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app.py
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# Copyright (c) Alibaba, Inc. and its affiliates.
import enum
import os
import shutil
import slugify
import time
from concurrent.futures import ProcessPoolExecutor
from torch import multiprocessing
import cv2
import gradio as gr
import numpy as np
import torch
from glob import glob
import platform
import subprocess
from modelscope import snapshot_download
from facechain.inference import preprocess_pose, GenPortrait
from facechain.train_text_to_image_lora import prepare_dataset, data_process_fn
from facechain.constants import neg_prompt, pos_prompt_with_cloth, pos_prompt_with_style, styles, cloth_prompt, \
pose_models, pose_examples, base_models
training_done_count = 0
inference_done_count = 0
class UploadTarget(enum.Enum):
PERSONAL_PROFILE = 'Personal Profile'
LORA_LIaBRARY = 'LoRA Library'
# utils
def concatenate_images(images):
heights = [img.shape[0] for img in images]
max_width = sum([img.shape[1] for img in images])
concatenated_image = np.zeros((max(heights), max_width, 3), dtype=np.uint8)
x_offset = 0
for img in images:
concatenated_image[0:img.shape[0], x_offset:x_offset + img.shape[1], :] = img
x_offset += img.shape[1]
return concatenated_image
# hooks
def update_cloth(style_index):
style = styles[style_index]
prompts = []
if style_index == 0:
example_prompt = generate_pos_prompt(style['name'],
cloth_prompt[0]['prompt'])
multiplier_human = 0.95
for prompt in cloth_prompt:
prompts.append(prompt['name'])
else:
example_prompt = generate_pos_prompt(style['name'],
style['add_prompt_style'])
multiplier_human = style['multiplier_human']
prompts.append(style['cloth_name'])
return gr.Radio.update(choices=prompts, value=prompts[0], visible=True), \
gr.Textbox.update(value=example_prompt), gr.Slider.update(value=multiplier_human)
def update_prompt(style_index, cloth_index):
style = styles[style_index]
if style_index == 0:
pos_prompt = generate_pos_prompt(style['name'],
cloth_prompt[cloth_index]['prompt'])
multiplier_style = 0.25
else:
pos_prompt = generate_pos_prompt(style['name'],
style['add_prompt_style'])
multiplier_style = style['multiplier_style']
return gr.Textbox.update(value=pos_prompt), gr.Slider.update(value=multiplier_style)
def update_pose_model(pose_image, pose_model):
if pose_image is None:
return gr.Radio.update(value=pose_models[0]['name']), gr.Image.update(visible=False)
else:
if pose_model == 0:
pose_model = 1
pose_res_img = preprocess_pose(pose_image)
return gr.Radio.update(value=pose_models[pose_model]['name']), gr.Image.update(value=pose_res_img, visible=True)
def update_optional_styles(base_model_index):
style_list = base_models[base_model_index]['style_list']
optional_styles = '\n'.join(style_list)
return gr.Textbox.update(value=optional_styles)
def train_lora_fn(base_model_path=None, revision=None, sub_path=None, output_img_dir=None, work_dir=None, photo_num=0):
torch.cuda.empty_cache()
lora_r = 4
lora_alpha = 32
max_train_steps = min(photo_num * 200, 800)
if platform.system() == 'Windows':
command = [
'accelerate', 'launch', 'facechain/train_text_to_image_lora.py',
f'--pretrained_model_name_or_path={base_model_path}',
f'--revision={revision}',
f'--sub_path={sub_path}',
f'--output_dataset_name={output_img_dir}',
'--caption_column=text',
'--resolution=512',
'--random_flip',
'--train_batch_size=1',
'--num_train_epochs=200',
'--checkpointing_steps=5000',
'--learning_rate=1.5e-04',
'--lr_scheduler=cosine',
'--lr_warmup_steps=0',
'--seed=42',
f'--output_dir={work_dir}',
f'--lora_r={lora_r}',
f'--lora_alpha={lora_alpha}',
'--lora_text_encoder_r=32',
'--lora_text_encoder_alpha=32',
'--resume_from_checkpoint="fromfacecommon"'
]
try:
subprocess.run(command, check=True)
except subprocess.CalledProcessError as e:
print(f"Error executing the command: {e}")
else:
os.system(
f'PYTHONPATH=. accelerate launch facechain/train_text_to_image_lora.py '
