expected by {pipeline_class.__name__} and will be ignored."
)
# import it here to avoid circular import
from diffusers import pipelines
# 3. Load each module in the pipeline
for name, (library_name, class_name) in init_dict.items():
# 3.1 - now that JAX/Flax is an official framework of the library, we might load from Flax names
if class_name.startswith("Flax"):
class_name = class_name[4:]
is_pipeline_module = hasattr(pipelines, library_name)
loaded_sub_model = None
# if the model is in a pipeline module, then we load it from the pipeline
if name in passed_class_obj:
# 1. check that passed_class_obj has correct parent class
if not is_pipeline_module:
library = importlib.import_module(library_name)
class_obj = getattr(library, class_name)
importable_classes = LOADABLE_CLASSES[library_name]
class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()}
expected_class_obj = None
for class_name, class_candidate in class_candidates.items():
if class_candidate is not None and issubclass(class_obj, class_candidate):
expected_class_obj = class_candidate
if not issubclass(passed_class_obj[name].__class__, expected_class_obj):
raise ValueError(
f"{passed_class_obj[name]} is of type: {type(passed_class_obj[name])}, but should be"
f" {expected_class_obj}"
)
else:
logger.warning(
f"You have passed a non-standard module {passed_class_obj[name]}. We cannot verify whether it"
" has the correct type"
)
# set passed class object
loaded_sub_model = passed_class_obj[name]
elif is_pipeline_module:
pipeline_module = getattr(pipelines, library_name)
class_obj = getattr(pipeline_module, class_name)
importable_classes = ALL_IMPORTABLE_CLASSES
class_candidates = {c: class_obj for c in importable_classes.keys()}
else:
# else we just import it from the library.
library = importlib.import_module(library_name)
class_obj = getattr(library, class_name)
importable_classes = LOADABLE_CLASSES[library_name]
class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()}
if loaded_sub_model is None:
load_method_name = None
for class_name, class_candidate in class_candidates.items():
if class_candidate is not None and issubclass(class_obj, class_candidate):
load_method_name = importable_classes[class_name][1]
if load_method_name is None:
none_module = class_obj.__module__
is_dummy_path = none_module.startswith(DUMMY_MODULES_FOLDER) or none_module.startswith(
TRANSFORMERS_DUMMY_MODULES_FOLDER
)
if is_dummy_path and "dummy" in none_module:
# call class_obj for nice error message of missing requirements
class_obj()
raise ValueError(
f"The component {class_obj} of {pipeline_class} cannot be loaded as it does not seem to have"
f" any of the loading methods defined in {ALL_IMPORTABLE_CLASSES}."
)
load_method = getattr(class_obj, load_method_name)
loading_kwargs = {}
if issubclass(class_obj, torch.nn.Module):
loading_kwargs["torch_dtype"] = torch_dtype
if issubclass(class_obj, diffusers.OnnxRuntimeModel):
loading_kwargs["provider"] = provider
loading_kwargs["sess_options"] = sess_options
is_diffusers_model = issubclass(class_obj, diffusers.ModelMixin)
is_transformers_model = (
is_transformers_available()
and issubclass(class_obj, PreTrainedModel)
and version.parse(version.parse(transformers.__version__).base_version) >= version.parse("4.20.0")
)
# When loading a transformers model, if the device_map is None, the weights will be initialized as opposed to diffusers.
# To make default loading faster we set the `low_cpu_mem_usage=low_cpu_mem_usage` flag which is `True` by default.
# This makes sure that the weights won't be initialized which significantly speeds up loading.
if is_diffusers_model or is_transformers_model:
loading_kwargs["device_map"] = device_map
loading_kwargs["low_cpu_mem_usage"] = low_cpu_mem_usage
# check if the module is in a subdirectory
if os.path.isdir(os.path.join(cached_folder, name)):
loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs)
else:
# else load from the root directory
loaded_sub_model = load_method(cached_folder, **loading_kwargs)
init_kwargs[name] = loaded_sub_model # UNet(...), # DiffusionSchedule(...)
# 4. Potentially add passed objects if expected
missing_modules = set(expected_modules) - set(init_kwargs.keys())
passed_modules = list(passed_class_obj.keys())
optional_modules = pipeline_class._optional_components
if len(missing_modules) > 0 and missing_modules <= set(passed_modules + optional_modules):
for module in missing_modules:
init_kwargs[module] = passed_class_obj.get(module, None)
elif len(missing_modules) > 0:
passed_modules = set(list(init_kwargs.keys()) + list(passed_class_obj.keys())) - optional_kwargs
raise ValueError(
f"Pipeline {pipeline_class} expected {expected_modules}, but only {passed_modules} were passed."
)
# 5. Instantiate the pipeline
model = pipeline_class(**init_kwargs)
return model
@staticmethod
def _get_signature_keys(obj):
parameters = inspect.signature(obj.__init__).parameters
required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty}
optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty})
expected_modules = set(required_parameters.keys()) - set(["self"])
return expected_modules, optional_parameters
@property
def components(self) -> Dict[str, Any]:
r"""
The `self.components` property can be useful to run different pipelines with the same weights and
configurations to not have to re-allocate memory.
Examples:
```py
>>> from diffusers import (
... StableDiffusionPipeline,
... StableDiffusionImg2ImgPipeline,
... StableDiffusionInpaintPipeline,
... )
>>> text2img = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> img2img = StableDiffusionImg2ImgPipeline(**text2img.components)
>>> inpaint = StableDiffusionInpaintPipeline(**text2img.components)
```
Returns:
A dictionaly containing all the modules needed to initialize the pipeline.
"""
expected_modules, optional_parameters = self._get_signature_keys(self)
components = {
k: getattr(self, k) for k in self.config.keys() if not k.startswith("_") and k not in optional_parameters
}
if set(components.keys()) != expected_modules:
raise ValueError(
f"{self} has been incorrectly initialized or {self.__class__} is incorrectly implemented. Expected"
f" {expected_modules} to be defined, but {components} are defined."
)
return components
@staticmethod
def numpy_to_pil(images):
"""
Convert a numpy image or a batch of images to a PIL image.
"""
if images.ndim == 3:
images = images[None, ...]
images = (images * 255).round().astype("uint8")
if images.shape[-1] == 1:
# special case for grayscale (single channel) images
pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images]
else:
pil_images = [Image.fromarray(image) for image in images]
return pil_images
def progress_bar(self, iterable=None, total=None):
if not hasattr(self, "_progress_bar_config"):
self._progress_bar_config = {}
elif not isinstance(self._progress_bar_config, dict):
raise ValueError(
f"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}."
)
if iterable is not None:
return tqdm(iterable, **self._progress_bar_config)
elif total is not None:
return tqdm(total=total, **self._progress_bar_config)
else:
raise ValueError("Either `total` or `iterable` has to be defined.")
def set_progress_bar_config(self, **kwargs):
self._progress_bar_config = kwargs
| diffusers-ft-main | src/diffusers/pipeline_utils.py |
from .rl import ValueGuidedRLPipeline
| diffusers-ft-main | src/diffusers/experimental/__init__.py |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
import tqdm
from ...models.unet_1d import UNet1DModel
from ...pipeline_utils import DiffusionPipeline
from ...utils.dummy_pt_objects import DDPMScheduler
class ValueGuidedRLPipeline(DiffusionPipeline):
def __init__(
self,
value_function: UNet1DModel,
unet: UNet1DModel,
scheduler: DDPMScheduler,
env,
):
super().__init__()
self.value_function = value_function
self.unet = unet
self.scheduler = scheduler
self.env = env
self.data = env.get_dataset()
self.means = dict()
for key in self.data.keys():
try:
self.means[key] = self.data[key].mean()
except:
pass
self.stds = dict()
for key in self.data.keys():
try:
self.stds[key] = self.data[key].std()
except:
pass
self.state_dim = env.observation_space.shape[0]
self.action_dim = env.action_space.shape[0]
def normalize(self, x_in, key):
return (x_in - self.means[key]) / self.stds[key]
def de_normalize(self, x_in, key):
return x_in * self.stds[key] + self.means[key]
def to_torch(self, x_in):
if type(x_in) is dict:
return {k: self.to_torch(v) for k, v in x_in.items()}
elif torch.is_tensor(x_in):
return x_in.to(self.unet.device)
return torch.tensor(x_in, device=self.unet.device)
def reset_x0(self, x_in, cond, act_dim):
for key, val in cond.items():
x_in[:, key, act_dim:] = val.clone()
return x_in
def run_diffusion(self, x, conditions, n_guide_steps, scale):
batch_size = x.shape[0]
y = None
for i in tqdm.tqdm(self.scheduler.timesteps):
# create batch of timesteps to pass into model
timesteps = torch.full((batch_size,), i, device=self.unet.device, dtype=torch.long)
for _ in range(n_guide_steps):
with torch.enable_grad():
x.requires_grad_()
y = self.value_function(x.permute(0, 2, 1), timesteps).sample
grad = torch.autograd.grad([y.sum()], [x])[0]
posterior_variance = self.scheduler._get_variance(i)
model_std = torch.exp(0.5 * posterior_variance)
grad = model_std * grad
grad[timesteps < 2] = 0
x = x.detach()
x = x + scale * grad
x = self.reset_x0(x, conditions, self.action_dim)
prev_x = self.unet(x.permute(0, 2, 1), timesteps).sample.permute(0, 2, 1)
# TODO: set prediction_type when instantiating the model
x = self.scheduler.step(prev_x, i, x, predict_epsilon=False)["prev_sample"]
# apply conditions to the trajectory
x = self.reset_x0(x, conditions, self.action_dim)
x = self.to_torch(x)
return x, y
def __call__(self, obs, batch_size=64, planning_horizon=32, n_guide_steps=2, scale=0.1):
# normalize the observations and create batch dimension
obs = self.normalize(obs, "observations")
obs = obs[None].repeat(batch_size, axis=0)
conditions = {0: self.to_torch(obs)}
shape = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
x1 = torch.randn(shape, device=self.unet.device)
x = self.reset_x0(x1, conditions, self.action_dim)
x = self.to_torch(x)
# run the diffusion process
x, y = self.run_diffusion(x, conditions, n_guide_steps, scale)
# sort output trajectories by value
sorted_idx = y.argsort(0, descending=True).squeeze()
sorted_values = x[sorted_idx]
actions = sorted_values[:, :, : self.action_dim]
actions = actions.detach().cpu().numpy()
denorm_actions = self.de_normalize(actions, key="actions")
# select the action with the highest value
if y is not None:
selected_index = 0
else:
# if we didn't run value guiding, select a random action
selected_index = np.random.randint(0, batch_size)
denorm_actions = denorm_actions[selected_index, 0]
return denorm_actions
| diffusers-ft-main | src/diffusers/experimental/rl/value_guided_sampling.py |
from .value_guided_sampling import ValueGuidedRLPipeline
| diffusers-ft-main | src/diffusers/experimental/rl/__init__.py |
from ..utils import is_flax_available, is_onnx_available, is_torch_available, is_transformers_available
if is_torch_available():
from .dance_diffusion import DanceDiffusionPipeline
from .ddim import DDIMPipeline
from .ddpm import DDPMPipeline
from .latent_diffusion import LDMSuperResolutionPipeline
from .latent_diffusion_uncond import LDMPipeline
from .pndm import PNDMPipeline
from .repaint import RePaintPipeline
from .score_sde_ve import ScoreSdeVePipeline
from .stochastic_karras_ve import KarrasVePipeline
else:
from ..utils.dummy_pt_objects import * # noqa F403
if is_torch_available() and is_transformers_available():
from .alt_diffusion import AltDiffusionImg2ImgPipeline, AltDiffusionPipeline
from .latent_diffusion import LDMTextToImagePipeline
from .stable_diffusion import (
CycleDiffusionPipeline,
StableDiffusionImageVariationPipeline,
StableDiffusionImg2ImgPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionPipeline,
StableDiffusionUpscalePipeline,
)
from .stable_diffusion_safe import StableDiffusionPipelineSafe
from .versatile_diffusion import (
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
)
from .vq_diffusion import VQDiffusionPipeline
if is_transformers_available() and is_onnx_available():
from .stable_diffusion import (
OnnxStableDiffusionImg2ImgPipeline,
OnnxStableDiffusionInpaintPipeline,
OnnxStableDiffusionInpaintPipelineLegacy,
OnnxStableDiffusionPipeline,
StableDiffusionOnnxPipeline,
)
if is_transformers_available() and is_flax_available():
from .stable_diffusion import FlaxStableDiffusionPipeline
| diffusers-ft-main | src/diffusers/pipelines/__init__.py |
from .pipeline_repaint import RePaintPipeline
| diffusers-ft-main | src/diffusers/pipelines/repaint/__init__.py |
# Copyright 2022 ETH Zurich Computer Vision Lab and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Tuple, Union
import numpy as np
import torch
import PIL
from tqdm.auto import tqdm
from ...models import UNet2DModel
from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from ...schedulers import RePaintScheduler
def _preprocess_image(image: PIL.Image.Image):
image = np.array(image.convert("RGB"))
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
return image
def _preprocess_mask(mask: PIL.Image.Image):
mask = np.array(mask.convert("L"))
mask = mask.astype(np.float32) / 255.0
mask = mask[None, None]
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
mask = torch.from_numpy(mask)
return mask
class RePaintPipeline(DiffusionPipeline):
unet: UNet2DModel
scheduler: RePaintScheduler
def __init__(self, unet, scheduler):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
original_image: Union[torch.FloatTensor, PIL.Image.Image],
mask_image: Union[torch.FloatTensor, PIL.Image.Image],
num_inference_steps: int = 250,
eta: float = 0.0,
jump_length: int = 10,
jump_n_sample: int = 10,
generator: Optional[torch.Generator] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
) -> Union[ImagePipelineOutput, Tuple]:
r"""
Args:
original_image (`torch.FloatTensor` or `PIL.Image.Image`):
The original image to inpaint on.
mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
The mask_image where 0.0 values define which part of the original image to inpaint (change).
num_inference_steps (`int`, *optional*, defaults to 1000):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
eta (`float`):
The weight of noise for added noise in a diffusion step. Its value is between 0.0 and 1.0 - 0.0 is DDIM
and 1.0 is DDPM scheduler respectively.
jump_length (`int`, *optional*, defaults to 10):
The number of steps taken forward in time before going backward in time for a single jump ("j" in
RePaint paper). Take a look at Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf.
jump_n_sample (`int`, *optional*, defaults to 10):
The number of times we will make forward time jump for a given chosen time sample. Take a look at
Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
Returns:
[`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
generated images.
