app.py

import gradio as gr
from all_models import models
from externalmod import gr_Interface_load, save_image, randomize_seed
import asyncio
import os
from threading import RLock
from datetime import datetime

preSetPrompt = "High fashion studio foto shoot. tall slender 18+ caucasian woman. gorgeous face. photorealistic. f1.4"
negPreSetPrompt = "[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry, text, fuzziness"

lock = RLock()
HF_TOKEN = os.environ.get("HF_TOKEN") if os.environ.get("HF_TOKEN") else None

def get_current_time():
    return datetime.now().strftime("%y-%m-%d %H:%M:%S")

def load_fn(models):
    global models_load
    models_load = {}
    for model in models:
        if model not in models_load:
            try:
                m = gr_Interface_load(f'models/{model}', hf_token=HF_TOKEN)
            except Exception as error:
                print(error)
                m = gr.Interface(lambda: None, ['text'], ['image'])
            models_load[model] = m

load_fn(models)

num_models = 6
max_images = 6
inference_timeout = 400
default_models = models[:num_models]
MAX_SEED = 2**32 - 1

def extend_choices(choices):
    return choices[:num_models] + (num_models - len(choices)) * ['NA']

def update_imgbox(choices):
    choices_plus = extend_choices(choices)
    return [gr.Image(None, label=m, visible=(m != 'NA')) for m in choices_plus]

def random_choices():
    import random
    random.seed()
    return random.choices(models, k=num_models)

async def infer(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1, timeout=inference_timeout):
    kwargs = {"height": height if height > 0 else None, "width": width if width > 0 else None, 
              "num_inference_steps": steps if steps > 0 else None, "guidance_scale": cfg if cfg > 0 else None}
    
    if seed == -1:
        kwargs["seed"] = randomize_seed()
    else:
        kwargs["seed"] = seed

    task = asyncio.create_task(asyncio.to_thread(models_load[model_str].fn, prompt=prompt, negative_prompt=nprompt, **kwargs, token=HF_TOKEN))
    
    try:
        result = await asyncio.wait_for(task, timeout=timeout)
    except asyncio.TimeoutError as e:
        print(f"Task timed out: {model_str}")
        task.cancel()
        result = None
    except Exception as e:
        print(f"Error generating image: {model_str} - {e}")
        task.cancel()
        result = None
    
    if result and not isinstance(result, tuple):
        png_path = f"{model_str.replace('/', '_')}_{get_current_time()}_{kwargs['seed']}.png"
        with lock:
            image = save_image(result, png_path, model_str, prompt, nprompt, height, width, steps, cfg, kwargs["seed"])
        return image
    return None

def gen_fn(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1):
    loop = asyncio.new_event_loop()
    try:
        result = loop.run_until_complete(infer(model_str, prompt, nprompt, height, width, steps, cfg, seed, inference_timeout))
    except (Exception, asyncio.CancelledError) as e:
        print(f"Error generating image: {e}")
        result = None
        raise gr.Error(f"Task aborted: {model_str}, Error: {e}")
    finally:
        loop.close()
    return result

def add_gallery(image, model_str, gallery):
    if image is not None:
        gallery = [(image, model_str)] + gallery[:5]  # Keep only the latest 6 images
    return gallery

# Interface Layout
CSS = """
.gradio-container { max-width: 1200px; margin: 0 auto; }
.output { width: 112px; height: 112px; }
.gallery { min-width: 512px; min-height: 512px; }
"""

js_func = """
function refresh() {
    const url = new URL(window.location);
    if (url.searchParams.get('__theme') !== 'dark') {
        url.searchParams.set('__theme', 'dark');
        window.location.href = url.href;
    }
}
"""

with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', fill_width=True, css=CSS) as demo:
    gr.HTML("")
    
    with gr.Tab('6 Models'):
        with gr.Column(scale=2):
            with gr.Group():
                txt_input = gr.Textbox(label='Your prompt:', value=preSetPrompt, lines=3, autofocus=1)
                neg_input = gr.Textbox(label='Negative prompt:', value=negPreSetPrompt, lines=1)
                with gr.Accordion("Advanced", open=False):
                    width = gr.Slider(label="Width", maximum=1216, step=32, value=0)
                    height = gr.Slider(label="Height", maximum=1216, step=32, value=0)
                    steps = gr.Slider(label="Number of inference steps", maximum=100, step=1, value=0)
                    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")
                    seed_rand.click(randomize_seed, None, [seed], queue=False)
            gen_button = gr.Button(f'Generate up to {num_models} images', variant='primary', scale=3)
            random_button = gr.Button('Randomize Models', variant='secondary', scale=1)
            gr.Markdown("")

        with gr.Row():
            output = [gr.Image(label=m, show_download_button=True, elem_classes="output", interactive=False, width=112, height=112, format="png", visible=True) for m in default_models]
            current_models = [gr.Textbox(m, visible=False) for m in default_models]

        with gr.Column(scale=2):
            gallery = gr.Gallery(label="Output", show_download_button=True, elem_classes="gallery", interactive=False, format="png", preview=True, object_fit="cover", columns=2, rows=2)

        for m, o in zip(current_models, output):
            gen_event = gr.on(triggers=[gen_button.click, txt_input.submit], fn=gen_fn,
                              inputs=[m, txt_input, neg_input, height, width, steps, cfg, seed], outputs=[o])
            o.change(add_gallery, [o, m, gallery], [gallery])

        model_choice = gr.CheckboxGroup(models, label=f'Choose up to {num_models} models', value=default_models)
        model_choice.change(update_imgbox, model_choice, output)
        model_choice.change(extend_choices, model_choice, current_models)
        random_button.click(random_choices, None, model_choice)

    with gr.Tab('Single model'):
        model_choice2 = gr.Dropdown(models, label='Choose model', value=models[0])
        txt_input2 = gr.Textbox(label='Your prompt:', value=preSetPrompt, lines=3, autofocus=1)
        neg_input2 = gr.Textbox(label='Negative prompt:', value=negPreSetPrompt, lines=1)
        num_images = gr.Slider(1, max_images, value=max_images, step=1, label='Number of images')

        gen_button2 = gr.Button('Let the machine hallucinate', variant='primary', scale=2)
        
        output2 = [gr.Image(label='', show_download_button=True, elem_classes="output", interactive=False, width=112, height=112, format="png", show_share_button=False) for _ in range(max_images)]
        
        with gr.Column(scale=2):
            gallery2 = gr.Gallery(label="Output", show_download_button=True, elem_classes="gallery", interactive=False, show_share_button=True, format="png", preview=True, object_fit="cover", columns=2, rows=2)

for i, o in enumerate(output2):
    # Change visibility based on num_images
    num_images.change(
        lambda num_images, i=i: gr.update(visible=(i < num_images)),  # `i` corresponds to the index of the image
        [num_images],  # Only num_images needs to be an input
        o,  # Outputs the updated visibility of the image `o`
        queue=False
    )
    
    # Image generation function
    gen_event2 = gr.on(
        triggers=[gen_button2.click, txt_input2.submit],
        fn=gen_fn,
        inputs=[i, num_images, model_choice2, txt_input2, neg_input2, height, width, steps, cfg, seed],
        outputs=[o]
    )
    
    # Update gallery when a new image is generated
    o.change(add_gallery, [o, model_choice2, gallery2], [gallery2])


    gr.Markdown("")
demo.launch(show_api=False, max_threads=400)