f'--pretrained_model_name_or_path={base_model_path} '
f'--revision={revision} '
f'--sub_path={sub_path} '
f'--output_dataset_name={output_img_dir} '
f'--caption_column="text" '
f'--resolution=512 '
f'--random_flip '
f'--train_batch_size=1 '
f'--num_train_epochs=200 '
f'--checkpointing_steps=5000 '
f'--learning_rate=1.5e-04 '
f'--lr_scheduler="cosine" '
f'--lr_warmup_steps=0 '
f'--seed=42 '
f'--output_dir={work_dir} '
f'--lora_r={lora_r} '
f'--lora_alpha={lora_alpha} '
f'--lora_text_encoder_r=32 '
f'--lora_text_encoder_alpha=32 '
f'--resume_from_checkpoint="fromfacecommon"')
def generate_pos_prompt(style_model, prompt_cloth):
if style_model == styles[0]['name'] or style_model is None:
pos_prompt = pos_prompt_with_cloth.format(prompt_cloth)
else:
matched = list(filter(lambda style: style_model == style['name'], styles))
if len(matched) == 0:
raise ValueError(f'styles not found: {style_model}')
matched = matched[0]
pos_prompt = pos_prompt_with_style.format(matched['add_prompt_style'])
return pos_prompt
def launch_pipeline(uuid,
pos_prompt,
neg_prompt=None,
base_model_index=None,
user_model=None,
num_images=1,
lora_choice=None,
style_model=None,
multiplier_style=0.25,
multiplier_human=0.85,
pose_model=None,
pose_image=None
):
if not uuid:
if os.getenv("MODELSCOPE_ENVIRONMENT") == 'studio':
return "请登陆后使用! (Please login first)"
else:
uuid = 'qw'
# Check base model
if base_model_index == None:
raise gr.Error('请选择基模型(Please select the base model)!')
# Check output model
if not user_model:
raise gr.Error('请选择产出模型(Please select the output model)!')
# Check lora choice
if lora_choice == None:
raise gr.Error('请选择LORA模型(Please select the LORA model)!')
# Check style model
if style_model == None and lora_choice == 'preset':
raise gr.Error('请选择风格模型(Please select the style model)!')
base_model = base_models[base_model_index]['model_id']
revision = base_models[base_model_index]['revision']
sub_path = base_models[base_model_index]['sub_path']
before_queue_size = 0
before_done_count = inference_done_count
style_model = styles[style_model]['name']
if lora_choice == 'preset':
if style_model == styles[0]['name']:
style_model_path = None
else:
matched = list(filter(lambda style: style_model == style['name'], styles))
if len(matched) == 0:
raise ValueError(f'styles not found: {style_model}')
matched = matched[0]
model_dir = snapshot_download(matched['model_id'], revision=matched['revision'])
style_model_path = os.path.join(model_dir, matched['bin_file'])
else:
print(f'uuid: {uuid}')
temp_lora_dir = f"/tmp/{uuid}/temp_lora"
file_name = lora_choice
print(lora_choice.split('.')[-1], os.path.join(temp_lora_dir, file_name))
if lora_choice.split('.')[-1] != 'safetensors' or not os.path.exists(os.path.join(temp_lora_dir, file_name)):
raise ValueError(f'Invalid lora file: {lora_file.name}')
style_model_path = os.path.join(temp_lora_dir, file_name)
if pose_image is None or pose_model == 0:
pose_model_path = None
use_depth_control = False
pose_image = None
else:
model_dir = snapshot_download('damo/face_chain_control_model', revision='v1.0.1')
pose_model_path = os.path.join(model_dir, 'model_controlnet/control_v11p_sd15_openpose')
if pose_model == 1:
use_depth_control = True
else:
use_depth_control = False
print("-------user_model: ", user_model)
if not uuid:
if os.getenv("MODELSCOPE_ENVIRONMENT") == 'studio':
return "请登陆后使用! (Please login first)"
else:
uuid = 'qw'
use_main_model = True
use_face_swap = True
use_post_process = True
use_stylization = False
instance_data_dir = os.path.join('/tmp', uuid, 'training_data', base_model, user_model)
lora_model_path = f'/tmp/{uuid}/{base_model}/{user_model}/ensemble'
if not os.path.exists(lora_model_path):
lora_model_path = f'/tmp/{uuid}/{base_model}/{user_model}/'
train_file = os.path.join(lora_model_path, 'pytorch_lora_weights.bin')
if not os.path.exists(train_file):
raise gr.Error('您还没有进行形象定制, 请先进行训练。(Training is required before inference.)')