"""
if not isinstance(original_image, torch.FloatTensor):
original_image = _preprocess_image(original_image)
original_image = original_image.to(self.device)
if not isinstance(mask_image, torch.FloatTensor):
mask_image = _preprocess_mask(mask_image)
mask_image = mask_image.to(self.device)
# sample gaussian noise to begin the loop
image = torch.randn(
original_image.shape,
generator=generator,
device=self.device,
)
image = image.to(self.device)
# set step values
self.scheduler.set_timesteps(num_inference_steps, jump_length, jump_n_sample, self.device)
self.scheduler.eta = eta
t_last = self.scheduler.timesteps[0] + 1
for i, t in enumerate(tqdm(self.scheduler.timesteps)):
if t < t_last:
# predict the noise residual
model_output = self.unet(image, t).sample
# compute previous image: x_t -> x_t-1
image = self.scheduler.step(model_output, t, image, original_image, mask_image, generator).prev_sample
else:
# compute the reverse: x_t-1 -> x_t
image = self.scheduler.undo_step(image, t_last, generator)
t_last = t
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| diffusers-ft-main | src/diffusers/pipelines/repaint/pipeline_repaint.py |
# flake8: noqa
from .pipeline_stochastic_karras_ve import KarrasVePipeline
| diffusers-ft-main | src/diffusers/pipelines/stochastic_karras_ve/__init__.py |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from ...models import UNet2DModel
from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from ...schedulers import KarrasVeScheduler
class KarrasVePipeline(DiffusionPipeline):
r"""
Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2]. Use Algorithm 2 and
the VE column of Table 1 from [1] for reference.
[1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models."
https://arxiv.org/abs/2206.00364 [2] Song, Yang, et al. "Score-based generative modeling through stochastic
differential equations." https://arxiv.org/abs/2011.13456
Parameters:
unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
scheduler ([`KarrasVeScheduler`]):
Scheduler for the diffusion process to be used in combination with `unet` to denoise the encoded image.
"""
# add type hints for linting
unet: UNet2DModel
scheduler: KarrasVeScheduler
def __init__(self, unet: UNet2DModel, scheduler: KarrasVeScheduler):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
batch_size: int = 1,
num_inference_steps: int = 50,
generator: Optional[torch.Generator] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
**kwargs,
) -> Union[Tuple, ImagePipelineOutput]:
r"""
Args:
batch_size (`int`, *optional*, defaults to 1):
The number of images to generate.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
Returns:
[`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
generated images.
"""
img_size = self.unet.config.sample_size
shape = (batch_size, 3, img_size, img_size)
model = self.unet
# sample x_0 ~ N(0, sigma_0^2 * I)
sample = torch.randn(*shape) * self.scheduler.init_noise_sigma
sample = sample.to(self.device)
self.scheduler.set_timesteps(num_inference_steps)
for t in self.progress_bar(self.scheduler.timesteps):
# here sigma_t == t_i from the paper
sigma = self.scheduler.schedule[t]
sigma_prev = self.scheduler.schedule[t - 1] if t > 0 else 0
# 1. Select temporarily increased noise level sigma_hat
# 2. Add new noise to move from sample_i to sample_hat
sample_hat, sigma_hat = self.scheduler.add_noise_to_input(sample, sigma, generator=generator)
# 3. Predict the noise residual given the noise magnitude `sigma_hat`
# The model inputs and output are adjusted by following eq. (213) in [1].
model_output = (sigma_hat / 2) * model((sample_hat + 1) / 2, sigma_hat / 2).sample
# 4. Evaluate dx/dt at sigma_hat
# 5. Take Euler step from sigma to sigma_prev
step_output = self.scheduler.step(model_output, sigma_hat, sigma_prev, sample_hat)
if sigma_prev != 0:
# 6. Apply 2nd order correction
# The model inputs and output are adjusted by following eq. (213) in [1].
model_output = (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2, sigma_prev / 2).sample
step_output = self.scheduler.step_correct(
model_output,
sigma_hat,
sigma_prev,
sample_hat,
step_output.prev_sample,
step_output["derivative"],
)
sample = step_output.prev_sample
sample = (sample / 2 + 0.5).clamp(0, 1)
image = sample.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(sample)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| diffusers-ft-main | src/diffusers/pipelines/stochastic_karras_ve/pipeline_stochastic_karras_ve.py |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from ...utils import deprecate
class DDIMPipeline(DiffusionPipeline):
r"""
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Parameters:
unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
[`DDPMScheduler`], or [`DDIMScheduler`].
"""
def __init__(self, unet, scheduler):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
batch_size: int = 1,
generator: Optional[torch.Generator] = None,
eta: float = 0.0,
num_inference_steps: int = 50,
use_clipped_model_output: Optional[bool] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
**kwargs,
) -> Union[ImagePipelineOutput, Tuple]:
r"""
Args:
batch_size (`int`, *optional*, defaults to 1):
The number of images to generate.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
eta (`float`, *optional*, defaults to 0.0):
The eta parameter which controls the scale of the variance (0 is DDIM and 1 is one type of DDPM).
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
use_clipped_model_output (`bool`, *optional*, defaults to `None`):
if `True` or `False`, see documentation for `DDIMScheduler.step`. If `None`, nothing is passed
downstream to the scheduler. So use `None` for schedulers which don't support this argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
Returns:
[`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
generated images.
"""
if generator is not None and generator.device.type != self.device.type and self.device.type != "mps":
message = (
f"The `generator` device is `{generator.device}` and does not match the pipeline "
f"device `{self.device}`, so the `generator` will be ignored. "
f'Please use `generator=torch.Generator(device="{self.device}")` instead.'
)
deprecate(
"generator.device == 'cpu'",
"0.11.0",
message,
)
generator = None
# Sample gaussian noise to begin loop
if isinstance(self.unet.sample_size, int):
image_shape = (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size)
else:
image_shape = (batch_size, self.unet.in_channels, *self.unet.sample_size)
if self.device.type == "mps":
# randn does not work reproducibly on mps
image = torch.randn(image_shape, generator=generator)
image = image.to(self.device)
else:
image = torch.randn(image_shape, generator=generator, device=self.device)
# set step values
self.scheduler.set_timesteps(num_inference_steps)
for t in self.progress_bar(self.scheduler.timesteps):
# 1. predict noise model_output
model_output = self.unet(image, t).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
image = self.scheduler.step(
model_output, t, image, eta=eta, use_clipped_model_output=use_clipped_model_output, generator=generator
).prev_sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| diffusers-ft-main | src/diffusers/pipelines/ddim/pipeline_ddim.py |
# flake8: noqa
from .pipeline_ddim import DDIMPipeline
| diffusers-ft-main | src/diffusers/pipelines/ddim/__init__.py |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from typing import Callable, List, Optional, Union
import numpy as np
import torch
import PIL
from diffusers.utils import is_accelerate_available
from packaging import version
from transformers import CLIPFeatureExtractor, XLMRobertaTokenizer
from ...configuration_utils import FrozenDict
from ...models import AutoencoderKL, UNet2DConditionModel
from ...pipeline_utils import DiffusionPipeline
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, deprecate, logging
from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from . import AltDiffusionPipelineOutput, RobertaSeriesModelWithTransformation
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
def preprocess(image):
w, h = image.size
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.0 * image - 1.0
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline with Stable->Alt, CLIPTextModel->RobertaSeriesModelWithTransformation, CLIPTokenizer->XLMRobertaTokenizer, AltDiffusionSafetyChecker->StableDiffusionSafetyChecker
class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
r"""
Pipeline for text-guided image to image generation using Alt Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`RobertaSeriesModelWithTransformation`]):
Frozen text-encoder. Alt Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.RobertaSeriesModelWithTransformation),
specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`XLMRobertaTokenizer`):
Tokenizer of class
[XLMRobertaTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.XLMRobertaTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: RobertaSeriesModelWithTransformation,
tokenizer: XLMRobertaTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
requires_safety_checker: bool = True,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
)
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["clip_sample"] = False
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Alt Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.register_to_config(requires_safety_checker=requires_safety_checker)
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.
Args:
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
if isinstance(self.unet.config.attention_head_dim, int):
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
else:
# if `attention_head_dim` is a list, take the smallest head size
slice_size = min(self.unet.config.attention_head_dim)
self.unet.set_attention_slice(slice_size)
def disable_attention_slicing(self):
r"""
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
back to computing attention in one step.
"""
# set slice_size = `None` to disable `attention slicing`
self.enable_attention_slicing(None)
def enable_sequential_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
`torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
device = torch.device(f"cuda:{gpu_id}")
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
if cpu_offloaded_model is not None:
cpu_offload(cpu_offloaded_model, device)
if self.safety_checker is not None:
# TODO(Patrick) - there is currently a bug with cpu offload of nn.Parameter in accelerate
# fix by only offloading self.safety_checker for now
cpu_offload(self.safety_checker.vision_model, device)
@property
def _execution_device(self):
r"""
Returns the device on which the pipeline's models will be executed. After calling
`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
hooks.
"""
if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
return self.device
for module in self.unet.modules():
if (
hasattr(module, "_hf_hook")
and hasattr(module._hf_hook, "execution_device")
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device)
return self.device
def enable_xformers_memory_efficient_attention(self):
r"""
Enable memory efficient attention as implemented in xformers.
When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference
time. Speed up at training time is not guaranteed.
Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention
is used.
"""
self.unet.set_use_memory_efficient_attention_xformers(True)
def disable_xformers_memory_efficient_attention(self):
r"""
Disable memory efficient attention as implemented in xformers.
"""
self.unet.set_use_memory_efficient_attention_xformers(False)
def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `list(int)`):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
"""
batch_size = len(prompt) if isinstance(prompt, list) else 1
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids
if not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
text_embeddings = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
text_embeddings = text_embeddings[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
uncond_embeddings = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
uncond_embeddings = uncond_embeddings[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_embeddings.shape[1]
uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
return text_embeddings
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
else:
has_nsfw_concept = None
return image, has_nsfw_concept
def decode_latents(self, latents):
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(self, prompt, strength, callback_steps):
if not isinstance(prompt, str) and not isinstance(prompt, list):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [1.0, 1.0] but is {strength}")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
offset = self.scheduler.config.get("steps_offset", 0)
init_timestep = int(num_inference_steps * strength) + offset
init_timestep = min(init_timestep, num_inference_steps)
t_start = max(num_inference_steps - init_timestep + offset, 0)
timesteps = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def prepare_latents(self, init_image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
init_image = init_image.to(device=device, dtype=dtype)
init_latent_dist = self.vae.encode(init_image).latent_dist
init_latents = init_latent_dist.sample(generator=generator)
init_latents = 0.18215 * init_latents
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
# expand init_latents for batch_size
deprecation_message = (
f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
" images (`init_image`). Initial images are now duplicating to match the number of text prompts. Note"
" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
" your script to pass as many init images as text prompts to suppress this warning."
)
deprecate("len(prompt) != len(init_image)", "1.0.0", deprecation_message, standard_warn=False)
additional_image_per_prompt = batch_size // init_latents.shape[0]
init_latents = torch.cat([init_latents] * additional_image_per_prompt * num_images_per_prompt, dim=0)
elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
raise ValueError(
f"Cannot duplicate `init_image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
)
else:
init_latents = torch.cat([init_latents] * num_images_per_prompt, dim=0)
# add noise to latents using the timesteps
noise = torch.randn(init_latents.shape, generator=generator, device=device, dtype=dtype)
# get latents
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
latents = init_latents
return latents
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
init_image: Union[torch.FloatTensor, PIL.Image.Image],
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: Optional[float] = 0.0,
generator: Optional[torch.Generator] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
init_image (`torch.FloatTensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to transform the reference `init_image`. Must be between 0 and 1.
`init_image` will be used as a starting point, adding more noise to it the larger the `strength`. The
number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
noise will be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `init_image`.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter will be modulated by `strength`.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
[`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
# 1. Check inputs
self.check_inputs(prompt, strength, callback_steps)
# 2. Define call parameters
batch_size = 1 if isinstance(prompt, str) else len(prompt)
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
text_embeddings = self._encode_prompt(
prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
# 4. Preprocess image
if isinstance(init_image, PIL.Image.Image):
init_image = preprocess(init_image)
# 5. set timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
# 6. Prepare latent variables
latents = self.prepare_latents(
init_image, latent_timestep, batch_size, num_images_per_prompt, text_embeddings.dtype, device, generator
)
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 8. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0:
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
# 9. Post-processing
image = self.decode_latents(latents)
# 10. Run safety checker
image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)
# 11. Convert to PIL
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return AltDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| diffusers-ft-main | src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class AltDiffusionPipelineOutput(BaseOutput):
"""
Output class for Alt Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
nsfw_content_detected (`List[bool]`)
List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, or `None` if safety checking could not be performed.
