# -*- coding: utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.
import argparse
import glob
import io
import os
from PIL import Image
from scepter.modules.transform.io import pillow_convert
from scepter.modules.utils.config import Config
from scepter.modules.utils.file_system import FS
from examples.examples import all_examples
from inference.ace_plus_diffusers import ACEPlusDiffuserInference
inference_dict = {
"ACE_DIFFUSER_PLUS": ACEPlusDiffuserInference
}
fs_list = [
Config(cfg_dict={"NAME": "HuggingfaceFs", "TEMP_DIR": "./cache"}, load=False),
Config(cfg_dict={"NAME": "ModelscopeFs", "TEMP_DIR": "./cache"}, load=False),
Config(cfg_dict={"NAME": "HttpFs", "TEMP_DIR": "./cache"}, load=False),
Config(cfg_dict={"NAME": "LocalFs", "TEMP_DIR": "./cache"}, load=False),
]
for one_fs in fs_list:
FS.init_fs_client(one_fs)
def run_one_case(pipe,
input_image = None,
input_mask = None,
input_reference_image = None,
save_path = "examples/output/example.png",
instruction = "",
output_h = 1024,
output_w = 1024,
seed = -1,
sample_steps = None,
guide_scale = None,
repainting_scale = None,
model_path = None,
**kwargs):
if input_image is not None:
input_image = Image.open(io.BytesIO(FS.get_object(input_image)))
input_image = pillow_convert(input_image, "RGB")
if input_mask is not None:
input_mask = Image.open(io.BytesIO(FS.get_object(input_mask)))
input_mask = pillow_convert(input_mask, "L")
if input_reference_image is not None:
input_reference_image = Image.open(io.BytesIO(FS.get_object(input_reference_image)))
input_reference_image = pillow_convert(input_reference_image, "RGB")
image, seed = pipe(
reference_image=input_reference_image,
edit_image=input_image,
edit_mask=input_mask,
prompt=instruction,
output_height=output_h,
output_width=output_w,
sampler='flow_euler',
sample_steps=sample_steps or pipe.input.get("sample_steps", 28),
guide_scale=guide_scale or pipe.input.get("guide_scale", 50),
seed=seed,
repainting_scale=repainting_scale or pipe.input.get("repainting_scale", 1.0),
lora_path = model_path
)
with FS.put_to(save_path) as local_path:
image.save(local_path)
return local_path, seed
def run():
parser = argparse.ArgumentParser(description='Argparser for Scepter:\n')
parser.add_argument('--instruction',
dest='instruction',
help='The instruction for editing or generating!',
default="")
parser.add_argument('--output_h',
dest='output_h',
help='The height of output image for generation tasks!',
type=int,
default=1024)
parser.add_argument('--output_w',
dest='output_w',
help='The width of output image for generation tasks!',
type=int,
default=1024)
parser.add_argument('--input_reference_image',
dest='input_reference_image',
help='The input reference image!',
default=None
)
parser.add_argument('--input_image',
dest='input_image',
help='The input image!',
default=None
)
parser.add_argument('--input_mask',
dest='input_mask',
help='The input mask!',
default=None
)
parser.add_argument('--save_path',
dest='save_path',
help='The save path for output image!',
default='examples/output_images/output.png'
)
parser.add_argument('--seed',
dest='seed',
help='The seed for generation!',
type=int,
default=-1)
parser.add_argument('--step',
dest='step',
help='The sample step for generation!',
type=int,
default=None)
parser.add_argument('--guide_scale',
dest='guide_scale',
help='The guide scale for generation!',
type=int,
default=None)
parser.add_argument('--repainting_scale',
dest='repainting_scale',
help='The repainting scale for content filling generation!',
type=int,
default=None)
parser.add_argument('--task_type',
dest='task_type',
choices=['portrait', 'subject', 'local_editing'],
help="Choose the task type.",
default='')
parser.add_argument('--task_model',
dest='task_model',
help='The models list for different tasks!',
default="./models/model_zoo.yaml")
parser.add_argument('--infer_type',
dest='infer_type',
choices=['diffusers'],
default='diffusers',
help="Choose the inference scripts. 'native' refers to using the official implementation of ace++, "
"while 'diffusers' refers to using the adaptation for diffusers")
parser.add_argument('--cfg_folder',
dest='cfg_folder',
help='The inference config!',
default="./config")
cfg = Config(load=True, parser_ins=parser)
model_yamls = glob.glob(os.path.join(cfg.args.cfg_folder, '*.yaml'))
model_choices = dict()
for i in model_yamls:
model_cfg = Config(load=True, cfg_file=i)
model_name = model_cfg.NAME
model_choices[model_name] = model_cfg
if cfg.args.infer_type == "native":
infer_name = "ace_plus_native_infer"
elif cfg.args.infer_type == "diffusers":
infer_name = "ace_plus_diffuser_infer"
else:
raise ValueError("infer_type should be native or diffusers")
assert infer_name in model_choices
# choose different model
task_model_cfg = Config(load=True, cfg_file=cfg.args.task_model)
task_model_dict = {}
for task_name, task_model in task_model_cfg.MODEL.items():
task_model_dict[task_name] = task_model
# choose the inference scripts.
pipe_cfg = model_choices[infer_name]
infer_name = pipe_cfg.get("INFERENCE_TYPE", "ACE_PLUS")
pipe = inference_dict[infer_name]()
pipe.init_from_cfg(pipe_cfg)
if cfg.args.instruction == "" and cfg.args.input_image is None and cfg.args.input_reference_image is None:
params = {
"output_h": cfg.args.output_h,
"output_w": cfg.args.output_w,
"sample_steps": cfg.args.step,
"guide_scale": cfg.args.guide_scale
}
# run examples
for example in all_examples:
example["model_path"] = FS.get_from(task_model_dict[example["task_type"].upper()]["MODEL_PATH"])
example.update(params)
if example["edit_type"] == "repainting":
example["repainting_scale"] = 1.0
else:
example["repainting_scale"] = task_model_dict[example["task_type"].upper()].get("REPAINTING_SCALE", 1.0)
print(example)
local_path, seed = run_one_case(pipe, **example)
else:
assert cfg.args.task_type.upper() in task_model_cfg
params = {
"input_image": cfg.args.input_image,
"input_mask": cfg.args.input_mask,
"input_reference_image": cfg.args.input_reference_image,
"save_path": cfg.args.save_path,
"instruction": cfg.args.instruction,
"output_h": cfg.args.output_h,
"output_w": cfg.args.output_w,
"sample_steps": cfg.args.step,
"guide_scale": cfg.args.guide_scale,
"repainting_scale": cfg.args.repainting_scale,
"model_path": FS.get_from(task_model_dict[cfg.args.task_type.upper()]["MODEL_PATH"])
}
local_path, seed = run_one_case(pipe, **params)
print(local_path, seed)
if __name__ == '__main__':
run()