gen_portrait = GenPortrait(pose_model_path, pose_image, use_depth_control, pos_prompt, neg_prompt, style_model_path,
multiplier_style, multiplier_human, use_main_model,
use_face_swap, use_post_process,
use_stylization)
num_images = min(6, num_images)
with ProcessPoolExecutor(max_workers=5) as executor:
future = executor.submit(gen_portrait, instance_data_dir,
num_images, base_model, lora_model_path, sub_path, revision)
while not future.done():
is_processing = future.running()
if not is_processing:
cur_done_count = inference_done_count
to_wait = before_queue_size - (cur_done_count - before_done_count)
yield ["排队等待资源中, 前方还有{}个生成任务, 预计需要等待{}分钟...".format(to_wait, to_wait * 2.5),
None]
else:
yield ["生成中, 请耐心等待(Generating)...", None]
time.sleep(1)
outputs = future.result()
outputs_RGB = []
for out_tmp in outputs:
outputs_RGB.append(cv2.cvtColor(out_tmp, cv2.COLOR_BGR2RGB))
save_dir = os.path.join('/tmp', uuid, 'inference_result', base_model, user_model)
if lora_choice == 'preset':
save_dir = os.path.join(save_dir, 'style_' + style_model)
else:
save_dir = os.path.join(save_dir, 'lora_' + os.path.basename(lora_choice).split('.')[0])
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# use single to save outputs
if not os.path.exists(os.path.join(save_dir, 'single')):
os.makedirs(os.path.join(save_dir, 'single'))
for img in outputs:
# count the number of images in the folder
num = len(os.listdir(os.path.join(save_dir, 'single')))
cv2.imwrite(os.path.join(save_dir, 'single', str(num) + '.png'), img)
if len(outputs) > 0:
result = concatenate_images(outputs)
if not os.path.exists(os.path.join(save_dir, 'concat')):
os.makedirs(os.path.join(save_dir, 'concat'))
num = len(os.listdir(os.path.join(save_dir, 'concat')))
image_path = os.path.join(save_dir, 'concat', str(num) + '.png')
cv2.imwrite(image_path, result)
yield ["生成完毕(Generation done)!", outputs_RGB]
else:
yield ["生成失败, 请重试(Generation failed, please retry)!", outputs_RGB]
class Trainer:
def __init__(self):
pass
def run(
self,
uuid: str,
instance_images: list,
base_model_index: int,
output_model_name: str,
) -> str:
# Check Cuda
if not torch.cuda.is_available():
raise gr.Error('CUDA不可用(CUDA not available)')
# Check Instance Valid
if instance_images is None:
raise gr.Error('您需要上传训练图片(Please upload photos)!')
# Check output model name
if not output_model_name:
raise gr.Error('请指定产出模型的名称(Please specify the output model name)!')
# Limit input Image
if len(instance_images) > 20:
raise gr.Error('请最多上传20张训练图片(20 images at most!)')
# Check UUID & Studio
if not uuid:
if os.getenv("MODELSCOPE_ENVIRONMENT") == 'studio':
return "请登陆后使用(Please login first)! "
else:
uuid = 'qw'
base_model_path = base_models[base_model_index]['model_id']
revision = base_models[base_model_index]['revision']
sub_path = base_models[base_model_index]['sub_path']
output_model_name = slugify.slugify(output_model_name)
# mv user upload data to target dir
instance_data_dir = os.path.join('/tmp', uuid, 'training_data', base_model_path, output_model_name)
print("--------uuid: ", uuid)
if not os.path.exists(f"/tmp/{uuid}"):
os.makedirs(f"/tmp/{uuid}")
work_dir = f"/tmp/{uuid}/{base_model_path}/{output_model_name}"
if os.path.exists(work_dir):
raise gr.Error("产出模型名称已存在。")
print("----------work_dir: ", work_dir)
shutil.rmtree(work_dir, ignore_errors=True)
shutil.rmtree(instance_data_dir, ignore_errors=True)
prepare_dataset([img['name'] for img in instance_images], output_dataset_dir=instance_data_dir)
data_process_fn(instance_data_dir, True)
# train lora
print("instance_data_dir", instance_data_dir)
train_lora_fn(base_model_path=base_model_path,
revision=revision,
sub_path=sub_path,
output_img_dir=instance_data_dir,
work_dir=work_dir,
photo_num=len(instance_images))
message = f'训练已经完成!请切换至 [形象体验] 标签体验模型效果(Training done, please switch to the inference tab to generate photos.)'
