app.py

import gradio as gr
import os
from model import models
from multit2i import (
    load_models, infer_fn, infer_rand_fn, save_gallery,
    change_model, warm_model, get_model_info_md, loaded_models,
    get_positive_prefix, get_positive_suffix, get_negative_prefix, get_negative_suffix,
    get_recom_prompt_type, set_recom_prompt_preset, get_tag_type, randomize_seed, translate_to_en
)

# Устанавливаем максимальные значения
max_images = 8
MAX_SEED = 2**32 - 1
load_models(models)

# CSS для оформления
css = """
.model_info { text-align: center; }
.output { width=112px; height=112px; max_width=112px; max_height=112px; !important; }
.gallery { min_width=512px; min_height=512px; max_height=1024px; !important; }
"""

# Функция установки HF_TOKEN
def set_hf_token(hf_token):
    if hf_token:
        os.environ["HF_TOKEN"] = hf_token  # Сохраняем токен в окружении
        return f"✅ HF_TOKEN установлен!", gr.update(value=hf_token, interactive=True)
    else:
        return "❌ Токен не введен!", gr.update(value="", interactive=True)

# Основной интерфейс
with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", fill_width=True, css=css) as demo:
    
    with gr.Tab("Settings"):  # Вкладка для настройки HF_TOKEN
        gr.Markdown("### Настройки токена Hugging Face")

        hf_token_input = gr.Textbox(label="Введите HF Token", type="password", interactive=True)
        confirm_token = gr.Button("Активировать HF_TOKEN")
        token_status = gr.Markdown("🔴 HF_TOKEN не установлен")

        confirm_token.click(set_hf_token, inputs=[hf_token_input], outputs=[token_status, hf_token_input])

    with gr.Tab("Image Generator"):
        with gr.Row():
            with gr.Column(scale=10): 
                with gr.Group():
                    prompt = gr.Text(label="Prompt", lines=2, max_lines=8, placeholder="1girl, solo, ...", show_copy_button=True)
                    with gr.Accordion("Advanced options", open=False):
                        neg_prompt = gr.Text(label="Negative Prompt", lines=1, max_lines=8, placeholder="")                      
                        with gr.Row():
                            width = gr.Slider(label="Width", maximum=2048, step=32, value=0)
                            height = gr.Slider(label="Height", maximum=2048, step=32, value=0)
                            steps = gr.Slider(label="Inference steps", maximum=100, step=1, value=0)
                        with gr.Row():
                            cfg = gr.Slider(label="Guidance scale", maximum=30.0, step=0.1, value=0)
                            seed = gr.Slider(label="Seed", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
                            seed_rand = gr.Button("🎲 Randomize Seed", size="sm", variant="secondary")
                        recom_prompt_preset = gr.Radio(label="Set Presets", choices=get_recom_prompt_type(), value="Common")
                        with gr.Row():
                            positive_prefix = gr.CheckboxGroup(label="Use Positive Prefix", choices=get_positive_prefix(), value=[])
                            positive_suffix = gr.CheckboxGroup(label="Use Positive Suffix", choices=get_positive_suffix(), value=["Common"])
                            negative_prefix = gr.CheckboxGroup(label="Use Negative Prefix", choices=get_negative_prefix(), value=[])
                            negative_suffix = gr.CheckboxGroup(label="Use Negative Suffix", choices=get_negative_suffix(), value=["Common"])
                    with gr.Row():
                        image_num = gr.Slider(label="Number of images", minimum=1, maximum=max_images, value=1, step=1, interactive=True, scale=2)
                        trans_prompt = gr.Button(value="Translate 📝", variant="secondary", size="sm", scale=2)
                        clear_prompt = gr.Button(value="Clear 🗑️", variant="secondary", size="sm", scale=1)
                with gr.Row():
                    run_button = gr.Button("Generate Image", variant="primary", scale=8)
                    random_button = gr.Button("Random Model 🎲", variant="secondary", scale=3)
                    stop_button = gr.Button('Stop', interactive=False, variant="stop", scale=1)
                with gr.Group():
                    model_name = gr.Dropdown(label="Select Model", choices=list(loaded_models.keys()), value=list(loaded_models.keys())[0], allow_custom_value=True)
                    model_info = gr.Markdown(value=get_model_info_md(list(loaded_models.keys())[0]), elem_classes="model_info")

