Spaces:
Running
on
Zero
Running
on
Zero
File size: 16,749 Bytes
a866b04 68221c5 a866b04 0e6b5f9 a866b04 12f63d9 a866b04 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 |
import torch
import torchvision.transforms.functional as TF
import numpy as np
import random
import os
import sys
from diffusers.utils import load_image
from diffusers import EulerDiscreteScheduler, T2IAdapter
from huggingface_hub import hf_hub_download
import spaces
import gradio as gr
from pipeline_t2i_adapter import PhotoMakerStableDiffusionXLAdapterPipeline
from face_utils import FaceAnalysis2, analyze_faces
from style_template import styles
from aspect_ratio_template import aspect_ratios
# global variable
base_model_path = 'SG161222/RealVisXL_V4.0'
face_detector = FaceAnalysis2(providers=['CPUExecutionProvider', 'CUDAExecutionProvider'], allowed_modules=['detection', 'recognition'])
face_detector.prepare(ctx_id=0, det_size=(640, 640))
try:
if torch.cuda.is_available():
device = "cuda"
elif sys.platform == "darwin" and torch.backends.mps.is_available():
device = "mps"
else:
device = "cpu"
except:
device = "cpu"
MAX_SEED = np.iinfo(np.int32).max
STYLE_NAMES = list(styles.keys())
DEFAULT_STYLE_NAME = "Photographic (Default)"
ASPECT_RATIO_LABELS = list(aspect_ratios)
DEFAULT_ASPECT_RATIO = ASPECT_RATIO_LABELS[0]
enable_doodle_arg = False
photomaker_ckpt = hf_hub_download(repo_id="TencentARC/PhotoMaker", filename="photomaker-v2.bin", repo_type="model")
if torch.cuda.is_available() and torch.cuda.is_bf16_supported():
torch_dtype = torch.bfloat16
else:
torch_dtype = torch.float16
if device == "mps":
torch_dtype = torch.float16
# load adapter
adapter = T2IAdapter.from_pretrained(
"TencentARC/t2i-adapter-sketch-sdxl-1.0", torch_dtype=torch_dtype, variant="fp16"
).to(device)
pipe = PhotoMakerStableDiffusionXLAdapterPipeline.from_pretrained(
base_model_path,
adapter=adapter,
torch_dtype=torch_dtype,
use_safetensors=True,
variant="fp16",
).to(device)
pipe.load_photomaker_adapter(
os.path.dirname(photomaker_ckpt),
subfolder="",
weight_name=os.path.basename(photomaker_ckpt),
trigger_word="img",
pm_version="v2",
)
pipe.id_encoder.to(device)
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
# pipe.set_adapters(["photomaker"], adapter_weights=[1.0])
pipe.fuse_lora()
pipe.to(device)
@spaces.GPU
def generate_image(
upload_images,
prompt,
negative_prompt,
aspect_ratio_name,
style_name,
num_steps,
style_strength_ratio,
num_outputs,
guidance_scale,
seed,
use_doodle,
sketch_image,
adapter_conditioning_scale,
adapter_conditioning_factor,
progress=gr.Progress(track_tqdm=True)
):
if use_doodle:
sketch_image = sketch_image["composite"]
r, g, b, a = sketch_image.split()
sketch_image = a.convert("RGB")
sketch_image = TF.to_tensor(sketch_image) > 0.5 # Inversion
sketch_image = TF.to_pil_image(sketch_image.to(torch.float32))
adapter_conditioning_scale = adapter_conditioning_scale
adapter_conditioning_factor = adapter_conditioning_factor
else:
adapter_conditioning_scale = 0.
adapter_conditioning_factor = 0.
sketch_image = None
# check the trigger word
image_token_id = pipe.tokenizer.convert_tokens_to_ids(pipe.trigger_word)
input_ids = pipe.tokenizer.encode(prompt)
if image_token_id not in input_ids:
raise gr.Error(f"Cannot find the trigger word '{pipe.trigger_word}' in text prompt! Please refer to step 2️⃣")
if input_ids.count(image_token_id) > 1:
raise gr.Error(f"Cannot use multiple trigger words '{pipe.trigger_word}' in text prompt!")