print(message)
return message
def flash_model_list(uuid, base_model_index, lora_choice:gr.Dropdown):
if base_model_index is None:
return gr.Radio.update(visible=False), gr.Dropdown.update(visible=False)
base_model_path = base_models[base_model_index]['model_id']
style_list = base_models[base_model_index]['style_list']
if not uuid:
if os.getenv("MODELSCOPE_ENVIRONMENT") == 'studio':
return "请登陆后使用! (Please login first)"
else:
uuid = 'qw'
folder_path = f"/tmp/{uuid}/{base_model_path}"
folder_list = []
lora_save_path = f"/tmp/{uuid}/temp_lora"
if not os.path.exists(lora_save_path):
lora_list = ['preset']
else:
lora_list = sorted(os.listdir(lora_save_path))
lora_list = ["preset"] + lora_list
if not os.path.exists(folder_path):
if lora_choice == 'preset':
return gr.Radio.update(choices=[]), \
gr.Dropdown.update(choices=style_list, value=style_list[0], visible=True), gr.Radio.update(visible=True), \
gr.Dropdown.update(choices=lora_list, visible=True), gr.File.update(visible=True)
else:
return gr.Radio.update(choices=[]), \
gr.Dropdown.update(visible=False), gr.Radio.update(visible=False), \
gr.Dropdown.update(choices=lora_list, visible=True), gr.File.update(visible=True)
else:
files = os.listdir(folder_path)
for file in files:
file_path = os.path.join(folder_path, file)
if os.path.isdir(folder_path):
file_lora_path = f"{file_path}/pytorch_lora_weights.bin"
if os.path.exists(file_lora_path):
folder_list.append(file)
if lora_choice == 'preset':
return gr.Radio.update(choices=folder_list), \
gr.Dropdown.update(choices=style_list, value=style_list[0], visible=True), gr.Radio.update(visible=True), \
gr.Dropdown.update(choices=lora_list, visible=True), gr.File.update(visible=True)
else:
return gr.Radio.update(choices=folder_list), \
gr.Dropdown.update(visible=False), gr.Radio.update(visible=False), \
gr.Dropdown.update(choices=lora_list, visible=True), gr.File.update(visible=True)
def update_output_model(uuid, base_model_index):
# Check base model
if base_model_index == None:
raise gr.Error('请选择基模型(Please select the base model)!')
base_model_path = base_models[base_model_index]['model_id']
style_list = base_models[base_model_index]['style_list']
if not uuid:
if os.getenv("MODELSCOPE_ENVIRONMENT") == 'studio':
return "请登陆后使用! (Please login first)"
else:
uuid = 'qw'
folder_path = f"/tmp/{uuid}/{base_model_path}"
folder_list = []
if not os.path.exists(folder_path):
return gr.Radio.update(choices=[]),gr.Dropdown.update(choices=style_list)
else:
files = os.listdir(folder_path)
for file in files:
file_path = os.path.join(folder_path, file)
if os.path.isdir(folder_path):
file_lora_path = f"{file_path}/pytorch_lora_weights.bin"
if os.path.exists(file_lora_path):
folder_list.append(file)
return gr.Radio.update(choices=folder_list)
def upload_file(files, current_files):
file_paths = [file_d['name'] for file_d in current_files] + [file.name for file in files]
return file_paths
def upload_lora_file(uuid, lora_file):
if not uuid:
if os.getenv("MODELSCOPE_ENVIRONMENT") == 'studio':
return "请登陆后使用! (Please login first)"
else:
uuid = 'qw'
print("uuid: ", uuid)
temp_lora_dir = f"/tmp/{uuid}/temp_lora"
if not os.path.exists(temp_lora_dir):
os.makedirs(temp_lora_dir)
shutil.copy(lora_file.name, temp_lora_dir)
filename = os.path.basename(lora_file.name)
newfilepath = os.path.join(temp_lora_dir, filename)
print("newfilepath: ", newfilepath)
lora_list = sorted(os.listdir(temp_lora_dir))
lora_list = ["preset"] + lora_list
return gr.Dropdown.update(choices=lora_list, value=filename)
def clear_lora_file(uuid, lora_file):
if not uuid:
if os.getenv("MODELSCOPE_ENVIRONMENT") == 'studio':
return "请登陆后使用! (Please login first)"
else:
uuid = 'qw'
return gr.Dropdown.update(value="preset")