"""
images: Union[List[PIL.Image.Image], np.ndarray]
nsfw_content_detected: Optional[List[bool]]
if is_transformers_available() and is_torch_available():
from .modeling_roberta_series import RobertaSeriesModelWithTransformation
from .pipeline_alt_diffusion import AltDiffusionPipeline
from .pipeline_alt_diffusion_img2img import AltDiffusionImg2ImgPipeline
| diffusers-ft-main | src/diffusers/pipelines/alt_diffusion/__init__.py |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from typing import Callable, List, Optional, Union
import torch
from diffusers.utils import is_accelerate_available
from packaging import version
from transformers import CLIPFeatureExtractor, XLMRobertaTokenizer
from ...configuration_utils import FrozenDict
from ...models import AutoencoderKL, UNet2DConditionModel
from ...pipeline_utils import DiffusionPipeline
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import deprecate, logging
from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from . import AltDiffusionPipelineOutput, RobertaSeriesModelWithTransformation
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline with Stable->Alt, CLIPTextModel->RobertaSeriesModelWithTransformation, CLIPTokenizer->XLMRobertaTokenizer, AltDiffusionSafetyChecker->StableDiffusionSafetyChecker
class AltDiffusionPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using Alt Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`RobertaSeriesModelWithTransformation`]):
Frozen text-encoder. Alt Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.RobertaSeriesModelWithTransformation),
specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`XLMRobertaTokenizer`):
Tokenizer of class
[XLMRobertaTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.XLMRobertaTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: RobertaSeriesModelWithTransformation,
tokenizer: XLMRobertaTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
requires_safety_checker: bool = True,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
)
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["clip_sample"] = False
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Alt Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.register_to_config(requires_safety_checker=requires_safety_checker)
def enable_xformers_memory_efficient_attention(self):
r"""
Enable memory efficient attention as implemented in xformers.
When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference
time. Speed up at training time is not guaranteed.
Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention
is used.
"""
self.unet.set_use_memory_efficient_attention_xformers(True)
def disable_xformers_memory_efficient_attention(self):
r"""
Disable memory efficient attention as implemented in xformers.
"""
self.unet.set_use_memory_efficient_attention_xformers(False)
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.
Args:
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
if isinstance(self.unet.config.attention_head_dim, int):
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
else:
# if `attention_head_dim` is a list, take the smallest head size
slice_size = min(self.unet.config.attention_head_dim)
self.unet.set_attention_slice(slice_size)
def disable_attention_slicing(self):
r"""
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
back to computing attention in one step.
"""
# set slice_size = `None` to disable `attention slicing`
self.enable_attention_slicing(None)
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding.
When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
steps. This is useful to save some memory and allow larger batch sizes.
"""
self.vae.enable_slicing()
def disable_vae_slicing(self):
r"""
Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
computing decoding in one step.
"""
self.vae.disable_slicing()
def enable_sequential_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
`torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
device = torch.device(f"cuda:{gpu_id}")
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
if cpu_offloaded_model is not None:
cpu_offload(cpu_offloaded_model, device)
if self.safety_checker is not None:
# TODO(Patrick) - there is currently a bug with cpu offload of nn.Parameter in accelerate
# fix by only offloading self.safety_checker for now
cpu_offload(self.safety_checker.vision_model, device)
@property
def _execution_device(self):
r"""
Returns the device on which the pipeline's models will be executed. After calling
`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
hooks.
"""
if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
return self.device
for module in self.unet.modules():
if (
hasattr(module, "_hf_hook")
and hasattr(module._hf_hook, "execution_device")
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device)
return self.device
def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `list(int)`):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
"""
batch_size = len(prompt) if isinstance(prompt, list) else 1
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids
if not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
text_embeddings = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
text_embeddings = text_embeddings[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
uncond_embeddings = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
uncond_embeddings = uncond_embeddings[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_embeddings.shape[1]
uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
return text_embeddings
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
else:
has_nsfw_concept = None
return image, has_nsfw_concept
def decode_latents(self, latents):
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(self, prompt, height, width, callback_steps):
if not isinstance(prompt, str) and not isinstance(prompt, list):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if latents is None:
if device.type == "mps":
# randn does not work reproducibly on mps
latents = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device)
else:
latents = torch.randn(shape, generator=generator, device=device, dtype=dtype)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
[`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(prompt, height, width, callback_steps)
# 2. Define call parameters
batch_size = 1 if isinstance(prompt, str) else len(prompt)
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
text_embeddings = self._encode_prompt(
prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.unet.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
text_embeddings.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0:
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
# 8. Post-processing
image = self.decode_latents(latents)
# 9. Run safety checker
image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)
# 10. Convert to PIL
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return AltDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| diffusers-ft-main | src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class TransformationModelOutput(ModelOutput):
"""
Base class for text model's outputs that also contains a pooling of the last hidden states.
Args:
text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
The text embeddings obtained by applying the projection layer to the pooler_output.
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
projection_state: Optional[torch.FloatTensor] = None
last_hidden_state: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
class RobertaSeriesConfig(XLMRobertaConfig):
def __init__(
self,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
project_dim=512,
pooler_fn="cls",
learn_encoder=False,
use_attention_mask=True,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.project_dim = project_dim
self.pooler_fn = pooler_fn
self.learn_encoder = learn_encoder
self.use_attention_mask = use_attention_mask
class RobertaSeriesModelWithTransformation(RobertaPreTrainedModel):
_keys_to_ignore_on_load_unexpected = [r"pooler"]
_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
base_model_prefix = "roberta"
config_class = RobertaSeriesConfig
def __init__(self, config):
super().__init__(config)
self.roberta = XLMRobertaModel(config)
self.transformation = nn.Linear(config.hidden_size, config.project_dim)
self.post_init()
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
):
r""" """
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.base_model(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
projection_state = self.transformation(outputs.last_hidden_state)
return TransformationModelOutput(
projection_state=projection_state,
last_hidden_state=outputs.last_hidden_state,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
| diffusers-ft-main | src/diffusers/pipelines/alt_diffusion/modeling_roberta_series.py |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from typing import Optional, Tuple, Union
import torch
from ...models import UNet2DModel, VQModel
from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from ...schedulers import DDIMScheduler
class LDMPipeline(DiffusionPipeline):
r"""
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Parameters:
vqvae ([`VQModel`]):
Vector-quantized (VQ) Model to encode and decode images to and from latent representations.
unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
[`DDIMScheduler`] is to be used in combination with `unet` to denoise the encoded image latents.
"""
def __init__(self, vqvae: VQModel, unet: UNet2DModel, scheduler: DDIMScheduler):
super().__init__()
self.register_modules(vqvae=vqvae, unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
batch_size: int = 1,
generator: Optional[torch.Generator] = None,
eta: float = 0.0,
num_inference_steps: int = 50,
output_type: Optional[str] = "pil",
return_dict: bool = True,
**kwargs,
) -> Union[Tuple, ImagePipelineOutput]:
r"""
Args:
batch_size (`int`, *optional*, defaults to 1):
Number of images to generate.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
Returns:
[`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
generated images.
"""
latents = torch.randn(
(batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
generator=generator,
)
latents = latents.to(self.device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(num_inference_steps)
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_kwargs = {}
if accepts_eta:
extra_kwargs["eta"] = eta
for t in self.progress_bar(self.scheduler.timesteps):
latent_model_input = self.scheduler.scale_model_input(latents, t)
# predict the noise residual
noise_prediction = self.unet(latent_model_input, t).sample
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_prediction, t, latents, **extra_kwargs).prev_sample
# decode the image latents with the VAE
image = self.vqvae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| diffusers-ft-main | src/diffusers/pipelines/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py |
# flake8: noqa
from .pipeline_latent_diffusion_uncond import LDMPipeline
| diffusers-ft-main | src/diffusers/pipelines/latent_diffusion_uncond/__init__.py |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
logger = logging.get_logger(__name__)
def cosine_distance(image_embeds, text_embeds):
normalized_image_embeds = nn.functional.normalize(image_embeds)
normalized_text_embeds = nn.functional.normalize(text_embeds)
return torch.mm(normalized_image_embeds, normalized_text_embeds.t())
class SafeStableDiffusionSafetyChecker(PreTrainedModel):
config_class = CLIPConfig
_no_split_modules = ["CLIPEncoderLayer"]
def __init__(self, config: CLIPConfig):
super().__init__(config)
self.vision_model = CLIPVisionModel(config.vision_config)
self.visual_projection = nn.Linear(config.vision_config.hidden_size, config.projection_dim, bias=False)
self.concept_embeds = nn.Parameter(torch.ones(17, config.projection_dim), requires_grad=False)
self.special_care_embeds = nn.Parameter(torch.ones(3, config.projection_dim), requires_grad=False)
self.concept_embeds_weights = nn.Parameter(torch.ones(17), requires_grad=False)
self.special_care_embeds_weights = nn.Parameter(torch.ones(3), requires_grad=False)
@torch.no_grad()
def forward(self, clip_input, images):
pooled_output = self.vision_model(clip_input)[1] # pooled_output
image_embeds = self.visual_projection(pooled_output)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds).cpu().float().numpy()
cos_dist = cosine_distance(image_embeds, self.concept_embeds).cpu().float().numpy()
result = []
batch_size = image_embeds.shape[0]
for i in range(batch_size):
result_img = {"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []}
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign images
adjustment = 0.0
for concept_idx in range(len(special_cos_dist[0])):
concept_cos = special_cos_dist[i][concept_idx]
concept_threshold = self.special_care_embeds_weights[concept_idx].item()
result_img["special_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)
if result_img["special_scores"][concept_idx] > 0:
result_img["special_care"].append({concept_idx, result_img["special_scores"][concept_idx]})
adjustment = 0.01
for concept_idx in range(len(cos_dist[0])):
concept_cos = cos_dist[i][concept_idx]
concept_threshold = self.concept_embeds_weights[concept_idx].item()
result_img["concept_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(concept_idx)
result.append(result_img)
has_nsfw_concepts = [len(res["bad_concepts"]) > 0 for res in result]
return images, has_nsfw_concepts
@torch.no_grad()
def forward_onnx(self, clip_input: torch.FloatTensor, images: torch.FloatTensor):
pooled_output = self.vision_model(clip_input)[1] # pooled_output
image_embeds = self.visual_projection(pooled_output)
special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds)
cos_dist = cosine_distance(image_embeds, self.concept_embeds)
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
adjustment = 0.0
special_scores = special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
special_care = torch.any(special_scores > 0, dim=1)
special_adjustment = special_care * 0.01
special_adjustment = special_adjustment.unsqueeze(1).expand(-1, cos_dist.shape[1])
concept_scores = (cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
has_nsfw_concepts = torch.any(concept_scores > 0, dim=1)
return images, has_nsfw_concepts
| diffusers-ft-main | src/diffusers/pipelines/stable_diffusion_safe/safety_checker.py |
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class SafetyConfig(object):
WEAK = {
"sld_warmup_steps": 15,
"sld_guidance_scale": 20,
"sld_threshold": 0.0,
"sld_momentum_scale": 0.0,
"sld_mom_beta": 0.0,
}
MEDIUM = {
"sld_warmup_steps": 10,
"sld_guidance_scale": 1000,
"sld_threshold": 0.01,
"sld_momentum_scale": 0.3,
"sld_mom_beta": 0.4,
}
STRONG = {
"sld_warmup_steps": 7,
"sld_guidance_scale": 2000,
"sld_threshold": 0.025,
"sld_momentum_scale": 0.5,
"sld_mom_beta": 0.7,
}
MAX = {
"sld_warmup_steps": 0,
"sld_guidance_scale": 5000,
"sld_threshold": 1.0,
"sld_momentum_scale": 0.5,
"sld_mom_beta": 0.7,
}
@dataclass
class StableDiffusionSafePipelineOutput(BaseOutput):
"""
Output class for Safe Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
nsfw_content_detected (`List[bool]`)
List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, or `None` if safety checking could not be performed.
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images that were flagged by the safety checker any may contain "not-safe-for-work"
(nsfw) content, or `None` if no safety check was performed or no images were flagged.
applied_safety_concept (`str`)
The safety concept that was applied for safety guidance, or `None` if safety guidance was disabled
"""
images: Union[List[PIL.Image.Image], np.ndarray]
nsfw_content_detected: Optional[List[bool]]
unsafe_images: Optional[Union[List[PIL.Image.Image], np.ndarray]]
applied_safety_concept: Optional[str]
if is_transformers_available() and is_torch_available():
from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
from .safety_checker import SafeStableDiffusionSafetyChecker
| diffusers-ft-main | src/diffusers/pipelines/stable_diffusion_safe/__init__.py |
import inspect
import warnings
from typing import Callable, List, Optional, Union
import numpy as np
import torch
from packaging import version
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from ...configuration_utils import FrozenDict
from ...models import AutoencoderKL, UNet2DConditionModel
from ...pipeline_utils import DiffusionPipeline
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import deprecate, is_accelerate_available, logging
from . import StableDiffusionSafePipelineOutput
from .safety_checker import SafeStableDiffusionSafetyChecker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class StableDiffusionPipelineSafe(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using Safe Latent Diffusion.