# determine output dimensions by the aspect ratio
output_w, output_h = aspect_ratios[aspect_ratio_name]
print(f"[Debug] Generate image using aspect ratio [{aspect_ratio_name}] => {output_w} x {output_h}")
# apply the style template
prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt)
if upload_images is None:
raise gr.Error(f"Cannot find any input face image! Please refer to step 1️⃣")
input_id_images = []
for img in upload_images:
input_id_images.append(load_image(img))
id_embed_list = []
for img in input_id_images:
img = np.array(img)
img = img[:, :, ::-1]
faces = analyze_faces(face_detector, img)
if len(faces) > 0:
id_embed_list.append(torch.from_numpy((faces[0]['embedding'])))
if len(id_embed_list) == 0:
raise gr.Error(f"No face detected, please update the input face image(s)")
id_embeds = torch.stack(id_embed_list)
generator = torch.Generator(device=device).manual_seed(seed)
print("Start inference...")
print(f"[Debug] Seed: {seed}")
print(f"[Debug] Prompt: {prompt}, \n[Debug] Neg Prompt: {negative_prompt}")
start_merge_step = int(float(style_strength_ratio) / 100 * num_steps)
if start_merge_step > 30:
start_merge_step = 30
print(start_merge_step)
images = pipe(
prompt=prompt,
width=output_w,
height=output_h,
input_id_images=input_id_images,
negative_prompt=negative_prompt,
num_images_per_prompt=num_outputs,
num_inference_steps=num_steps,
start_merge_step=start_merge_step,
generator=generator,
guidance_scale=guidance_scale,
id_embeds=id_embeds,
image=sketch_image,
adapter_conditioning_scale=adapter_conditioning_scale,
adapter_conditioning_factor=adapter_conditioning_factor,
).images
return images, gr.update(visible=True)
def swap_to_gallery(images):
return gr.update(value=images, visible=True), gr.update(visible=True), gr.update(visible=False)
def upload_example_to_gallery(images, prompt, style, negative_prompt):
return gr.update(value=images, visible=True), gr.update(visible=True), gr.update(visible=False)
def remove_back_to_files():
return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
def change_doodle_space(use_doodle):
if use_doodle:
return gr.update(visible=True)
else:
return gr.update(visible=False)
def remove_tips():
return gr.update(visible=False)
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
if randomize_seed:
seed = random.randint(0, MAX_SEED)
return seed
def apply_style(style_name: str, positive: str, negative: str = "") -> tuple[str, str]:
p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
return p.replace("{prompt}", positive), n + ' ' + negative
def get_image_path_list(folder_name):
image_basename_list = os.listdir(folder_name)
image_path_list = sorted([os.path.join(folder_name, basename) for basename in image_basename_list])
return image_path_list
def get_example():
case = [
[
get_image_path_list('./examples/scarletthead_woman'),
"instagram photo, portrait photo of a woman img, colorful, perfect face, natural skin, hard shadows, film grain",
"(No style)",
"(asymmetry, worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth",
],
[
get_image_path_list('./examples/newton_man'),
"sci-fi, closeup portrait photo of a man img wearing the sunglasses in Iron man suit, face, slim body, high quality, film grain",
"(No style)",
"(asymmetry, worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth",
],
]
return case
### Description and style
logo = r"""
<center><img src='https://photo-maker.github.io/assets/logo.png' alt='PhotoMaker logo' style="width:80px; margin-bottom:10px"></center>
"""
title = r"""
<h1 align="center">PhotoMaker V2: Improved ID Fidelity and Better Controllability than PhotoMaker V1</h1>
"""
description = r"""
<b>Official 🤗 Gradio demo</b> for <a href='https://github.com/TencentARC/PhotoMaker' target='_blank'><b>PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding</b></a>.<br>
The details of PhotoMaker V2 can be found in
<br>
<br>
For previous version of PhotoMaker, you could use our original gradio demos [PhotoMaker](https://huggingface.co/spaces/TencentARC/PhotoMaker) and [PhotoMaker-Style](https://huggingface.co/spaces/TencentARC/PhotoMaker-Style).
<br>
❗️❗️❗️[<b>Important</b>] Personalization steps:<br>
1️⃣ Upload images of someone you want to customize. One image is ok, but more is better. Although we do not perform face detection, the face in the uploaded image should <b>occupy the majority of the image</b>.<br>
2️⃣ Enter a text prompt, making sure to <b>follow the class word</b> you want to customize with the <b>trigger word</b>: `img`, such as: `man img` or `woman img` or `girl img`.<br>
3️⃣ Choose your preferred style template.<br>
4️⃣ <b>(Optional: but new feature)</b> Select the ‘Enable Drawing Doodle...’ option and draw on the canvas<br>
5️⃣ Click the <b>Submit</b> button to start customizing.
"""
article = r"""
If PhotoMaker V2 is helpful, please help to ⭐ the <a href='https://github.com/TencentARC/PhotoMaker' target='_blank'>Github Repo</a>. Thanks!
[![GitHub Stars](https://img.shields.io/github/stars/TencentARC/PhotoMaker?style=social)](https://github.com/TencentARC/PhotoMaker)
---
📝 **Citation**
<br>
If our work is useful for your research, please consider citing:
```bibtex
@article{li2023photomaker,
title={PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding},
author={Li, Zhen and Cao, Mingdeng and Wang, Xintao and Qi, Zhongang and Cheng, Ming-Ming and Shan, Ying},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2024}
}
```
📋 **License**
<br>
Apache-2.0 LICENSE. Please refer to the [LICENSE file](https://huggingface.co/TencentARC/PhotoMaker/blob/main/LICENSE) for details.
