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 = "a tall slender athletic nude caucasian young woman. perky breasts. gorgeous face. sexy eyes. feme fatale. provocative. stainles steel metal dildo. masturbation. pussy dripping white fluid. high tech space ship interiour."
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 # If private or gated models aren't used, ENV setting is unnecessary.

def get_current_time():
    now = datetime.now()
    now2 = now
    current_time = now2.strftime("%y-%m-%d %H:%M:%S")
    return current_time

def load_fn(models):
    global models_load
    models_load = {}
    for model in models:
        if model not in models_load.keys():
            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.update({model: m})


load_fn(models)


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


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


def update_imgbox(choices):
    choices_plus = extend_choices(choices[:num_models])
    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)


# https://huggingface.co/docs/api-inference/detailed_parameters
# https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
async def infer(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1, timeout=inference_timeout):
    kwargs = {}
    if height > 0: kwargs["height"] = height
    if width > 0: kwargs["width"] = width
    if steps > 0: kwargs["num_inference_steps"] = steps
    if cfg > 0: cfg = kwargs["guidance_scale"] = cfg
    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))
    await asyncio.sleep(0)
    try:
        result = await asyncio.wait_for(task, timeout=timeout)
    except asyncio.TimeoutError as e:
        print(e)
        print(f"Task timed out: {model_str}")
        if not task.done(): task.cancel()
        result = None
        raise Exception(f"Task timed out: {model_str}") from e
    except Exception as e:
        print(e)
        if not task.done(): task.cancel()
        result = None
        raise Exception() from e
    if task.done() and result is not None and not isinstance(result, tuple):
        with lock:
            # png_path = "img.png"
            global loopcounter 
            # png_path = get_current_time() + "_" + str(loopcounter) + ".png"
            png_path = get_current_time() + "_" + model_str.replace("/", "_") + ".png"
            # png_path = get_current_time() + "_" + str(models_load[model_str]) + "_" + str(seed) + ".png"
            loopcounter += 1
            image = save_image(result, png_path, model_str, prompt, nprompt, height, width, steps, cfg, seed)
        return image
    return None


def gen_fn(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1):
    try:
        loop = asyncio.new_event_loop()
        result = loop.run_until_complete(infer(model_str, prompt, nprompt,
                                         height, width, steps, cfg, seed, inference_timeout))
    except (Exception, asyncio.CancelledError) as e:
        print(e)
        print(f"Task aborted: {model_str}")
        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 gallery is None: gallery = []
    with lock:
        if image is not None: gallery.insert(0, (image, model_str))
    return gallery


CSS="""
.gradio-container { max-width: 1200px; margin: 0 auto; !important; }
.output { width=112px; height=112px; max_width=112px; max_height=112px; !important; }
.gallery { min_width=512px; min_height=512px; max_height=512px; !important; }
.guide { text-align: center; !important; }
"""

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;
    }
}
"""

js_AutoSave="""

        console.log("Yo");
        
        var img1 = document.querySelector("div#component-355 .svelte-1kpcxni button.svelte-1kpcxni .svelte-1kpcxni img"),
        observer = new MutationObserver((changes) => {
            changes.forEach(change => {
                    if(change.attributeName.includes('src')){
                        console.log(img1.src);
                        document.querySelector("div#component-355 .svelte-1kpcxni .svelte-sr71km a.svelte-1s8vnbx button").click();
                    }
            });
        });
        observer.observe(img1, {attributes : true});
        
"""

with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', fill_width=True, css=CSS) as demo:
# with gr.Blocks(theme='JohnSmith9982/small_and_pretty', fill_width=True, css=CSS, js=js_func) 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, visible=True):
                    with gr.Row():
                        width = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
                        height = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
                    with gr.Row():
                        steps = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
                        cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
                        seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", 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)
            with gr.Row():
                gen_button = gr.Button(f'Generate up to {int(num_models)} images', variant='primary', scale=3)
                random_button = gr.Button(f'Randomize Models', variant='secondary', scale=1)
                #stop_button = gr.Button('Stop', variant='stop', interactive=False, scale=1)
                #gen_button.click(lambda: gr.update(interactive=True), None, stop_button)
            gr.Markdown("", elem_classes="guide")

        with gr.Column(scale=1):
            with gr.Group():
                with gr.Row():
                    output = [gr.Image(label=m, show_download_button=True, elem_classes="output",
                              interactive=False, width=112, height=112, show_share_button=False, 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, show_share_button=False, container=True, 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],
                              concurrency_limit=None, queue=False) # Be sure to delete ", queue=False" when activating the stop button
            o.change(add_gallery, [o, m, gallery], [gallery])
            #stop_button.click(lambda: gr.update(interactive=False), None, stop_button, cancels=[gen_event])

        with gr.Column(scale=4):
            with gr.Accordion('Model selection'):
                model_choice = gr.CheckboxGroup(models, label = f'Choose up to {int(num_models)} different models from the {len(models)} available!', value=default_models, interactive=True)
                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'):
        with gr.Column(scale=2):
            model_choice2 = gr.Dropdown(models, label='Choose model', value=models[0])
            with gr.Group():
                # global preSetPrompt
                # global negPreSetPrompt
                txt_input2 = gr.Textbox(label='Your prompt:', value = preSetPrompt, lines=3, autofocus=1)
                neg_input2 = gr.Textbox(label='Negative prompt:', value=negPreSetPrompt, lines=1)
                with gr.Accordion("Advanced", open=False, visible=True):
                    with gr.Row():
                        width2 = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
                        height2 = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
                    with gr.Row():
                        steps2 = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
                        cfg2 = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
                        seed2 = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
                        seed_rand2 = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary")
                        seed_rand2.click(randomize_seed, None, [seed2], queue=False)
            num_images = gr.Slider(1, max_images, value=max_images, step=1, label='Number of images')
            with gr.Row():
                gen_button2 = gr.Button('Generate', variant='primary', scale=2)
                #stop_button2 = gr.Button('Stop', variant='stop', interactive=False, scale=1)
                #gen_button2.click(lambda: gr.update(interactive=True), None, stop_button2)

        with gr.Column(scale=1):
            with gr.Group():
                with gr.Row():
                    output2 = [gr.Image(label='', show_download_button=True, elem_classes="output",
                               interactive=False, width=112, height=112, visible=True, format="png",
                               show_share_button=False, show_label=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, container=True, format="png",
                                preview=True, object_fit="cover", columns=2, rows=2) 

        for i, o in enumerate(output2):
            img_i = gr.Number(i, visible=False)
            num_images.change(lambda i, n: gr.update(visible = (i < n)), [img_i, num_images], o, queue=False)
            gen_event2 = gr.on(triggers=[gen_button2.click, txt_input2.submit],
                               fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, n5: gen_fn(m, t1, t2, n1, n2, n3, n4, n5) if (i < n) else None,
                               inputs=[img_i, num_images, model_choice2, txt_input2, neg_input2,
                                       height2, width2, steps2, cfg2, seed2], outputs=[o],
                                       concurrency_limit=None, queue=False)  # Be sure to delete ", queue=False" when activating the stop button
            o.change(add_gallery, [o, model_choice2, gallery2], [gallery2])
            #stop_button2.click(lambda: gr.update(interactive=False), None, stop_button2, cancels=[gen_event2])
    
    # gr.Markdown(js_AutoSave)
    gr.Markdown("")

# demo.queue(default_concurrency_limit=200, max_size=200)
demo.launch(show_api=False, max_threads=400)
# demo.launch(show_api=False, max_threads=400, js=js_AutoSave)