The implementation is based on the [`StableDiffusionPipeline`]
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
],
safety_checker: SafeStableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
requires_safety_checker: bool = True,
):
super().__init__()
safety_concept: Optional[str] = (
"an image showing hate, harassment, violence, suffering, humiliation, harm, suicide, sexual, nudity,"
" bodily fluids, blood, obscene gestures, illegal activity, drug use, theft, vandalism, weapons, child"
" abuse, brutality, cruelty"
)
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
)
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["clip_sample"] = False
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self._safety_text_concept = safety_concept
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.register_to_config(requires_safety_checker=requires_safety_checker)
@property
def safety_concept(self):
r"""
Getter method for the safety concept used with SLD
Returns:
`str`: The text describing the safety concept
"""
return self._safety_text_concept
@safety_concept.setter
def safety_concept(self, concept):
r"""
Setter method for the safety concept used with SLD
Args:
concept (`str`):
The text of the new safety concept
"""
self._safety_text_concept = concept
def enable_xformers_memory_efficient_attention(self):
r"""
Enable memory efficient attention as implemented in xformers.
When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference
time. Speed up at training time is not guaranteed.
Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention
is used.
"""
self.unet.set_use_memory_efficient_attention_xformers(True)
def disable_xformers_memory_efficient_attention(self):
r"""
Disable memory efficient attention as implemented in xformers.
"""
self.unet.set_use_memory_efficient_attention_xformers(False)
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.
Args:
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(slice_size)
def disable_attention_slicing(self):
r"""
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
back to computing attention in one step.
"""
# set slice_size = `None` to disable `attention slicing`
self.enable_attention_slicing(None)
def enable_sequential_cpu_offload(self):
r"""
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
`torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
device = torch.device("cuda")
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
if cpu_offloaded_model is not None:
cpu_offload(cpu_offloaded_model, device)
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def _execution_device(self):
r"""
Returns the device on which the pipeline's models will be executed. After calling
`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
hooks.
"""
if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
return self.device
for module in self.unet.modules():
if (
hasattr(module, "_hf_hook")
and hasattr(module._hf_hook, "execution_device")
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device)
return self.device
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
enable_safety_guidance,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `list(int)`):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
"""
batch_size = len(prompt) if isinstance(prompt, list) else 1
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids
if not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
text_embeddings = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
text_embeddings = text_embeddings[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
uncond_embeddings = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
uncond_embeddings = uncond_embeddings[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_embeddings.shape[1]
uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
# Encode the safety concept text
if enable_safety_guidance:
safety_concept_input = self.tokenizer(
[self._safety_text_concept],
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
safety_embeddings = self.text_encoder(safety_concept_input.input_ids.to(self.device))[0]
# duplicate safety embeddings for each generation per prompt, using mps friendly method
seq_len = safety_embeddings.shape[1]
safety_embeddings = safety_embeddings.repeat(batch_size, num_images_per_prompt, 1)
safety_embeddings = safety_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance + sld, we need to do three forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing three forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings, safety_embeddings])
else:
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
return text_embeddings
def run_safety_checker(self, image, device, dtype, enable_safety_guidance):
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
flagged_images = None
if any(has_nsfw_concept):
logger.warning(
"Potential NSFW content was detected in one or more images. A black image will be returned"
" instead."
f" {'You may look at this images in the `unsafe_images` variable of the output at your own discretion.' if enable_safety_guidance else 'Try again with a different prompt and/or seed.'} "
)
flagged_images = np.zeros((2, *image.shape[1:]))
for idx, has_nsfw_concept in enumerate(has_nsfw_concept):
if has_nsfw_concept:
flagged_images[idx] = image[idx]
image[idx] = np.zeros(image[idx].shape) # black image
else:
has_nsfw_concept = None
flagged_images = None
return image, has_nsfw_concept, flagged_images
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
def check_inputs(self, prompt, height, width, callback_steps):
if not isinstance(prompt, str) and not isinstance(prompt, list):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if latents is None:
if device.type == "mps":
# randn does not work reproducibly on mps
latents = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device)
else:
latents = torch.randn(shape, generator=generator, device=device, dtype=dtype)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def perform_safety_guidance(
self,
enable_safety_guidance,
safety_momentum,
noise_guidance,
noise_pred_out,
i,
sld_guidance_scale,
sld_warmup_steps,
sld_threshold,
sld_momentum_scale,
sld_mom_beta,
):
# Perform SLD guidance
if enable_safety_guidance:
if safety_momentum is None:
safety_momentum = torch.zeros_like(noise_guidance)
noise_pred_text, noise_pred_uncond = noise_pred_out[0], noise_pred_out[1]
noise_pred_safety_concept = noise_pred_out[2]
# Equation 6
scale = torch.clamp(torch.abs((noise_pred_text - noise_pred_safety_concept)) * sld_guidance_scale, max=1.0)
# Equation 6
safety_concept_scale = torch.where(
(noise_pred_text - noise_pred_safety_concept) >= sld_threshold, torch.zeros_like(scale), scale
)
# Equation 4
noise_guidance_safety = torch.mul((noise_pred_safety_concept - noise_pred_uncond), safety_concept_scale)
# Equation 7
noise_guidance_safety = noise_guidance_safety + sld_momentum_scale * safety_momentum
# Equation 8
safety_momentum = sld_mom_beta * safety_momentum + (1 - sld_mom_beta) * noise_guidance_safety
if i >= sld_warmup_steps: # Warmup
# Equation 3
noise_guidance = noise_guidance - noise_guidance_safety
return noise_guidance, safety_momentum
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
sld_guidance_scale: Optional[float] = 1000,
sld_warmup_steps: Optional[int] = 10,
sld_threshold: Optional[float] = 0.01,
sld_momentum_scale: Optional[float] = 0.3,
sld_mom_beta: Optional[float] = 0.4,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
sld_guidance_scale (`float`, *optional*, defaults to 1000):
Safe latent guidance as defined in [Safe Latent Diffusion](https://arxiv.org/abs/2211.05105).
`sld_guidance_scale` is defined as sS of Eq. 6. If set to be less than 1, safety guidance will be
disabled.
sld_warmup_steps (`int`, *optional*, defaults to 10):
Number of warmup steps for safety guidance. SLD will only be applied for diffusion steps greater than
`sld_warmup_steps`. `sld_warmup_steps` is defined as `delta` of [Safe Latent
Diffusion](https://arxiv.org/abs/2211.05105).
sld_threshold (`float`, *optional*, defaults to 0.01):
Threshold that separates the hyperplane between appropriate and inappropriate images. `sld_threshold`
is defined as `lamda` of Eq. 5 in [Safe Latent Diffusion](https://arxiv.org/abs/2211.05105).
sld_momentum_scale (`float`, *optional*, defaults to 0.3):
Scale of the SLD momentum to be added to the safety guidance at each diffusion step. If set to 0.0
momentum will be disabled. Momentum is already built up during warmup, i.e. for diffusion steps smaller
than `sld_warmup_steps`. `sld_momentum_scale` is defined as `sm` of Eq. 7 in [Safe Latent
Diffusion](https://arxiv.org/abs/2211.05105).
sld_mom_beta (`float`, *optional*, defaults to 0.4):
Defines how safety guidance momentum builds up. `sld_mom_beta` indicates how much of the previous
momentum will be kept. Momentum is already built up during warmup, i.e. for diffusion steps smaller
than `sld_warmup_steps`. `sld_mom_beta` is defined as `beta m` of Eq. 8 in [Safe Latent
Diffusion](https://arxiv.org/abs/2211.05105).
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(prompt, height, width, callback_steps)
# 2. Define call parameters
batch_size = 1 if isinstance(prompt, str) else len(prompt)
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
enable_safety_guidance = sld_guidance_scale > 1.0 and do_classifier_free_guidance
if not enable_safety_guidance:
warnings.warn("Safety checker disabled!")
# 3. Encode input prompt
text_embeddings = self._encode_prompt(
prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, enable_safety_guidance
)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.unet.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
text_embeddings.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs.
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
safety_momentum = None
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = (
torch.cat([latents] * (3 if enable_safety_guidance else 2))
if do_classifier_free_guidance
else latents
)
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_out = noise_pred.chunk((3 if enable_safety_guidance else 2))
noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1]
# default classifier free guidance
noise_guidance = noise_pred_text - noise_pred_uncond
# Perform SLD guidance
if enable_safety_guidance:
if safety_momentum is None:
safety_momentum = torch.zeros_like(noise_guidance)
noise_pred_safety_concept = noise_pred_out[2]
# Equation 6
scale = torch.clamp(
torch.abs((noise_pred_text - noise_pred_safety_concept)) * sld_guidance_scale, max=1.0
)
# Equation 6
safety_concept_scale = torch.where(
(noise_pred_text - noise_pred_safety_concept) >= sld_threshold,
torch.zeros_like(scale),
scale,
)
# Equation 4
noise_guidance_safety = torch.mul(
(noise_pred_safety_concept - noise_pred_uncond), safety_concept_scale
)
# Equation 7
noise_guidance_safety = noise_guidance_safety + sld_momentum_scale * safety_momentum
# Equation 8
safety_momentum = sld_mom_beta * safety_momentum + (1 - sld_mom_beta) * noise_guidance_safety
if i >= sld_warmup_steps: # Warmup
# Equation 3
noise_guidance = noise_guidance - noise_guidance_safety
noise_pred = noise_pred_uncond + guidance_scale * noise_guidance
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0:
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
# 8. Post-processing
image = self.decode_latents(latents)
# 9. Run safety checker
image, has_nsfw_concept, flagged_images = self.run_safety_checker(
image, device, text_embeddings.dtype, enable_safety_guidance
)
# 10. Convert to PIL
if output_type == "pil":
image = self.numpy_to_pil(image)
if flagged_images is not None:
flagged_images = self.numpy_to_pil(flagged_images)
if not return_dict:
return (
image,
has_nsfw_concept,
self._safety_text_concept if enable_safety_guidance else None,
flagged_images,
)
return StableDiffusionSafePipelineOutput(
images=image,
nsfw_content_detected=has_nsfw_concept,
applied_safety_concept=self._safety_text_concept if enable_safety_guidance else None,
unsafe_images=flagged_images,
)
| diffusers-ft-main | src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| diffusers-ft-main | src/diffusers/pipelines/vq_diffusion/__init__.py |
# Copyright 2022 Microsoft and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Callable, List, Optional, Tuple, Union
import torch
from diffusers import Transformer2DModel, VQModel
from diffusers.schedulers.scheduling_vq_diffusion import VQDiffusionScheduler
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...modeling_utils import ModelMixin
from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from ...utils import logging
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class LearnedClassifierFreeSamplingEmbeddings(ModelMixin, ConfigMixin):
"""
Utility class for storing learned text embeddings for classifier free sampling
"""
@register_to_config
def __init__(self, learnable: bool, hidden_size: Optional[int] = None, length: Optional[int] = None):
super().__init__()
self.learnable = learnable
if self.learnable:
assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
assert length is not None, "learnable=True requires `length` to be set"
embeddings = torch.zeros(length, hidden_size)
else:
embeddings = None
self.embeddings = torch.nn.Parameter(embeddings)
class VQDiffusionPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using VQ Diffusion
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vqvae ([`VQModel`]):
Vector Quantized Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent
representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. VQ Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
transformer ([`Transformer2DModel`]):
Conditional transformer to denoise the encoded image latents.
scheduler ([`VQDiffusionScheduler`]):
A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
"""
vqvae: VQModel
text_encoder: CLIPTextModel
tokenizer: CLIPTokenizer
transformer: Transformer2DModel
learned_classifier_free_sampling_embeddings: LearnedClassifierFreeSamplingEmbeddings
scheduler: VQDiffusionScheduler
def __init__(
self,
vqvae: VQModel,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
transformer: Transformer2DModel,
scheduler: VQDiffusionScheduler,
learned_classifier_free_sampling_embeddings: LearnedClassifierFreeSamplingEmbeddings,
):
super().__init__()
self.register_modules(
vqvae=vqvae,
transformer=transformer,
text_encoder=text_encoder,
tokenizer=tokenizer,
scheduler=scheduler,
learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings,
)
def _encode_prompt(self, prompt, num_images_per_prompt, do_classifier_free_guidance):
batch_size = len(prompt) if isinstance(prompt, list) else 1
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
# While CLIP does normalize the pooled output of the text transformer when combining
# the image and text embeddings, CLIP does not directly normalize the last hidden state.
#
# CLIP normalizing the pooled output.
# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
text_embeddings = text_embeddings / text_embeddings.norm(dim=-1, keepdim=True)
# duplicate text embeddings for each generation per prompt
text_embeddings = text_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
if do_classifier_free_guidance:
if self.learned_classifier_free_sampling_embeddings.learnable:
uncond_embeddings = self.learned_classifier_free_sampling_embeddings.embeddings
uncond_embeddings = uncond_embeddings.unsqueeze(0).repeat(batch_size, 1, 1)
else:
uncond_tokens = [""] * batch_size
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# See comment for normalizing text embeddings
uncond_embeddings = uncond_embeddings / uncond_embeddings.norm(dim=-1, keepdim=True)
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_embeddings.shape[1]
uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
return text_embeddings
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
num_inference_steps: int = 100,
guidance_scale: float = 5.0,
truncation_rate: float = 1.0,
num_images_per_prompt: int = 1,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
) -> Union[ImagePipelineOutput, Tuple]:
"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
num_inference_steps (`int`, *optional*, defaults to 100):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
truncation_rate (`float`, *optional*, defaults to 1.0 (equivalent to no truncation)):
Used to "truncate" the predicted classes for x_0 such that the cumulative probability for a pixel is at
most `truncation_rate`. The lowest probabilities that would increase the cumulative probability above
`truncation_rate` are set to zero.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
latents (`torch.FloatTensor` of shape (batch), *optional*):
Pre-generated noisy latents to be used as inputs for image generation. Must be valid embedding indices.
Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will
be generated of completely masked latent pixels.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
[`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~ pipeline_utils.ImagePipelineOutput `] if
`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
generated images.
"""
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
batch_size = batch_size * num_images_per_prompt
do_classifier_free_guidance = guidance_scale > 1.0
text_embeddings = self._encode_prompt(prompt, num_images_per_prompt, do_classifier_free_guidance)
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
# get the initial completely masked latents unless the user supplied it
latents_shape = (batch_size, self.transformer.num_latent_pixels)
if latents is None:
mask_class = self.transformer.num_vector_embeds - 1
latents = torch.full(latents_shape, mask_class).to(self.device)
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
raise ValueError(
"Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"
f" {self.transformer.num_vector_embeds - 1} (inclusive)."
)
latents = latents.to(self.device)
# set timesteps
self.scheduler.set_timesteps(num_inference_steps, device=self.device)
timesteps_tensor = self.scheduler.timesteps.to(self.device)
sample = latents
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
# expand the sample if we are doing classifier free guidance
latent_model_input = torch.cat([sample] * 2) if do_classifier_free_guidance else sample
# predict the un-noised image
# model_output == `log_p_x_0`
model_output = self.transformer(
latent_model_input, encoder_hidden_states=text_embeddings, timestep=t
).sample
if do_classifier_free_guidance:
model_output_uncond, model_output_text = model_output.chunk(2)
model_output = model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
model_output -= torch.logsumexp(model_output, dim=1, keepdim=True)
model_output = self.truncate(model_output, truncation_rate)
# remove `log(0)`'s (`-inf`s)
model_output = model_output.clamp(-70)
# compute the previous noisy sample x_t -> x_t-1
sample = self.scheduler.step(model_output, timestep=t, sample=sample, generator=generator).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(i, t, sample)
embedding_channels = self.vqvae.config.vq_embed_dim
embeddings_shape = (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
embeddings = self.vqvae.quantize.get_codebook_entry(sample, shape=embeddings_shape)
image = self.vqvae.decode(embeddings, force_not_quantize=True).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
def truncate(self, log_p_x_0: torch.FloatTensor, truncation_rate: float) -> torch.FloatTensor:
"""
Truncates log_p_x_0 such that for each column vector, the total cumulative probability is `truncation_rate` The
lowest probabilities that would increase the cumulative probability above `truncation_rate` are set to zero.
"""
sorted_log_p_x_0, indices = torch.sort(log_p_x_0, 1, descending=True)
sorted_p_x_0 = torch.exp(sorted_log_p_x_0)
keep_mask = sorted_p_x_0.cumsum(dim=1) < truncation_rate
# Ensure that at least the largest probability is not zeroed out
all_true = torch.full_like(keep_mask[:, 0:1, :], True)
keep_mask = torch.cat((all_true, keep_mask), dim=1)
keep_mask = keep_mask[:, :-1, :]
keep_mask = keep_mask.gather(1, indices.argsort(1))
rv = log_p_x_0.clone()
rv[~keep_mask] = -torch.inf # -inf = log(0)
return rv
| diffusers-ft-main | src/diffusers/pipelines/vq_diffusion/pipeline_vq_diffusion.py |
import inspect
from typing import Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.checkpoint
import PIL
from ...models import UNet2DModel, VQModel
from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION
def preprocess(image):
w, h = image.size
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.0 * image - 1.0
class LDMSuperResolutionPipeline(DiffusionPipeline):
r"""
A pipeline for image super-resolution using Latent
This class inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Parameters:
vqvae ([`VQModel`]):
Vector-quantized (VQ) VAE Model to encode and decode images to and from latent representations.
unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], [`EulerDiscreteScheduler`],
[`EulerAncestralDiscreteScheduler`], [`DPMSolverMultistepScheduler`], or [`PNDMScheduler`].
"""
def __init__(
self,
vqvae: VQModel,
unet: UNet2DModel,
scheduler: Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
],
):
super().__init__()
self.register_modules(vqvae=vqvae, unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
init_image: Union[torch.Tensor, PIL.Image.Image],
batch_size: Optional[int] = 1,
num_inference_steps: Optional[int] = 100,
eta: Optional[float] = 0.0,
generator: Optional[torch.Generator] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
**kwargs,
) -> Union[Tuple, ImagePipelineOutput]:
r"""
Args:
init_image (`torch.Tensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
batch_size (`int`, *optional*, defaults to 1):
Number of images to generate.
num_inference_steps (`int`, *optional*, defaults to 100):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*):
Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
Returns:
[`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
generated images.
"""
if isinstance(init_image, PIL.Image.Image):
batch_size = 1
elif isinstance(init_image, torch.Tensor):
batch_size = init_image.shape[0]
else:
raise ValueError(
f"`init_image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(init_image)}"
)
if isinstance(init_image, PIL.Image.Image):
init_image = preprocess(init_image)
height, width = init_image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
latents_shape = (batch_size, self.unet.in_channels // 2, height, width)
latents_dtype = next(self.unet.parameters()).dtype
if self.device.type == "mps":
# randn does not work reproducibly on mps
latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype)
latents = latents.to(self.device)
else:
latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
init_image = init_image.to(device=self.device, dtype=latents_dtype)
# set timesteps and move to the correct device
self.scheduler.set_timesteps(num_inference_steps, device=self.device)
timesteps_tensor = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_kwargs = {}
if accepts_eta:
extra_kwargs["eta"] = eta
for t in self.progress_bar(timesteps_tensor):
# concat latents and low resolution image in the channel dimension.
latents_input = torch.cat([latents, init_image], dim=1)
latents_input = self.scheduler.scale_model_input(latents_input, t)
# predict the noise residual
noise_pred = self.unet(latents_input, t).sample
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_kwargs).prev_sample
# decode the image latents with the VQVAE
image = self.vqvae.decode(latents).sample
image = torch.clamp(image, -1.0, 1.0)
image = image / 2 + 0.5
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| diffusers-ft-main | src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py |
# flake8: noqa
from ...utils import is_transformers_available
from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline
if is_transformers_available():
from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline
| diffusers-ft-main | src/diffusers/pipelines/latent_diffusion/__init__.py |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.utils.checkpoint
from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_outputs import BaseModelOutput
from transformers.modeling_utils import PreTrainedModel
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.utils import logging
from ...models import AutoencoderKL, UNet2DConditionModel, UNet2DModel, VQModel
from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
class LDMTextToImagePipeline(DiffusionPipeline):
r"""
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Parameters:
vqvae ([`VQModel`]):
Vector-quantized (VQ) Model to encode and decode images to and from latent representations.
bert ([`LDMBertModel`]):
Text-encoder model based on [BERT](https://huggingface.co/docs/transformers/model_doc/bert) architecture.
tokenizer (`transformers.BertTokenizer`):
Tokenizer of class
[BertTokenizer](https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
"""
def __init__(
self,
vqvae: Union[VQModel, AutoencoderKL],
bert: PreTrainedModel,
tokenizer: PreTrainedTokenizer,
unet: Union[UNet2DModel, UNet2DConditionModel],
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
):
super().__init__()
self.register_modules(vqvae=vqvae, bert=bert, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
self.vae_scale_factor = 2 ** (len(self.vqvae.config.block_out_channels) - 1)
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 1.0,
eta: Optional[float] = 0.0,
generator: Optional[torch.Generator] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
**kwargs,
) -> Union[Tuple, ImagePipelineOutput]:
r"""
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 1.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt` at
the, usually at the expense of lower image quality.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*):
Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
Returns:
[`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
generated images.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
# get unconditional embeddings for classifier free guidance
if guidance_scale != 1.0:
uncond_input = self.tokenizer([""] * batch_size, padding="max_length", max_length=77, return_tensors="pt")
uncond_embeddings = self.bert(uncond_input.input_ids.to(self.device))[0]
# get prompt text embeddings
text_input = self.tokenizer(prompt, padding="max_length", max_length=77, return_tensors="pt")
text_embeddings = self.bert(text_input.input_ids.to(self.device))[0]
latents = torch.randn(
(batch_size, self.unet.in_channels, height // 8, width // 8),
generator=generator,
)
latents = latents.to(self.device)
self.scheduler.set_timesteps(num_inference_steps)
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_kwargs = {}
if accepts_eta:
extra_kwargs["eta"] = eta
for t in self.progress_bar(self.scheduler.timesteps):
if guidance_scale == 1.0:
# guidance_scale of 1 means no guidance
latents_input = latents
context = text_embeddings
else:
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
latents_input = torch.cat([latents] * 2)
context = torch.cat([uncond_embeddings, text_embeddings])
# predict the noise residual
noise_pred = self.unet(latents_input, t, encoder_hidden_states=context).sample
# perform guidance
if guidance_scale != 1.0:
noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_kwargs).prev_sample
# scale and decode the image latents with vae
latents = 1 / 0.18215 * latents
image = self.vqvae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
################################################################################
# Code for the text transformer model
################################################################################
""" PyTorch LDMBERT model."""
logger = logging.get_logger(__name__)
LDMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"ldm-bert",
# See all LDMBert models at https://huggingface.co/models?filter=ldmbert
]
LDMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"ldm-bert": "https://huggingface.co/valhalla/ldm-bert/blob/main/config.json",
}
""" LDMBERT model configuration"""
class LDMBertConfig(PretrainedConfig):
model_type = "ldmbert"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__(
self,
vocab_size=30522,
max_position_embeddings=77,
encoder_layers=32,
encoder_ffn_dim=5120,
encoder_attention_heads=8,
head_dim=64,
encoder_layerdrop=0.0,
activation_function="gelu",
d_model=1280,
dropout=0.1,
attention_dropout=0.0,
activation_dropout=0.0,
init_std=0.02,
classifier_dropout=0.0,
scale_embedding=False,
use_cache=True,
pad_token_id=0,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.d_model = d_model
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.head_dim = head_dim
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.activation_function = activation_function
self.init_std = init_std
self.encoder_layerdrop = encoder_layerdrop
self.classifier_dropout = classifier_dropout
self.use_cache = use_cache
self.num_hidden_layers = encoder_layers
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(pad_token_id=pad_token_id, **kwargs)
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->LDMBert
class LDMBertAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
head_dim: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = False,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = head_dim
self.inner_dim = head_dim * num_heads
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.k_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias)
self.out_proj = nn.Linear(self.inner_dim, embed_dim)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned across GPUs when using tensor-parallelism.
attn_output = attn_output.reshape(bsz, tgt_len, self.inner_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped, past_key_value
class LDMBertEncoderLayer(nn.Module):
def __init__(self, config: LDMBertConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = LDMBertAttention(
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_heads,
head_dim=config.head_dim,
dropout=config.attention_dropout,
)
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.FloatTensor,
attention_mask: torch.FloatTensor,
layer_head_mask: torch.FloatTensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
hidden_states, attn_weights, _ = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
if hidden_states.dtype == torch.float16 and (
torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
):
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
# Copied from transformers.models.bart.modeling_bart.BartPretrainedModel with Bart->LDMBert
class LDMBertPreTrainedModel(PreTrainedModel):
config_class = LDMBertConfig
base_model_prefix = "model"
_supports_gradient_checkpointing = True
_keys_to_ignore_on_load_unexpected = [r"encoder\.version", r"decoder\.version"]
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (LDMBertEncoder,)):
module.gradient_checkpointing = value
@property
def dummy_inputs(self):
pad_token = self.config.pad_token_id
input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
dummy_inputs = {
"attention_mask": input_ids.ne(pad_token),
"input_ids": input_ids,
}
return dummy_inputs
class LDMBertEncoder(LDMBertPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`LDMBertEncoderLayer`].
Args:
config: LDMBertConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: LDMBertConfig):
super().__init__(config)
self.dropout = config.dropout
embed_dim = config.d_model
self.padding_idx = config.pad_token_id
self.max_source_positions = config.max_position_embeddings
self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim)
self.embed_positions = nn.Embedding(config.max_position_embeddings, embed_dim)
self.layers = nn.ModuleList([LDMBertEncoderLayer(config) for _ in range(config.encoder_layers)])
self.layer_norm = nn.LayerNorm(embed_dim)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`BartTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.BaseModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
seq_len = input_shape[1]
if position_ids is None:
position_ids = torch.arange(seq_len, dtype=torch.long, device=inputs_embeds.device).expand((1, -1))
embed_pos = self.embed_positions(position_ids)
hidden_states = inputs_embeds + embed_pos
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
if head_mask.size()[0] != (len(self.layers)):
raise ValueError(
f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(encoder_layer),
hidden_states,
attention_mask,
(head_mask[idx] if head_mask is not None else None),
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
hidden_states = self.layer_norm(hidden_states)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
class LDMBertModel(LDMBertPreTrainedModel):
_no_split_modules = []
def __init__(self, config: LDMBertConfig):
super().__init__(config)
self.model = LDMBertEncoder(config)
self.to_logits = nn.Linear(config.hidden_size, config.vocab_size)
def forward(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
outputs = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
return outputs
| diffusers-ft-main | src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py |
# flake8: noqa
from .pipeline_dance_diffusion import DanceDiffusionPipeline
| diffusers-ft-main | src/diffusers/pipelines/dance_diffusion/__init__.py |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from ...pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from ...utils import logging
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class DanceDiffusionPipeline(DiffusionPipeline):
r"""
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Parameters:
unet ([`UNet1DModel`]): U-Net architecture to denoise the encoded image.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
[`IPNDMScheduler`].