def change_lora_choice(uuid, lora_choice, base_model_index):
if not uuid:
if os.getenv("MODELSCOPE_ENVIRONMENT") == 'studio':
return "请登陆后使用! (Please login first)"
else:
uuid = 'qw'
style_list = base_models[base_model_index]['style_list']
if lora_choice == 'preset':
style_model = gr.Dropdown.update(choices=style_list, value=style_list[0], visible=True)
else:
style_model = gr.Dropdown.update(choices=style_list, value=style_list[0], visible=False)
cloth_style = gr.Radio.update(visible=False)
return style_model, cloth_style
def deal_history(uuid, base_model_index=None , user_model=None, lora_choice=None, style_model=None, deal_type="load"):
if not uuid:
if os.getenv("MODELSCOPE_ENVIRONMENT") == 'studio':
return "请登陆后使用! (Please login first)"
else:
uuid = 'qw'
if base_model_index is None:
raise gr.Error('请选择基模型(Please select the base model)!')
if user_model is None:
raise gr.Error('请选择用户模型(Please select the user model)!')
if lora_choice is None:
raise gr.Error('请选择LoRa文件(Please select the LoRa file)!')
if style_model is None and lora_choice == 'preset':
raise gr.Error('请选择风格(Please select the style)!')
base_model = base_models[base_model_index]['model_id']
style_model = styles[style_model]['name']
save_dir = os.path.join('/tmp', uuid, 'inference_result', base_model, user_model)
if lora_choice == 'preset':
save_dir = os.path.join(save_dir, 'style_' + style_model)
else:
save_dir = os.path.join(save_dir, 'lora_' + os.path.basename(lora_choice).split('.')[0])
if not os.path.exists(save_dir):
return gr.Gallery.update(value=[], visible=True), gr.Gallery.update(value=[], visible=True)
if deal_type == "load":
single_dir = os.path.join(save_dir, 'single')
concat_dir = os.path.join(save_dir, 'concat')
single_imgs = []
concat_imgs = []
if os.path.exists(single_dir):
single_imgs = sorted(os.listdir(single_dir))
single_imgs = [os.path.join(single_dir, img) for img in single_imgs]
if os.path.exists(concat_dir):
concat_imgs = sorted(os.listdir(concat_dir))
concat_imgs = [os.path.join(concat_dir, img) for img in concat_imgs]
return gr.Gallery.update(value=single_imgs, visible=True), gr.Gallery.update(value=concat_imgs, visible=True)
elif deal_type == "delete":
shutil.rmtree(save_dir)
return gr.Gallery.update(value=[], visible=True), gr.Gallery.update(value=[], visible=True)
def train_input():
trainer = Trainer()
with gr.Blocks() as demo:
uuid = gr.Text(label="modelscope_uuid", visible=False)
with gr.Row():
with gr.Column():
with gr.Box():
gr.Markdown('模型选择(Model list)')
base_model_list = []
for base_model in base_models:
base_model_list.append(base_model['name'])
base_model_index = gr.Radio(label="基模型选择(Base model list)", choices=base_model_list, type="index",
value=base_model_list[0])
optional_style = '\n'.join(base_models[0]['style_list'])
optional_styles = gr.Textbox(label="该基模型支持的风格(Styles supported by this base model.)", lines=3,
value=optional_style, interactive=False)
output_model_name = gr.Textbox(label="产出模型名称(Output model name)", value='test', lines=1)
gr.Markdown('训练图片(Training photos)')
instance_images = gr.Gallery()
with gr.Row():
upload_button = gr.UploadButton("选择图片上传(Upload photos)", file_types=["image"],
file_count="multiple")
clear_button = gr.Button("清空图片(Clear photos)")
clear_button.click(fn=lambda: [], inputs=None, outputs=instance_images)
upload_button.upload(upload_file, inputs=[upload_button, instance_images], outputs=instance_images,
queue=False)
gr.Markdown('''
- Step 1. 上传计划训练的图片, 3~10张头肩照(注意: 请避免图片中出现多人脸、脸部遮挡等情况, 否则可能导致效果异常)
- Step 2. 点击 [开始训练] , 启动形象定制化训练, 约需15分钟, 请耐心等待～
- Step 3. 切换至 [形象体验] , 生成你的风格照片
''')
gr.Markdown('''
- Step 1. Upload 3-10 headshot photos of yours (Note: avoid photos with multiple faces or face obstruction, which may lead to non-ideal result).
