app.py

# import torch
import numpy as np
import random
import os

# from diffusers.utils import load_image
# from diffusers import DDIMScheduler
# from huggingface_hub import hf_hub_download
# import spaces
import gradio as gr

# from pipeline import PhotoMakerStableDiffusionXLPipeline
# from style_template import styles

# # global variable
# base_model_path = 'SG161222/RealVisXL_V3.0'
# device = "cuda" if torch.cuda.is_available() else "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")

# pipe = PhotoMakerStableDiffusionXLPipeline.from_pretrained(
#     base_model_path, 
#     torch_dtype=torch.bfloat16, 
#     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"
# )     
# pipe.id_encoder.to(device)

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

def generate_image(upload_images, 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!")

    # # Update nsfw negative prompt
    # negative_prompt = f"nsfw, naked, {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
    values = 5
    return upload_images, values

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 = "") -> 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



### 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">Estimating the Values from Gasmeter Through Vision</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.
"""


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)  # uncoment to show desc


    # 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.File(
                        label="Drag (Select) 1 or more photos of your face",
                        file_types=["image"],
                        file_count="multiple"
                    )
            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=['choice1', 'choice2'], value="Default Vlaue")
            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],
            outputs=[gallery, seed] # this will return the value to a seed, can be used for showing values
        )

    # 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=True)