"""
def __init__(self, unet, scheduler):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
batch_size: int = 1,
num_inference_steps: int = 100,
generator: Optional[torch.Generator] = None,
audio_length_in_s: Optional[float] = None,
return_dict: bool = True,
) -> Union[AudioPipelineOutput, Tuple]:
r"""
Args:
batch_size (`int`, *optional*, defaults to 1):
The number of audio samples to generate.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality audio sample at
the expense of slower inference.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
audio_length_in_s (`float`, *optional*, defaults to `self.unet.config.sample_size/self.unet.config.sample_rate`):
The length of the generated audio sample in seconds. Note that the output of the pipeline, *i.e.*
`sample_size`, will be `audio_length_in_s` * `self.unet.sample_rate`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipeline_utils.AudioPipelineOutput`] instead of a plain tuple.
Returns:
[`~pipeline_utils.AudioPipelineOutput`] or `tuple`: [`~pipelines.utils.AudioPipelineOutput`] if
`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
generated images.
"""
if audio_length_in_s is None:
audio_length_in_s = self.unet.config.sample_size / self.unet.config.sample_rate
sample_size = audio_length_in_s * self.unet.sample_rate
down_scale_factor = 2 ** len(self.unet.up_blocks)
if sample_size < 3 * down_scale_factor:
raise ValueError(
f"{audio_length_in_s} is too small. Make sure it's bigger or equal to"
f" {3 * down_scale_factor / self.unet.sample_rate}."
)
original_sample_size = int(sample_size)
if sample_size % down_scale_factor != 0:
sample_size = ((audio_length_in_s * self.unet.sample_rate) // down_scale_factor + 1) * down_scale_factor
logger.info(
f"{audio_length_in_s} is increased to {sample_size / self.unet.sample_rate} so that it can be handled"
f" by the model. It will be cut to {original_sample_size / self.unet.sample_rate} after the denoising"
" process."
)
sample_size = int(sample_size)
dtype = next(iter(self.unet.parameters())).dtype
audio = torch.randn(
(batch_size, self.unet.in_channels, sample_size), generator=generator, device=self.device, dtype=dtype
)
# set step values
self.scheduler.set_timesteps(num_inference_steps, device=audio.device)
self.scheduler.timesteps = self.scheduler.timesteps.to(dtype)
for t in self.progress_bar(self.scheduler.timesteps):
# 1. predict noise model_output
model_output = self.unet(audio, t).sample
# 2. compute previous image: x_t -> t_t-1
audio = self.scheduler.step(model_output, t, audio).prev_sample
audio = audio.clamp(-1, 1).float().cpu().numpy()
audio = audio[:, :, :original_sample_size]
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=audio)
| diffusers-ft-main | src/diffusers/pipelines/dance_diffusion/pipeline_dance_diffusion.py |
# flake8: noqa
from .pipeline_score_sde_ve import ScoreSdeVePipeline
| diffusers-ft-main | src/diffusers/pipelines/score_sde_ve/__init__.py |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from ...models import UNet2DModel
from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from ...schedulers import ScoreSdeVeScheduler
class ScoreSdeVePipeline(DiffusionPipeline):
r"""
Parameters:
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image. scheduler ([`SchedulerMixin`]):
The [`ScoreSdeVeScheduler`] scheduler to be used in combination with `unet` to denoise the encoded image.
"""
unet: UNet2DModel
scheduler: ScoreSdeVeScheduler
def __init__(self, unet: UNet2DModel, scheduler: DiffusionPipeline):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
batch_size: int = 1,
num_inference_steps: int = 2000,
generator: Optional[torch.Generator] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
**kwargs,
) -> Union[ImagePipelineOutput, Tuple]:
r"""
Args:
batch_size (`int`, *optional*, defaults to 1):
The number of images to generate.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
Returns:
[`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
generated images.
"""
img_size = self.unet.config.sample_size
shape = (batch_size, 3, img_size, img_size)
model = self.unet
sample = torch.randn(*shape, generator=generator) * self.scheduler.init_noise_sigma
sample = sample.to(self.device)
self.scheduler.set_timesteps(num_inference_steps)
self.scheduler.set_sigmas(num_inference_steps)
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
sigma_t = self.scheduler.sigmas[i] * torch.ones(shape[0], device=self.device)
# correction step
for _ in range(self.scheduler.config.correct_steps):
model_output = self.unet(sample, sigma_t).sample
sample = self.scheduler.step_correct(model_output, sample, generator=generator).prev_sample
# prediction step
model_output = model(sample, sigma_t).sample
output = self.scheduler.step_pred(model_output, t, sample, generator=generator)
sample, sample_mean = output.prev_sample, output.prev_sample_mean
sample = sample_mean.clamp(0, 1)
sample = sample.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
sample = self.numpy_to_pil(sample)
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=sample)
| diffusers-ft-main | src/diffusers/pipelines/score_sde_ve/pipeline_score_sde_ve.py |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from typing import Callable, List, Optional, Union
import torch
import PIL
from diffusers.utils import is_accelerate_available
from packaging import version
from transformers import CLIPFeatureExtractor, CLIPVisionModelWithProjection
from ...configuration_utils import FrozenDict
from ...models import AutoencoderKL, UNet2DConditionModel
from ...pipeline_utils import DiffusionPipeline
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import deprecate, logging
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class StableDiffusionImageVariationPipeline(DiffusionPipeline):
r"""
Pipeline to generate variations from an input image using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
image_encoder ([`CLIPVisionModelWithProjection`]):
Frozen CLIP image-encoder. Stable Diffusion Image Variation uses the vision portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPVisionModelWithProjection),
specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
image_encoder: CLIPVisionModelWithProjection,
unet: UNet2DConditionModel,
scheduler: Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
requires_safety_checker: bool = True,
):
super().__init__()
if safety_checker is None and requires_safety_checker:
logger.warn(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
image_encoder=image_encoder,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_xformers_memory_efficient_attention
def enable_xformers_memory_efficient_attention(self):
r"""
Enable memory efficient attention as implemented in xformers.
When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference
time. Speed up at training time is not guaranteed.
Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention
is used.
"""
self.unet.set_use_memory_efficient_attention_xformers(True)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_xformers_memory_efficient_attention
def disable_xformers_memory_efficient_attention(self):
r"""
Disable memory efficient attention as implemented in xformers.
"""
self.unet.set_use_memory_efficient_attention_xformers(False)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_attention_slicing
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.
Args:
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
if isinstance(self.unet.config.attention_head_dim, int):
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
else:
# if `attention_head_dim` is a list, take the smallest head size
slice_size = min(self.unet.config.attention_head_dim)
self.unet.set_attention_slice(slice_size)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_attention_slicing
def disable_attention_slicing(self):
r"""
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
back to computing attention in one step.
"""
# set slice_size = `None` to disable `attention slicing`
self.enable_attention_slicing(None)
def enable_sequential_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
`torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
device = torch.device(f"cuda:{gpu_id}")
for cpu_offloaded_model in [self.unet, self.image_encoder, self.vae, self.safety_checker]:
if cpu_offloaded_model is not None:
cpu_offload(cpu_offloaded_model, device)
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def _execution_device(self):
r"""
Returns the device on which the pipeline's models will be executed. After calling
`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
hooks.
"""
if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
return self.device
for module in self.unet.modules():
if (
hasattr(module, "_hf_hook")
and hasattr(module._hf_hook, "execution_device")
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device)
return self.device
def _encode_image(self, image, device, num_images_per_prompt, do_classifier_free_guidance):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(images=image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embeddings = self.image_encoder(image).image_embeds
image_embeddings = image_embeddings.unsqueeze(1)
# duplicate image embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = image_embeddings.shape
image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)
image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
if do_classifier_free_guidance:
uncond_embeddings = torch.zeros_like(image_embeddings)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
image_embeddings = torch.cat([uncond_embeddings, image_embeddings])
return image_embeddings
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
else:
has_nsfw_concept = None
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(self, image, height, width, callback_steps):
if (
not isinstance(image, torch.Tensor)
and not isinstance(image, PIL.Image.Image)
and not isinstance(image, list)
):
raise ValueError(
f"`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `list` but is {type(image)}"
)
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if latents is None:
if device.type == "mps":
# randn does not work reproducibly on mps
latents = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device)
else:
latents = torch.randn(shape, generator=generator, device=device, dtype=dtype)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@torch.no_grad()
def __call__(
self,
image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`):
The image or images to guide the image generation. If you provide a tensor, it needs to comply with the
configuration of
[this](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json)
`CLIPFeatureExtractor`
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(image, height, width, callback_steps)
# 2. Define call parameters
if isinstance(image, PIL.Image.Image):
batch_size = 1
elif isinstance(image, list):
batch_size = len(image)
else:
batch_size = image.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input image
image_embeddings = self._encode_image(image, device, num_images_per_prompt, do_classifier_free_guidance)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.unet.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
image_embeddings.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=image_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0:
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
# 8. Post-processing
image = self.decode_latents(latents)
# 9. Run safety checker
image, has_nsfw_concept = self.run_safety_checker(image, device, image_embeddings.dtype)
# 10. Convert to PIL
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| diffusers-ft-main | src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from functools import partial
from typing import Dict, List, Optional, Union
import numpy as np
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from flax.jax_utils import unreplicate
from flax.training.common_utils import shard
from packaging import version
from PIL import Image
from transformers import CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel
from ...models import FlaxAutoencoderKL, FlaxUNet2DConditionModel
from ...pipeline_flax_utils import FlaxDiffusionPipeline
from ...schedulers import (
FlaxDDIMScheduler,
FlaxDPMSolverMultistepScheduler,
FlaxLMSDiscreteScheduler,
FlaxPNDMScheduler,
)
from ...utils import deprecate, logging
from . import FlaxStableDiffusionPipelineOutput
from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class FlaxStableDiffusionPipeline(FlaxDiffusionPipeline):
r"""
Pipeline for text-to-image generation using Stable Diffusion.
This model inherits from [`FlaxDiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`FlaxAutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`FlaxCLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.FlaxCLIPTextModel),
specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`FlaxUNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`FlaxDDIMScheduler`], [`FlaxLMSDiscreteScheduler`], [`FlaxPNDMScheduler`], or
[`FlaxDPMSolverMultistepScheduler`].
safety_checker ([`FlaxStableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
def __init__(
self,
vae: FlaxAutoencoderKL,
text_encoder: FlaxCLIPTextModel,
tokenizer: CLIPTokenizer,
unet: FlaxUNet2DConditionModel,
scheduler: Union[
FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler
],
safety_checker: FlaxStableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
dtype: jnp.dtype = jnp.float32,
):
super().__init__()
self.dtype = dtype
if safety_checker is None:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
def prepare_inputs(self, prompt: Union[str, List[str]]):
if not isinstance(prompt, (str, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="np",
)
return text_input.input_ids
def _get_has_nsfw_concepts(self, features, params):
has_nsfw_concepts = self.safety_checker(features, params)
return has_nsfw_concepts
def _run_safety_checker(self, images, safety_model_params, jit=False):
# safety_model_params should already be replicated when jit is True
pil_images = [Image.fromarray(image) for image in images]
features = self.feature_extractor(pil_images, return_tensors="np").pixel_values
if jit:
features = shard(features)
has_nsfw_concepts = _p_get_has_nsfw_concepts(self, features, safety_model_params)
has_nsfw_concepts = unshard(has_nsfw_concepts)
safety_model_params = unreplicate(safety_model_params)
else:
has_nsfw_concepts = self._get_has_nsfw_concepts(features, safety_model_params)
images_was_copied = False
for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):
if has_nsfw_concept:
if not images_was_copied:
images_was_copied = True
images = images.copy()
images[idx] = np.zeros(images[idx].shape, dtype=np.uint8) # black image
if any(has_nsfw_concepts):
warnings.warn(
"Potential NSFW content was detected in one or more images. A black image will be returned"
" instead. Try again with a different prompt and/or seed."
)
return images, has_nsfw_concepts
def _generate(
self,
prompt_ids: jnp.array,
params: Union[Dict, FrozenDict],
prng_seed: jax.random.PRNGKey,
num_inference_steps: int = 50,
height: Optional[int] = None,
width: Optional[int] = None,
guidance_scale: float = 7.5,
latents: Optional[jnp.array] = None,
debug: bool = False,
neg_prompt_ids: jnp.array = None,
):
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
# get prompt text embeddings
text_embeddings = self.text_encoder(prompt_ids, params=params["text_encoder"])[0]
# TODO: currently it is assumed `do_classifier_free_guidance = guidance_scale > 1.0`
# implement this conditional `do_classifier_free_guidance = guidance_scale > 1.0`
batch_size = prompt_ids.shape[0]
max_length = prompt_ids.shape[-1]
if neg_prompt_ids is None:
uncond_input = self.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="np"
).input_ids
else:
uncond_input = neg_prompt_ids
uncond_embeddings = self.text_encoder(uncond_input, params=params["text_encoder"])[0]
context = jnp.concatenate([uncond_embeddings, text_embeddings])
latents_shape = (
batch_size,
self.unet.in_channels,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
if latents is None:
latents = jax.random.normal(prng_seed, shape=latents_shape, dtype=jnp.float32)
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
def loop_body(step, args):
latents, scheduler_state = args
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
latents_input = jnp.concatenate([latents] * 2)
t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step]
timestep = jnp.broadcast_to(t, latents_input.shape[0])
latents_input = self.scheduler.scale_model_input(scheduler_state, latents_input, t)
# predict the noise residual
noise_pred = self.unet.apply(
{"params": params["unet"]},
jnp.array(latents_input),
jnp.array(timestep, dtype=jnp.int32),
encoder_hidden_states=context,
).sample
# perform guidance
noise_pred_uncond, noise_prediction_text = jnp.split(noise_pred, 2, axis=0)
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents, scheduler_state = self.scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple()
return latents, scheduler_state
scheduler_state = self.scheduler.set_timesteps(
params["scheduler"], num_inference_steps=num_inference_steps, shape=latents.shape
)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
if debug:
# run with python for loop
for i in range(num_inference_steps):
latents, scheduler_state = loop_body(i, (latents, scheduler_state))
else:
latents, _ = jax.lax.fori_loop(0, num_inference_steps, loop_body, (latents, scheduler_state))
# scale and decode the image latents with vae
latents = 1 / 0.18215 * latents
image = self.vae.apply({"params": params["vae"]}, latents, method=self.vae.decode).sample
image = (image / 2 + 0.5).clip(0, 1).transpose(0, 2, 3, 1)
return image
def __call__(
self,
prompt_ids: jnp.array,
params: Union[Dict, FrozenDict],
prng_seed: jax.random.PRNGKey,
num_inference_steps: int = 50,
height: Optional[int] = None,
width: Optional[int] = None,
guidance_scale: float = 7.5,
latents: jnp.array = None,
return_dict: bool = True,
jit: bool = False,
debug: bool = False,
neg_prompt_ids: jnp.array = None,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
latents (`jnp.array`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
jit (`bool`, defaults to `False`):
Whether to run `pmap` versions of the generation and safety scoring functions. NOTE: This argument
exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a future release.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of
a plain tuple.
