Spaces:
Running
on
Zero
Running
on
Zero
File size: 7,815 Bytes
3b609b9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 |
# Copyright 2024 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 contextlib import nullcontext
from ..models.embeddings import (
ImageProjection,
MultiIPAdapterImageProjection,
)
from ..models.modeling_utils import load_model_dict_into_meta
from ..utils import (
is_accelerate_available,
is_torch_version,
logging,
)
if is_accelerate_available():
pass
logger = logging.get_logger(__name__)
class FluxTransformer2DLoadersMixin:
"""
Load layers into a [`FluxTransformer2DModel`].
"""
def _convert_ip_adapter_image_proj_to_diffusers(self, state_dict, low_cpu_mem_usage=False):
if low_cpu_mem_usage:
if is_accelerate_available():
from accelerate import init_empty_weights
else:
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 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`."
)
updated_state_dict = {}
image_projection = None
init_context = init_empty_weights if low_cpu_mem_usage else nullcontext
if "proj.weight" in state_dict:
# IP-Adapter
num_image_text_embeds = 4
if state_dict["proj.weight"].shape[0] == 65536:
num_image_text_embeds = 16
clip_embeddings_dim = state_dict["proj.weight"].shape[-1]
cross_attention_dim = state_dict["proj.weight"].shape[0] // num_image_text_embeds
with init_context():
image_projection = ImageProjection(
cross_attention_dim=cross_attention_dim,
image_embed_dim=clip_embeddings_dim,
num_image_text_embeds=num_image_text_embeds,
)
for key, value in state_dict.items():
diffusers_name = key.replace("proj", "image_embeds")
updated_state_dict[diffusers_name] = value
if not low_cpu_mem_usage:
image_projection.load_state_dict(updated_state_dict, strict=True)
else:
load_model_dict_into_meta(image_projection, updated_state_dict, device=self.device, dtype=self.dtype)
return image_projection
def _convert_ip_adapter_attn_to_diffusers(self, state_dicts, low_cpu_mem_usage=False):
from ..models.attention_processor import (
FluxIPAdapterJointAttnProcessor2_0,
)
if low_cpu_mem_usage:
if is_accelerate_available():
from accelerate import init_empty_weights
else:
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 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`."
)
# set ip-adapter cross-attention processors & load state_dict
attn_procs = {}
key_id = 0
init_context = init_empty_weights if low_cpu_mem_usage else nullcontext
for name in self.attn_processors.keys():
if name.startswith("single_transformer_blocks"):
attn_processor_class = self.attn_processors[name].__class__
attn_procs[name] = attn_processor_class()
else:
cross_attention_dim = self.config.joint_attention_dim
hidden_size = self.inner_dim
attn_processor_class = FluxIPAdapterJointAttnProcessor2_0
num_image_text_embeds = []
for state_dict in state_dicts:
if "proj.weight" in state_dict["image_proj"]:
num_image_text_embed = 4
if state_dict["image_proj"]["proj.weight"].shape[0] == 65536:
num_image_text_embed = 16
# IP-Adapter
num_image_text_embeds += [num_image_text_embed]
with init_context():
attn_procs[name] = attn_processor_class(
hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim,
scale=1.0,
num_tokens=num_image_text_embeds,
dtype=self.dtype,
device=self.device,
)
value_dict = {}
for i, state_dict in enumerate(state_dicts):
value_dict.update({f"to_k_ip.{i}.weight": state_dict["ip_adapter"][f"{key_id}.to_k_ip.weight"]})
value_dict.update({f"to_v_ip.{i}.weight": state_dict["ip_adapter"][f"{key_id}.to_v_ip.weight"]})
value_dict.update({f"to_k_ip.{i}.bias": state_dict["ip_adapter"][f"{key_id}.to_k_ip.bias"]})
value_dict.update({f"to_v_ip.{i}.bias": state_dict["ip_adapter"][f"{key_id}.to_v_ip.bias"]})
if not low_cpu_mem_usage:
attn_procs[name].load_state_dict(value_dict)
else:
device = self.device
dtype = self.dtype
load_model_dict_into_meta(attn_procs[name], value_dict, device=device, dtype=dtype)
key_id += 1
return attn_procs
def _load_ip_adapter_weights(self, state_dicts, low_cpu_mem_usage=False):
if not isinstance(state_dicts, list):
state_dicts = [state_dicts]
self.encoder_hid_proj = None
attn_procs = self._convert_ip_adapter_attn_to_diffusers(state_dicts, low_cpu_mem_usage=low_cpu_mem_usage)
self.set_attn_processor(attn_procs)
image_projection_layers = []
for state_dict in state_dicts:
image_projection_layer = self._convert_ip_adapter_image_proj_to_diffusers(
state_dict["image_proj"], low_cpu_mem_usage=low_cpu_mem_usage
)
image_projection_layers.append(image_projection_layer)
self.encoder_hid_proj = MultiIPAdapterImageProjection(image_projection_layers)
self.config.encoder_hid_dim_type = "ip_image_proj"
self.to(dtype=self.dtype, device=self.device)
|