            with gr.Column(scale=10): 
                with gr.Group():
                    with gr.Row():
                        output = [gr.Image(label='', elem_classes="output", type="filepath", format="png",
                                show_download_button=True, show_share_button=False, show_label=False,
                                interactive=False, min_width=80, visible=True, width=112, height=112) for _ in range(max_images)]
                with gr.Group():
                    results = gr.Gallery(label="Gallery", elem_classes="gallery", interactive=False, show_download_button=True, show_share_button=False,
                                        container=True, format="png", object_fit="cover", columns=2, rows=2)
                    image_files = gr.Files(label="Download", interactive=False)
                    clear_results = gr.Button("Clear Gallery / Download 🗑️", variant="secondary")
        with gr.Column():
            examples = gr.Examples(
                examples = [
                    ["souryuu asuka langley, 1girl, neon genesis evangelion, plugsuit, pilot suit, red bodysuit, sitting, crossing legs, black eye patch, cat hat, throne, symmetrical, looking down, from bottom, looking at viewer, outdoors"],
                    ["sailor moon, magical girl transformation, sparkles and ribbons, soft pastel colors, crescent moon motif, starry night sky background, shoujo manga style"],
                    ["kafuu chino, 1girl, solo"],
                    ["1girl"],
                    ["beautiful sunset"],
                ],
                inputs=[prompt],
                cache_examples=False,
            )
    with gr.Tab("PNG Info"):
        def extract_exif_data(image):
            if image is None: return ""
            try:
                metadata_keys = ['parameters', 'metadata', 'prompt', 'Comment']
                for key in metadata_keys:
                    if key in image.info:
                        return image.info[key]
                return str(image.info)
            except Exception as e:
                return f"Error extracting metadata: {str(e)}"
        with gr.Row():
            with gr.Column():
                image_metadata = gr.Image(label="Image with metadata", type="pil", sources=["upload"])
            with gr.Column():
                result_metadata = gr.Textbox(label="Metadata", show_label=True, show_copy_button=True, interactive=False, container=True, max_lines=99)

                image_metadata.change(
                    fn=extract_exif_data,
                    inputs=[image_metadata],
                    outputs=[result_metadata],
                )
    gr.Markdown(
        f"""This demo was created in reference to the following demos.<br>
[Nymbo/Flood](https://huggingface.co/spaces/Nymbo/Flood), 
[Yntec/ToyWorldXL](https://huggingface.co/spaces/Yntec/ToyWorldXL), 
[Yntec/Diffusion80XX](https://huggingface.co/spaces/Yntec/Diffusion80XX).
            """
    )
    gr.DuplicateButton(value="Duplicate Space")
    gr.Markdown(f"Just a few edits to *model.py* are all it takes to complete your own collection.")

    gr.on(triggers=[run_button.click, prompt.submit, random_button.click], fn=lambda: gr.update(interactive=True), inputs=None, outputs=stop_button, show_api=False)
    model_name.change(change_model, [model_name], [model_info], queue=True, show_api=True)\
    .success(warm_model, [model_name], None, queue=True, show_api=True)
    for i, o in enumerate(output):
        img_i = gr.Number(i, visible=False)
        image_num.change(lambda i, n: gr.update(visible = (i < n)), [img_i, image_num], o, show_api=True)
        gen_event = gr.on(triggers=[run_button.click, prompt.submit],
         fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4: infer_fn(m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4) if (i < n) else None,
         inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg, seed,
                  positive_prefix, positive_suffix, negative_prefix, negative_suffix],
         outputs=[o], queue=True, show_api=False) # Be sure to delete ", queue=False" when activating the stop button
        gen_event2 = gr.on(triggers=[random_button.click],
         fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4: infer_rand_fn(m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4) if (i < n) else None,
         inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg, seed,
                  positive_prefix, positive_suffix, negative_prefix, negative_suffix],
         outputs=[o], queue=True, show_api=False) # Be sure to delete ", queue=False" when activating the stop button
        o.change(save_gallery, [o, results], [results, image_files], show_api=False)
        stop_button.click(lambda: gr.update(interactive=False), None, stop_button, cancels=[gen_event, gen_event2], show_api=False)

    clear_prompt.click(lambda: (None, None), None, [prompt, neg_prompt], queue=True, show_api=True)
    clear_results.click(lambda: (None, None), None, [results, image_files], queue=True, show_api=True)
    recom_prompt_preset.change(set_recom_prompt_preset, [recom_prompt_preset],
     [positive_prefix, positive_suffix, negative_prefix, negative_suffix], queue=True, show_api=True)
    seed_rand.click(randomize_seed, None, [seed], queue=True, show_api=True)
    trans_prompt.click(translate_to_en, [prompt], [prompt], queue=True, show_api=True)\
    .then(translate_to_en, [neg_prompt], [neg_prompt], queue=True, show_api=True)




demo.queue(default_concurrency_limit=240, max_size=240)
demo.launch(max_threads=400, ssr_mode=True)
# https://github.com/gradio-app/gradio/issues/6339

demo.queue(concurrency_count=50)
demo.launch()