Returns:
[`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a
`tuple. When returning a tuple, the first element is a list with the generated images, and the second
element is a list of `bool`s denoting whether the corresponding generated image likely represents
"not-safe-for-work" (nsfw) content, according to the `safety_checker`.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
if jit:
images = _p_generate(
self,
prompt_ids,
params,
prng_seed,
num_inference_steps,
height,
width,
guidance_scale,
latents,
debug,
neg_prompt_ids,
)
else:
images = self._generate(
prompt_ids,
params,
prng_seed,
num_inference_steps,
height,
width,
guidance_scale,
latents,
debug,
neg_prompt_ids,
)
if self.safety_checker is not None:
safety_params = params["safety_checker"]
images_uint8_casted = (images * 255).round().astype("uint8")
num_devices, batch_size = images.shape[:2]
images_uint8_casted = np.asarray(images_uint8_casted).reshape(num_devices * batch_size, height, width, 3)
images_uint8_casted, has_nsfw_concept = self._run_safety_checker(images_uint8_casted, safety_params, jit)
images = np.asarray(images)
# block images
if any(has_nsfw_concept):
for i, is_nsfw in enumerate(has_nsfw_concept):
if is_nsfw:
images[i] = np.asarray(images_uint8_casted[i])
images = images.reshape(num_devices, batch_size, height, width, 3)
else:
images = np.asarray(images)
has_nsfw_concept = False
if not return_dict:
return (images, has_nsfw_concept)
return FlaxStableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept)
# TODO: maybe use a config dict instead of so many static argnums
@partial(jax.pmap, static_broadcasted_argnums=(0, 4, 5, 6, 7, 9))
def _p_generate(
pipe,
prompt_ids,
params,
prng_seed,
num_inference_steps,
height,
width,
guidance_scale,
latents,
debug,
neg_prompt_ids,
):
return pipe._generate(
prompt_ids,
params,
prng_seed,
num_inference_steps,
height,
width,
guidance_scale,
latents,
debug,
neg_prompt_ids,
)
@partial(jax.pmap, static_broadcasted_argnums=(0,))
def _p_get_has_nsfw_concepts(pipe, features, params):
return pipe._get_has_nsfw_concepts(features, params)
def unshard(x: jnp.ndarray):
# einops.rearrange(x, 'd b ... -> (d b) ...')
num_devices, batch_size = x.shape[:2]
rest = x.shape[2:]
return x.reshape(num_devices * batch_size, *rest)
| diffusers-ft-main | src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from typing import Callable, List, Optional, Union
import numpy as np
import torch
from transformers import CLIPFeatureExtractor, CLIPTokenizer
from ...configuration_utils import FrozenDict
from ...onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ...pipeline_utils import DiffusionPipeline
from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from ...utils import deprecate, logging
from . import StableDiffusionPipelineOutput
logger = logging.get_logger(__name__)
class OnnxStableDiffusionPipeline(DiffusionPipeline):
vae_encoder: OnnxRuntimeModel
vae_decoder: OnnxRuntimeModel
text_encoder: OnnxRuntimeModel
tokenizer: CLIPTokenizer
unet: OnnxRuntimeModel
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
safety_checker: OnnxRuntimeModel
feature_extractor: CLIPFeatureExtractor
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae_encoder: OnnxRuntimeModel,
vae_decoder: OnnxRuntimeModel,
text_encoder: OnnxRuntimeModel,
tokenizer: CLIPTokenizer,
unet: OnnxRuntimeModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: OnnxRuntimeModel,
feature_extractor: CLIPFeatureExtractor,
requires_safety_checker: bool = True,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
)
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["clip_sample"] = False
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
self.register_modules(
vae_encoder=vae_encoder,
vae_decoder=vae_decoder,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.register_to_config(requires_safety_checker=requires_safety_checker)
def _encode_prompt(self, prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `list(int)`):
prompt to be encoded
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
"""
batch_size = len(prompt) if isinstance(prompt, list) else 1
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="np",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="np").input_ids
if not np.array_equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_embeddings = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]
text_embeddings = np.repeat(text_embeddings, num_images_per_prompt, axis=0)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt] * batch_size
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="np",
)
uncond_embeddings = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0]
uncond_embeddings = np.repeat(uncond_embeddings, num_images_per_prompt, axis=0)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
return text_embeddings
def __call__(
self,
prompt: Union[str, List[str]],
height: Optional[int] = 512,
width: Optional[int] = 512,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: Optional[float] = 0.0,
generator: Optional[np.random.RandomState] = None,
latents: Optional[np.ndarray] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
callback_steps: Optional[int] = 1,
):
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if generator is None:
generator = np.random
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
text_embeddings = self._encode_prompt(
prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
# get the initial random noise unless the user supplied it
latents_dtype = text_embeddings.dtype
latents_shape = (batch_size * num_images_per_prompt, 4, height // 8, width // 8)
if latents is None:
latents = generator.randn(*latents_shape).astype(latents_dtype)
elif latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
# set timesteps
self.scheduler.set_timesteps(num_inference_steps)
latents = latents * np.float(self.scheduler.init_noise_sigma)
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
timestep_dtype = next(
(input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)"
)
timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)
latent_model_input = latent_model_input.cpu().numpy()
# predict the noise residual
timestep = np.array([t], dtype=timestep_dtype)
noise_pred = self.unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=text_embeddings)
noise_pred = noise_pred[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
scheduler_output = self.scheduler.step(
torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs
)
latents = scheduler_output.prev_sample.numpy()
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
latents = 1 / 0.18215 * latents
# image = self.vae_decoder(latent_sample=latents)[0]
# it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
image = np.concatenate(
[self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
)
image = np.clip(image / 2 + 0.5, 0, 1)
image = image.transpose((0, 2, 3, 1))
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(
self.numpy_to_pil(image), return_tensors="np"
).pixel_values.astype(image.dtype)
image, has_nsfw_concepts = self.safety_checker(clip_input=safety_checker_input, images=image)
# There will throw an error if use safety_checker batchsize>1
images, has_nsfw_concept = [], []
for i in range(image.shape[0]):
image_i, has_nsfw_concept_i = self.safety_checker(
clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
)
images.append(image_i)
has_nsfw_concept.append(has_nsfw_concept_i[0])
image = np.concatenate(images)
else:
has_nsfw_concept = None
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
class StableDiffusionOnnxPipeline(OnnxStableDiffusionPipeline):
def __init__(
self,
vae_encoder: OnnxRuntimeModel,
vae_decoder: OnnxRuntimeModel,
text_encoder: OnnxRuntimeModel,
tokenizer: CLIPTokenizer,
unet: OnnxRuntimeModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: OnnxRuntimeModel,
feature_extractor: CLIPFeatureExtractor,
):
deprecation_message = "Please use `OnnxStableDiffusionPipeline` instead of `StableDiffusionOnnxPipeline`."
deprecate("StableDiffusionOnnxPipeline", "1.0.0", deprecation_message)
super().__init__(
vae_encoder=vae_encoder,
vae_decoder=vae_decoder,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
| diffusers-ft-main | src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from typing import Callable, List, Optional, Union
import numpy as np
import torch
import PIL
from diffusers.utils import is_accelerate_available
from packaging import version
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from ...configuration_utils import FrozenDict
from ...models import AutoencoderKL, UNet2DConditionModel
from ...pipeline_utils import DiffusionPipeline
from ...schedulers import DDIMScheduler
from ...utils import PIL_INTERPOLATION, deprecate, logging
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def preprocess(image):
w, h = image.size
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.0 * image - 1.0
def posterior_sample(scheduler, latents, timestep, clean_latents, generator, eta):
# 1. get previous step value (=t-1)
prev_timestep = timestep - scheduler.config.num_train_timesteps // scheduler.num_inference_steps
if prev_timestep <= 0:
return clean_latents
# 2. compute alphas, betas
alpha_prod_t = scheduler.alphas_cumprod[timestep]
alpha_prod_t_prev = (
scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else scheduler.final_alpha_cumprod
)
variance = scheduler._get_variance(timestep, prev_timestep)
std_dev_t = eta * variance ** (0.5)
# direction pointing to x_t
e_t = (latents - alpha_prod_t ** (0.5) * clean_latents) / (1 - alpha_prod_t) ** (0.5)
dir_xt = (1.0 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * e_t
noise = std_dev_t * torch.randn(
clean_latents.shape, dtype=clean_latents.dtype, device=clean_latents.device, generator=generator
)
prev_latents = alpha_prod_t_prev ** (0.5) * clean_latents + dir_xt + noise
return prev_latents
def compute_noise(scheduler, prev_latents, latents, timestep, noise_pred, eta):
# 1. get previous step value (=t-1)
prev_timestep = timestep - scheduler.config.num_train_timesteps // scheduler.num_inference_steps
# 2. compute alphas, betas
alpha_prod_t = scheduler.alphas_cumprod[timestep]
alpha_prod_t_prev = (
scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else scheduler.final_alpha_cumprod
)
beta_prod_t = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)
# 4. Clip "predicted x_0"
if scheduler.config.clip_sample:
pred_original_sample = torch.clamp(pred_original_sample, -1, 1)
# 5. compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
variance = scheduler._get_variance(timestep, prev_timestep)
std_dev_t = eta * variance ** (0.5)
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * noise_pred
noise = (prev_latents - (alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction)) / (
variance ** (0.5) * eta
)
return noise
class CycleDiffusionPipeline(DiffusionPipeline):
r"""
Pipeline for text-guided image to image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: DDIMScheduler,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
requires_safety_checker: bool = True,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_attention_slicing
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.
Args:
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
if isinstance(self.unet.config.attention_head_dim, int):
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
else:
# if `attention_head_dim` is a list, take the smallest head size
slice_size = min(self.unet.config.attention_head_dim)
self.unet.set_attention_slice(slice_size)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_attention_slicing
def disable_attention_slicing(self):
r"""
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
back to computing attention in one step.
"""
# set slice_size = `None` to disable `attention slicing`
self.enable_attention_slicing(None)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload
def enable_sequential_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
`torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
device = torch.device(f"cuda:{gpu_id}")
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
if cpu_offloaded_model is not None:
cpu_offload(cpu_offloaded_model, device)
if self.safety_checker is not None:
# TODO(Patrick) - there is currently a bug with cpu offload of nn.Parameter in accelerate
# fix by only offloading self.safety_checker for now
cpu_offload(self.safety_checker.vision_model, device)
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def _execution_device(self):
r"""
Returns the device on which the pipeline's models will be executed. After calling
`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
hooks.
"""
if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
return self.device
for module in self.unet.modules():
if (
hasattr(module, "_hf_hook")
and hasattr(module._hf_hook, "execution_device")
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device)
return self.device
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_xformers_memory_efficient_attention
def enable_xformers_memory_efficient_attention(self):
r"""
Enable memory efficient attention as implemented in xformers.
When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference
time. Speed up at training time is not guaranteed.
Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention
is used.
"""
self.unet.set_use_memory_efficient_attention_xformers(True)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_xformers_memory_efficient_attention
def disable_xformers_memory_efficient_attention(self):
r"""
Disable memory efficient attention as implemented in xformers.
"""
self.unet.set_use_memory_efficient_attention_xformers(False)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `list(int)`):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
"""
batch_size = len(prompt) if isinstance(prompt, list) else 1
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids
if not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
text_embeddings = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
text_embeddings = text_embeddings[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
uncond_embeddings = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
uncond_embeddings = uncond_embeddings[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_embeddings.shape[1]
uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
return text_embeddings
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.check_inputs
def check_inputs(self, prompt, strength, callback_steps):
if not isinstance(prompt, str) and not isinstance(prompt, list):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [1.0, 1.0] but is {strength}")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
else:
has_nsfw_concept = None
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
offset = self.scheduler.config.get("steps_offset", 0)
init_timestep = int(num_inference_steps * strength) + offset
init_timestep = min(init_timestep, num_inference_steps)
t_start = max(num_inference_steps - init_timestep + offset, 0)
timesteps = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def prepare_latents(self, init_image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
init_image = init_image.to(device=device, dtype=dtype)
init_latent_dist = self.vae.encode(init_image).latent_dist
init_latents = init_latent_dist.sample(generator=generator)
init_latents = 0.18215 * init_latents
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
# expand init_latents for batch_size
deprecation_message = (
f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
" images (`init_image`). Initial images are now duplicating to match the number of text prompts. Note"
" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
" your script to pass as many init images as text prompts to suppress this warning."