📧 **Contact**
<br>
If you have any questions, please feel free to reach me out at <b>zhenli1031@gmail.com</b>.
"""
tips = r"""
### Usage tips of PhotoMaker
1. Upload **more photos**of the person to be customized to **improve ID fidelty**.
2. If you find that the image quality is poor when using doodle for control, you can reduce the conditioning scale and factor of the adapter.
If you have any issues, leave the issue in the discussion page of the space. For a more stable (queue-free) experience, you can duplicate the space.
"""
# We have provided some generate examples and comparisons at: [this website]().
css = '''
.gradio-container {width: 85% !important}
'''
with gr.Blocks(css=css) as demo:
gr.Markdown(logo)
gr.Markdown(title)
gr.Markdown(description)
# gr.DuplicateButton(
# value="Duplicate Space for private use ",
# elem_id="duplicate-button",
# visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
# )
with gr.Row():
with gr.Column():
files = gr.Files(
label="Drag (Select) 1 or more photos of your face",
file_types=["image"]
)
uploaded_files = gr.Gallery(label="Your images", visible=False, columns=5, rows=1, height=200)
with gr.Column(visible=False) as clear_button:
remove_and_reupload = gr.ClearButton(value="Remove and upload new ones", components=files, size="sm")
prompt = gr.Textbox(label="Prompt",
info="Try something like 'a photo of a man/woman img', 'img' is the trigger word.",
placeholder="A photo of a [man/woman img]...")
style = gr.Dropdown(label="Style template", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME)
aspect_ratio = gr.Dropdown(label="Output aspect ratio", choices=ASPECT_RATIO_LABELS, value=DEFAULT_ASPECT_RATIO)
submit = gr.Button("Submit")
enable_doodle = gr.Checkbox(
label="Enable Drawing Doodle for Control", value=enable_doodle_arg,
info="After enabling this option, PhotoMaker will generate content based on your doodle on the canvas, driven by the T2I-Adapter (Quality may be decreased)",
)
with gr.Accordion("T2I-Adapter-Doodle (Optional)", visible=False) as doodle_space:
with gr.Row():
sketch_image = gr.Sketchpad(
label="Canvas",
type="pil",
crop_size=[1024,1024],
layers=False,
canvas_size=(350, 350),
brush=gr.Brush(default_size=5, colors=["#000000"], color_mode="fixed")
)
with gr.Group():
adapter_conditioning_scale = gr.Slider(
label="Adapter conditioning scale",
minimum=0.5,
maximum=1,
step=0.1,
value=0.7,
)
adapter_conditioning_factor = gr.Slider(
label="Adapter conditioning factor",
info="Fraction of timesteps for which adapter should be applied",
minimum=0.5,
maximum=1,
step=0.1,
value=0.8,
)
with gr.Accordion(open=False, label="Advanced Options"):
negative_prompt = gr.Textbox(
label="Negative Prompt",
placeholder="low quality",
value="nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry",
)
num_steps = gr.Slider(
label="Number of sample steps",
minimum=20,
maximum=100,
step=1,
value=50,
)
style_strength_ratio = gr.Slider(
label="Style strength (%)",
minimum=15,
maximum=50,
step=1,
value=20,
)
num_outputs = gr.Slider(
label="Number of output images",
minimum=1,
maximum=4,
step=1,
value=2,
)
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.1,
maximum=10.0,
step=0.1,
value=5,
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Column():
gallery = gr.Gallery(label="Generated Images")
usage_tips = gr.Markdown(label="Usage tips of PhotoMaker", value=tips ,visible=False)
files.upload(fn=swap_to_gallery, inputs=files, outputs=[uploaded_files, clear_button, files])
remove_and_reupload.click(fn=remove_back_to_files, outputs=[uploaded_files, clear_button, files])
enable_doodle.select(fn=change_doodle_space, inputs=enable_doodle, outputs=doodle_space)
input_list = [
files,
prompt,
negative_prompt,
aspect_ratio,
style,
num_steps,
style_strength_ratio,
num_outputs,
guidance_scale,
seed,
enable_doodle,
sketch_image,
adapter_conditioning_scale,
adapter_conditioning_factor
]
submit.click(
fn=remove_tips,
outputs=usage_tips,
).then(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=generate_image,
inputs=input_list,
outputs=[gallery, usage_tips]
)
gr.Examples(
examples=get_example(),
inputs=[files, prompt, style, negative_prompt],
run_on_click=True,
fn=upload_example_to_gallery,
outputs=[uploaded_files, clear_button, files],
)
gr.Markdown(article)
demo.launch()
|