- Step 2. Click [Train] to start training for customizing your Digital-Twin, this may take up-to 15 mins.
- Step 3. Switch to [Inference] Tab to generate stylized photos.
''')
run_button = gr.Button('开始训练(等待上传图片加载显示出来再点, 否则会报错)'
'Start training (please wait until photo(s) fully uploaded, otherwise it may result in training failure)')
with gr.Box():
gr.Markdown('''
请等待训练完成, 请勿刷新或关闭页面。
Please wait for the training to complete, do not refresh or close the page.
''')
output_message = gr.Markdown()
with gr.Box():
gr.Markdown('''
碰到抓狂的错误或者计算资源紧张的情况下, 推荐直接在[NoteBook](https://modelscope.cn/my/mynotebook/preset)上进行体验。
安装方法请参考: https://github.com/modelscope/facechain .
If you are experiencing prolonged waiting time, you may try on [ModelScope NoteBook](https://modelscope.cn/my/mynotebook/preset) to prepare your dedicated environment.
You may refer to: https://github.com/modelscope/facechain for installation instruction.
''')
base_model_index.change(fn=update_optional_styles,
inputs=[base_model_index],
outputs=[optional_styles],
queue=False)
run_button.click(fn=trainer.run,
inputs=[
uuid,
instance_images,
base_model_index,
output_model_name,
],
outputs=[output_message])
return demo
def inference_input():
with gr.Blocks() as demo:
uuid = gr.Text(label="modelscope_uuid", visible=False)
with gr.Row():
with gr.Column():
base_model_list = []
for base_model in base_models:
base_model_list.append(base_model['name'])
base_model_index = gr.Radio(label="基模型选择(Base model list)", choices=base_model_list, type="index")
with gr.Row():
with gr.Column(scale=3):
user_model = gr.Radio(label="产出模型(Output Model list)", choices=[], type="value")
with gr.Column(scale=1):
update_button = gr.Button('刷新产出模型(Update output model list)')
style_model_list = []
for style in styles:
style_model_list.append(style['name'])
style_model = gr.Dropdown(choices=style_model_list, type="index", value=style_model_list[0],
label="风格模型(Style model)", visible=False)
prompts = []
for prompt in cloth_prompt:
prompts.append(prompt['name'])
for style in styles[1:]:
prompts.append(style['cloth_name'])
cloth_style = gr.Radio(choices=prompts, value=cloth_prompt[0]['name'],
type="index", label="服装风格(Cloth style)", visible=False)
pmodels = []
for pmodel in pose_models:
pmodels.append(pmodel['name'])
with gr.Accordion("高级选项(Advanced Options)", open=False):
# upload one lora file and show the name or path of the file
with gr.Box():
lora_choice = gr.Dropdown(choices=["preset"], type="value", value="preset", label="LoRA文件(LoRA file)", visible=False)
lora_file = gr.File(
value=None,
label="上传LoRA文件(Upload LoRA file)",
type="file",
file_types=[".safetensors"],
file_count="single",
visible=False,
)
pos_prompt = gr.Textbox(label="提示语(Prompt)", lines=3,
value=generate_pos_prompt(None, cloth_prompt[0]['prompt']),
interactive=True)
neg_prompt = gr.Textbox(label="负向提示语(Negative Prompt)", lines=3,
value="",
interactive=True)
multiplier_style = gr.Slider(minimum=0, maximum=1, value=0.25,
step=0.05, label='风格权重(Multiplier style)')
multiplier_human = gr.Slider(minimum=0, maximum=1.2, value=0.95,
step=0.05, label='形象权重(Multiplier human)')
with gr.Box():
with gr.Row():
pose_image = gr.Image(source='upload', type='filepath', label='姿态图片(Pose image)', height=250)
pose_res_image = gr.Image(source='upload', interactive=False, label='姿态结果(Pose result)', visible=False, height=250)
gr.Examples(pose_examples['man'], inputs=[pose_image], label='男性姿态示例')
gr.Examples(pose_examples['woman'], inputs=[pose_image], label='女性姿态示例')
pose_model = gr.Radio(choices=pmodels, value=pose_models[0]['name'],
type="index", label="姿态控制模型(Pose control model)")
with gr.Box():
num_images = gr.Number(
label='生成图片数量(Number of photos)', value=6, precision=1, minimum=1, maximum=6)
gr.Markdown('''
注意: 最多支持生成6张图片!(You may generate a maximum of 6 photos at one time!)
