app.py

import gradio as gr
from random import randint
from all_models import models
from externalmod import gr_Interface_load
import asyncio
import os
from threading import RLock
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 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 = 1
max_imagesone = 1
max_images = 6
default_models = models[:num_models]
inference_timeout = 300
MAX_SEED = 2**32-1

def extend_choices(choices):
    return choices + (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 gen_fn_original(model_str, prompt):
    if model_str == 'NA':
        return None
    noise = str('') #str(randint(0, 99999999999))
    return models_load[model_str](f'{prompt} {noise}')

def gen_fnsix(model_str, prompt):
    if model_str == 'NA':
        return None
    noisesix = str(randint(1941, 2023)) #str(randint(0, 99999999999))
    return models_load[model_str](f'{prompt} {noisesix}')

# 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=None, width=None, steps=None, cfg=None, seed=-1, timeout=inference_timeout):
    from pathlib import Path
    kwargs = {}
    if height is not None and height >= 256: kwargs["height"] = height
    if width is not None and width >= 256: kwargs["width"] = width
    if steps is not None and steps >= 1: kwargs["num_inference_steps"] = steps
    if cfg is not None and cfg > 0: cfg = kwargs["guidance_scale"] = cfg
    noise = ""
    if seed >= 0: kwargs["seed"] = seed
    else:
        rand = randint(1, 500)
        for i in range(rand):
            noise += " "
    task = asyncio.create_task(asyncio.to_thread(models_load[model_str].fn,
                               prompt=f'{prompt} {noise}', negative_prompt=nprompt, **kwargs, token=HF_TOKEN))
    await asyncio.sleep(0)
    try:
        result = await asyncio.wait_for(task, timeout=timeout)
    except (Exception, asyncio.TimeoutError) as e:
        print(e)
        print(f"Task timed out: {model_str}")
        if not task.done(): task.cancel()
        result = None
    if task.done() and result is not None:
        with lock:
            png_path = "image.png"
            result.save(png_path)
            image = str(Path(png_path).resolve())
        return image
    return None

def gen_fn(model_str, prompt, nprompt="", height=None, width=None, steps=None, cfg=None, seed=-1):
    if model_str == 'NA':
        return None
    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
    finally:
        loop.close()
    return result

css="""

.gradio-container {max-width: 1200px; margin: 0 auto; !important;}

.output { width=128px; height=128px; !important; }

.outputone { width=512px; height=512px; !important; }

"""
with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=css) as demo:
    gr.HTML(
    """

        <div>

        <p> <center><img src="https://huggingface.co/Yntec/OpenGenDiffusers/resolve/main/pp.png" style="height:128px; width:482px; margin-top: -22px; margin-bottom: -44px;" span title="Free ai art image generator Printing Press"></center>

        </p>

    """
)    
    with gr.Tab('One Image'):
        model_choice = gr.Dropdown(models, label=f'Choose a model from the {int(len(models))} available! Try clearing the box and typing on it to filter them!', value=models[0], filterable=True)
        with gr.Group():
            txt_input = gr.Textbox(label='Your prompt:', lines=1)
            with gr.Accordion("Advanced", open=False, visible=True):
                neg_input = gr.Textbox(label='Negative prompt:', lines=1)
                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)
        num_imagesone = gr.Slider(1, max_imagesone, value=max_imagesone, step=1, label='Nobody gets to see this label so I can put here whatever I want!', visible=False)
        
        with gr.Row():
            gen_button = gr.Button('Generate', variant='primary', scale=3)
            #stop_button = gr.Button('Stop', variant='secondary', interactive=False, scale=1)
        #gen_button.click(lambda: gr.update(interactive=True), None, stop_button)
        
        with gr.Row():
            output = [gr.Image(label='', show_download_button=True, elem_classes="outputone",
                               interactive=False, min_width=80, show_share_button=False, format="png",
                               visible=True) for _ in range(max_imagesone)]

        for i, o in enumerate(output):
            img_in = gr.Number(i, visible = False)
            num_imagesone.change(lambda i, n: gr.update(visible = (i < n)), [img_in, num_imagesone], o, show_progress = False)
            gen_event = gr.on(triggers=[gen_button.click, txt_input.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_in, num_imagesone, model_choice, 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
            #stop_button.click(lambda: gr.update(interactive = False), None, stop_button, cancels=[gen_event])
        with gr.Row():
            gr.HTML(
    """

        <div class="footer">

        <p> Based on the <a href="https://huggingface.co/spaces/derwahnsinn/TestGen">TestGen</a> Space by derwahnsinn, the <a href="https://huggingface.co/spaces/RdnUser77/SpacIO_v1">SpacIO</a> Space by RdnUser77, Omnibus's Maximum Multiplier, <a href="https://huggingface.co/spaces/Yntec/Diffusion60XX">Diffusion60XX</a> and <a href="https://huggingface.co/spaces/Yntec/ToyWorld">Toy World</a>!

        </p>

    """
)
    with gr.Tab('Up To Six'):
        model_choice2 = gr.Dropdown(models, label=f'Choose a model from the {int(len(models))} available! Try clearing the box and typing on it to filter them!',
                                    value=models[0], filterable=True)
        with gr.Group():
            txt_input2 = gr.Textbox(label='Your prompt:', lines=1)
            with gr.Accordion("Advanced", open=False, visible=True):
                neg_input2 = gr.Textbox(label='Negative prompt:', lines=1)
                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)

        num_images = gr.Slider(1, max_images, value=max_images, step=1, 
         label=f'Number of images (if you want less than {int(max_images)} decrease them slowly until they match the boxes below)')
        
        with gr.Row():
            gen_button2 = gr.Button(f'Generate up to {int(max_images)} images in up to 3 minutes total', scale=3)
            #stop_button2 = gr.Button('Stop', variant='secondary', interactive=False, scale=1)
        #gen_button2.click(lambda: gr.update(interactive=True), None, stop_button2)
        gr.HTML(
        """

            <div style="text-align: center; max-width: 1200px; margin: 0 auto;">

              <div>

                <body>

                <div class="center"><p style="margin-bottom: 10px;">Scroll down to see more images (they generate in a random order).</p>

                </div>

                </body>

              </div>

            </div>

        """
               )
        with gr.Row():
            output2 = [gr.Image(label = '', show_download_button=True, elem_classes="output",
                                interactive=False, min_width=80, visible=True, format="png",
                                show_share_button=False, show_label=False) for _ in range(max_images)]

        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, show_progress=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
            #stop_button2.click(lambda: gr.update(interactive=False), None, stop_button2, cancels=[gen_event2])
        with gr.Row():
            gr.HTML(
    """

        <div class="footer">

        <p> Based on the <a href="https://huggingface.co/spaces/derwahnsinn/TestGen">TestGen</a> Space by derwahnsinn, the <a href="https://huggingface.co/spaces/RdnUser77/SpacIO_v1">SpacIO</a> Space by RdnUser77, Omnibus's Maximum Multiplier, <a href="https://huggingface.co/spaces/Yntec/Diffusion60XX">Diffusion60XX</a> and <a href="https://huggingface.co/spaces/Yntec/ToyWorld">Toy World</a>!

        </p>

    """
)

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