app.py

import torch
import numpy as np
import random
import os
import sys

from diffusers.utils import load_image
from diffusers import EulerDiscreteScheduler

from huggingface_hub import hf_hub_download
import spaces
import gradio as gr

from photomaker import PhotoMakerStableDiffusionXLPipeline
from style_template import styles

# global variable
base_model_path = 'SG161222/RealVisXL_V3.0'
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)"

# download PhotoMaker checkpoint to cache
photomaker_ckpt = hf_hub_download(repo_id="TencentARC/PhotoMaker", filename="photomaker-v1.bin", repo_type="model")

if device == "mps":
    torch_dtype = torch.float16
else:
    torch_dtype = torch.bfloat16
pipe = PhotoMakerStableDiffusionXLPipeline.from_pretrained(
    base_model_path, 
    torch_dtype=torch_dtype,
    use_safetensors=True, 
    variant="fp16",
    # local_files_only=True,
).to(device)

pipe.load_photomaker_adapter(
    os.path.dirname(photomaker_ckpt),
    subfolder="",
    weight_name=os.path.basename(photomaker_ckpt),
    trigger_word="img"
)
pipe.id_encoder.to(device)

pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
# pipe.set_adapters(["photomaker"], adapter_weights=[1.0])
pipe.fuse_lora()

@spaces.GPU(enable_queue=True)
def generate_image(upload_images, prompt, negative_prompt, style_name, num_steps, style_strength_ratio, num_outputs, guidance_scale, seed, progress=gr.Progress(track_tqdm=True)):
    # 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!")

    # 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))
    
    generator = torch.Generator(device=device).manual_seed(seed)

    print("Start inference...")
    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,
        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,
    ).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 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 = ""):
    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: Customizing Realistic Human Photos via Stacked ID Embedding</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>
<br>
For stylization, you could use our other gradio demo [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 Click the <b>Submit</b> button to start customizing.
"""

article = r"""

If PhotoMaker 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={arXiv preprint arxiv:2312.04461},
  year={2023}
}
```
📋 **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**. If the input is Asian face(s), maybe consider adding 'asian' before the class word, e.g., `asian woman img`
2. When stylizing, does the generated face look too realistic? Try switching to our **other gradio demo** [PhotoMaker-Style](https://huggingface.co/spaces/TencentARC/PhotoMaker-Style). Adjust the **Style strength** to 30-50, the larger the number, the less ID fidelty, but the stylization ability will be better.
3. For **faster** speed, reduce the number of generated images and sampling steps. However, please note that reducing the sampling steps may compromise the ID fidelity.
"""
# We have provided some generate examples and comparisons at: [this website]().
# 3. Don't make the prompt too long, as we will trim it if it exceeds 77 tokens. 
# 4. When generating realistic photos, if it's not real enough, try switching to our other gradio application [PhotoMaker-Realistic]().

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)
            submit = gr.Button("Submit")

            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])

        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=[files, prompt, negative_prompt, style, num_steps, style_strength_ratio, num_outputs, guidance_scale, seed],
            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(share=False)