如果使用自定义LoRA文件, 需要上传LoRA文件, 否则默认使用风格模型的LoRA文件。(LoRA file is required if you are using custom LoRA file, otherwise the default LoRA file of the style model will be used.)
使用自定义LoRA文件时, 需要手动再prompt中输入提示词, 否则可能不能正常触发LoRA文件中的风格。(You need to manually input the prompt when using custom LoRA file, otherwise the style in the LoRA file may not be triggered.)
''')
with gr.Row():
display_button = gr.Button('开始生成(Start!)')
with gr.Column():
history_button = gr.Button('查看历史(Show history)')
load_history_text = gr.Text("load", visible=False)
delete_history_button = gr.Button('删除历史(Delete history)')
delete_history_text = gr.Text("delete", visible=False)
with gr.Box():
infer_progress = gr.Textbox(label="生成进度(Progress)", value="当前无生成任务(No task)", interactive=False)
with gr.Box():
gr.Markdown('生成结果(Result)')
output_images = gr.Gallery(label='Output', show_label=False).style(columns=3, rows=2, height=600,
object_fit="contain")
with gr.Accordion(label="历史生成结果(History)", open=False):
with gr.Row():
single_history = gr.Gallery(label='单张图片(Single image history)')
batch_history = gr.Gallery(label='图片组(Batch image history)')
lora_choice.change(fn=change_lora_choice, inputs=[uuid, lora_choice, base_model_index], outputs=[style_model, cloth_style], queue=False)
lora_file.upload(fn=upload_lora_file, inputs=[uuid, lora_file], outputs=[lora_choice], queue=False)
lora_file.clear(fn=clear_lora_file, inputs=[uuid, lora_file], outputs=[lora_choice], queue=False)
style_model.change(update_cloth, style_model, [cloth_style, pos_prompt, multiplier_human], queue=False)
cloth_style.change(update_prompt, [style_model, cloth_style], [pos_prompt, multiplier_style], queue=False)
pose_image.change(update_pose_model, [pose_image, pose_model], [pose_model, pose_res_image])
base_model_index.change(fn=flash_model_list,
inputs=[uuid, base_model_index, lora_choice],
outputs=[user_model, style_model, cloth_style, lora_choice, lora_file],
queue=False)
update_button.click(fn=update_output_model,
inputs=[uuid, base_model_index],
outputs=[user_model],
queue=False)
display_button.click(fn=launch_pipeline,
inputs=[uuid, pos_prompt, neg_prompt, base_model_index, user_model, num_images, lora_choice, style_model, multiplier_style, multiplier_human,
pose_model, pose_image],
outputs=[infer_progress, output_images])
history_button.click(fn=deal_history,
inputs=[uuid, base_model_index, user_model, lora_choice, style_model, load_history_text],
outputs=[single_history, batch_history])
delete_history_button.click(fn=deal_history,
inputs=[uuid, base_model_index, user_model, lora_choice, style_model, delete_history_text],
outputs=[single_history, batch_history])
return demo
with gr.Blocks(css='style.css') as demo:
gr.Markdown("# <center> \N{fire} FaceChain Potrait Generation ([Github star it here](https://github.com/modelscope/facechain/tree/main) \N{whale}, [Paper cite it here](https://arxiv.org/abs/2308.14256) \N{whale})</center>")
with gr.Tabs():
with gr.TabItem('\N{rocket}形象定制(Train)'):
train_input()
with gr.TabItem('\N{party popper}形象体验(Inference)'):
inference_input()
if __name__ == "__main__":
multiprocessing.set_start_method('spawn')
demo.queue(status_update_rate=1).launch(share=True)