)
deprecate("len(prompt) != len(init_image)", "1.0.0", deprecation_message, standard_warn=False)
additional_image_per_prompt = batch_size // init_latents.shape[0]
init_latents = torch.cat([init_latents] * additional_image_per_prompt * num_images_per_prompt, dim=0)
elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
raise ValueError(
f"Cannot duplicate `init_image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
)
else:
init_latents = torch.cat([init_latents] * num_images_per_prompt, dim=0)
# add noise to latents using the timestep
noise = torch.randn(init_latents.shape, generator=generator, device=device, dtype=dtype)
# get latents
clean_latents = init_latents
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
latents = init_latents
return latents, clean_latents
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
source_prompt: Union[str, List[str]],
init_image: Union[torch.FloatTensor, PIL.Image.Image],
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
source_guidance_scale: Optional[float] = 1,
num_images_per_prompt: Optional[int] = 1,
eta: Optional[float] = 0.1,
generator: Optional[torch.Generator] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
init_image (`torch.FloatTensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to transform the reference `init_image`. Must be between 0 and 1.
`init_image` will be used as a starting point, adding more noise to it the larger the `strength`. The
number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
noise will be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `init_image`.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter will be modulated by `strength`.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
source_guidance_scale (`float`, *optional*, defaults to 1):
Guidance scale for the source prompt. This is useful to control the amount of influence the source
prompt for encoding.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.1):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
# 1. Check inputs
self.check_inputs(prompt, strength, callback_steps)
# 2. Define call parameters
batch_size = 1 if isinstance(prompt, str) else len(prompt)
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
text_embeddings = self._encode_prompt(prompt, device, num_images_per_prompt, do_classifier_free_guidance, None)
source_text_embeddings = self._encode_prompt(
source_prompt, device, num_images_per_prompt, do_classifier_free_guidance, None
)
# 4. Preprocess image
if isinstance(init_image, PIL.Image.Image):
init_image = preprocess(init_image)
# 5. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
# 6. Prepare latent variables
latents, clean_latents = self.prepare_latents(
init_image, latent_timestep, batch_size, num_images_per_prompt, text_embeddings.dtype, device, generator
)
source_latents = latents
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
generator = extra_step_kwargs.pop("generator", None)
# 8. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2)
source_latent_model_input = torch.cat([source_latents] * 2)
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
source_latent_model_input = self.scheduler.scale_model_input(source_latent_model_input, t)
# predict the noise residual
concat_latent_model_input = torch.stack(
[
source_latent_model_input[0],
latent_model_input[0],
source_latent_model_input[1],
latent_model_input[1],
],
dim=0,
)
concat_text_embeddings = torch.stack(
[
source_text_embeddings[0],
text_embeddings[0],
source_text_embeddings[1],
text_embeddings[1],
],
dim=0,
)
concat_noise_pred = self.unet(
concat_latent_model_input, t, encoder_hidden_states=concat_text_embeddings
).sample
# perform guidance
(
source_noise_pred_uncond,
noise_pred_uncond,
source_noise_pred_text,
noise_pred_text,
) = concat_noise_pred.chunk(4, dim=0)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
source_noise_pred = source_noise_pred_uncond + source_guidance_scale * (
source_noise_pred_text - source_noise_pred_uncond
)
# Sample source_latents from the posterior distribution.
prev_source_latents = posterior_sample(
self.scheduler, source_latents, t, clean_latents, generator=generator, **extra_step_kwargs
)
# Compute noise.
noise = compute_noise(
self.scheduler, prev_source_latents, source_latents, t, source_noise_pred, **extra_step_kwargs
)
source_latents = prev_source_latents
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(
noise_pred, t, latents, variance_noise=noise, **extra_step_kwargs
).prev_sample
# call the callback, if provided
if (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0:
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
# 9. Post-processing
image = self.decode_latents(latents)
# 10. Run safety checker
image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)
# 11. Convert to PIL
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| diffusers-ft-main | src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def jax_cosine_distance(emb_1, emb_2, eps=1e-12):
norm_emb_1 = jnp.divide(emb_1.T, jnp.clip(jnp.linalg.norm(emb_1, axis=1), a_min=eps)).T
norm_emb_2 = jnp.divide(emb_2.T, jnp.clip(jnp.linalg.norm(emb_2, axis=1), a_min=eps)).T
return jnp.matmul(norm_emb_1, norm_emb_2.T)
class FlaxStableDiffusionSafetyCheckerModule(nn.Module):
config: CLIPConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.vision_model = FlaxCLIPVisionModule(self.config.vision_config)
self.visual_projection = nn.Dense(self.config.projection_dim, use_bias=False, dtype=self.dtype)
self.concept_embeds = self.param("concept_embeds", jax.nn.initializers.ones, (17, self.config.projection_dim))
self.special_care_embeds = self.param(
"special_care_embeds", jax.nn.initializers.ones, (3, self.config.projection_dim)
)
self.concept_embeds_weights = self.param("concept_embeds_weights", jax.nn.initializers.ones, (17,))
self.special_care_embeds_weights = self.param("special_care_embeds_weights", jax.nn.initializers.ones, (3,))
def __call__(self, clip_input):
pooled_output = self.vision_model(clip_input)[1]
image_embeds = self.visual_projection(pooled_output)
special_cos_dist = jax_cosine_distance(image_embeds, self.special_care_embeds)
cos_dist = jax_cosine_distance(image_embeds, self.concept_embeds)
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign image inputs
adjustment = 0.0
special_scores = special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
special_scores = jnp.round(special_scores, 3)
is_special_care = jnp.any(special_scores > 0, axis=1, keepdims=True)
# Use a lower threshold if an image has any special care concept
special_adjustment = is_special_care * 0.01
concept_scores = cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
concept_scores = jnp.round(concept_scores, 3)
has_nsfw_concepts = jnp.any(concept_scores > 0, axis=1)
return has_nsfw_concepts
class FlaxStableDiffusionSafetyChecker(FlaxPreTrainedModel):
config_class = CLIPConfig
main_input_name = "clip_input"
module_class = FlaxStableDiffusionSafetyCheckerModule
def __init__(
self,
config: CLIPConfig,
input_shape: Optional[Tuple] = None,
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
**kwargs,
):
if input_shape is None:
input_shape = (1, 224, 224, 3)
module = self.module_class(config=config, dtype=dtype, **kwargs)
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensor
clip_input = jax.random.normal(rng, input_shape)
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
random_params = self.module.init(rngs, clip_input)["params"]
return random_params
def __call__(
self,
clip_input,
params: dict = None,
):
clip_input = jnp.transpose(clip_input, (0, 2, 3, 1))
return self.module.apply(
{"params": params or self.params},
jnp.array(clip_input, dtype=jnp.float32),
rngs={},
)
| diffusers-ft-main | src/diffusers/pipelines/stable_diffusion/safety_checker_flax.py |
import inspect
from typing import Callable, List, Optional, Union
import numpy as np
import torch
import PIL
from transformers import CLIPFeatureExtractor, CLIPTokenizer
from ...configuration_utils import FrozenDict
from ...onnx_utils import OnnxRuntimeModel
from ...pipeline_utils import DiffusionPipeline
from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from ...utils import deprecate, logging
from . import StableDiffusionPipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def preprocess(image):
w, h = image.size
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL.Image.LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
return 2.0 * image - 1.0
def preprocess_mask(mask, scale_factor=8):
mask = mask.convert("L")
w, h = mask.size
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL.Image.NEAREST)
mask = np.array(mask).astype(np.float32) / 255.0
mask = np.tile(mask, (4, 1, 1))
mask = mask[None].transpose(0, 1, 2, 3) # what does this step do?
mask = 1 - mask # repaint white, keep black
return mask
class OnnxStableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
r"""
Pipeline for text-guided image inpainting using Stable Diffusion. This is a *legacy feature* for Onnx pipelines to
provide compatibility with StableDiffusionInpaintPipelineLegacy and may be removed in the future.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
vae_encoder: OnnxRuntimeModel
vae_decoder: OnnxRuntimeModel
text_encoder: OnnxRuntimeModel
tokenizer: CLIPTokenizer
unet: OnnxRuntimeModel
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
safety_checker: OnnxRuntimeModel
feature_extractor: CLIPFeatureExtractor
def __init__(
self,
vae_encoder: OnnxRuntimeModel,
vae_decoder: OnnxRuntimeModel,
text_encoder: OnnxRuntimeModel,
tokenizer: CLIPTokenizer,
unet: OnnxRuntimeModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: OnnxRuntimeModel,
feature_extractor: CLIPFeatureExtractor,
requires_safety_checker: bool = True,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
)
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["clip_sample"] = False
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
self.register_modules(
vae_encoder=vae_encoder,
vae_decoder=vae_decoder,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion.OnnxStableDiffusionPipeline._encode_prompt
def _encode_prompt(self, prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `list(int)`):
prompt to be encoded
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
"""
batch_size = len(prompt) if isinstance(prompt, list) else 1
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="np",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="np").input_ids
if not np.array_equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_embeddings = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]
text_embeddings = np.repeat(text_embeddings, num_images_per_prompt, axis=0)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt] * batch_size
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="np",
)
uncond_embeddings = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0]
uncond_embeddings = np.repeat(uncond_embeddings, num_images_per_prompt, axis=0)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
return text_embeddings
def __call__(
self,
prompt: Union[str, List[str]],
init_image: Union[np.ndarray, PIL.Image.Image],
mask_image: Union[np.ndarray, PIL.Image.Image],
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: Optional[float] = 0.0,
generator: Optional[np.random.RandomState] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
callback_steps: Optional[int] = 1,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
init_image (`nd.ndarray` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process. This is the image whose masked region will be inpainted.
mask_image (`nd.ndarray` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, to mask `init_image`. White pixels in the mask will be
replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.uu
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to transform the reference `init_image`. Must be between 0 and 1.
`init_image` will be used as a starting point, adding more noise to it the larger the `strength`. The
number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
noise will be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `init_image`.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter will be modulated by `strength`.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (?) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`np.random.RandomState`, *optional*):
A np.random.RandomState to make generation deterministic.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if generator is None:
generator = np.random
# set timesteps
self.scheduler.set_timesteps(num_inference_steps)
if isinstance(init_image, PIL.Image.Image):
init_image = preprocess(init_image)
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
text_embeddings = self._encode_prompt(
prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
latents_dtype = text_embeddings.dtype
init_image = init_image.astype(latents_dtype)
# encode the init image into latents and scale the latents
init_latents = self.vae_encoder(sample=init_image)[0]
init_latents = 0.18215 * init_latents
# Expand init_latents for batch_size and num_images_per_prompt
init_latents = np.concatenate([init_latents] * num_images_per_prompt, axis=0)
init_latents_orig = init_latents
# preprocess mask
if not isinstance(mask_image, np.ndarray):
mask_image = preprocess_mask(mask_image, 8)
mask_image = mask_image.astype(latents_dtype)
mask = np.concatenate([mask_image] * num_images_per_prompt, axis=0)
# check sizes
if not mask.shape == init_latents.shape:
raise ValueError("The mask and init_image should be the same size!")
# get the original timestep using init_timestep
offset = self.scheduler.config.get("steps_offset", 0)
init_timestep = int(num_inference_steps * strength) + offset
init_timestep = min(init_timestep, num_inference_steps)
timesteps = self.scheduler.timesteps.numpy()[-init_timestep]
timesteps = np.array([timesteps] * batch_size * num_images_per_prompt)
# add noise to latents using the timesteps
noise = generator.randn(*init_latents.shape).astype(latents_dtype)
init_latents = self.scheduler.add_noise(
torch.from_numpy(init_latents), torch.from_numpy(noise), torch.from_numpy(timesteps)
)
init_latents = init_latents.numpy()
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (?) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to ? in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
latents = init_latents
t_start = max(num_inference_steps - init_timestep + offset, 0)
timesteps = self.scheduler.timesteps[t_start:].numpy()
for i, t in enumerate(self.progress_bar(timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
sample=latent_model_input, timestep=np.array([t]), encoder_hidden_states=text_embeddings
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(
torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs
).prev_sample
latents = latents.numpy()
init_latents_proper = self.scheduler.add_noise(
torch.from_numpy(init_latents_orig), torch.from_numpy(noise), torch.from_numpy(np.array([t]))
)
init_latents_proper = init_latents_proper.numpy()
latents = (init_latents_proper * mask) + (latents * (1 - mask))
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
latents = 1 / 0.18215 * latents
# image = self.vae_decoder(latent_sample=latents)[0]
# it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
image = np.concatenate(
[self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
)
image = np.clip(image / 2 + 0.5, 0, 1)
image = image.transpose((0, 2, 3, 1))
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(
self.numpy_to_pil(image), return_tensors="np"
).pixel_values.astype(image.dtype)
# There will throw an error if use safety_checker batchsize>1
images, has_nsfw_concept = [], []
for i in range(image.shape[0]):
image_i, has_nsfw_concept_i = self.safety_checker(
clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
)
images.append(image_i)
has_nsfw_concept.append(has_nsfw_concept_i[0])
image = np.concatenate(images)
else:
has_nsfw_concept = None
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| diffusers-ft-main | src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint_legacy.py |
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