app.py

import gradio as gr
import os
from all_models import models
from externalmod import gr_Interface_load, save_image, randomize_seed
from prompt_extend import extend_prompt
import asyncio
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.

inference_timeout = 600
MAX_SEED = 2**32-1
current_model = models[0]
text_gen1 = extend_prompt

models2 = [gr_Interface_load(f"models/{m}", live=False, preprocess=True, postprocess=False, hf_token=HF_TOKEN) for m in models]

def text_it1(inputs, text_gen1=text_gen1):
        go_t1 = text_gen1(inputs)
        return(go_t1)

def set_model(current_model):
    current_model = models[current_model]
    return gr.update(label=(f"{current_model}"))

def send_it1(inputs, model_choice, neg_input, height, width, steps, cfg, seed):
        output1 = gen_fn(model_choice, inputs, neg_input, height, width, steps, cfg, seed)
        return (output1)

# https://huggingface.co/docs/api-inference/detailed_parameters
# https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
async def infer(model_index, 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(models2[model_index].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: {models[model_index]}")
        if not task.done(): task.cancel()
        result = None
        raise Exception(f"Task timed out: {models[model_index]}") 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 = "image.png"
            image = save_image(result, png_path, models[model_index], prompt, nprompt, height, width, steps, cfg, seed)
        return image
    return None

def gen_fn(model_index, 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_index, prompt, nprompt,
                                         height, width, steps, cfg, seed, inference_timeout))
    except (Exception, asyncio.CancelledError) as e:
        print(e)
        print(f"Task aborted: {models[model_index]}")
        result = None
        raise gr.Error(f"Task aborted: {models[model_index]}, Error: {e}")
    finally:
        loop.close()
    return result

css="""
.gradio-container {background-image: linear-gradient(#254150, #1e2f40, #182634) !important;
 color: #ffaa66 !important; font-family: 'IBM Plex Sans', sans-serif !important;}
h1 {font-size: 6em; color: #ffc99f; margin-top: 30px; margin-bottom: 30px;
 text-shadow: 3px 3px 0 rgba(0, 0, 0, 1) !important;}
h3 {color: #ffc99f; !important;}
h4 {display: inline-block; color: #ffffff !important;}
.wrapper img {font-size: 98% !important; white-space: nowrap !important; text-align: center !important;
display: inline-block !important; color: #ffffff !important;}
.wrapper {color: #ffffff !important;}
.gr-box {background-image: linear-gradient(#182634, #1e2f40, #254150) !important;
 border-top-color: #000000 !important; border-right-color: #ffffff !important;
 border-bottom-color: #ffffff !important; border-left-color: #000000 !important;}
"""

with gr.Blocks(theme='John6666/YntecDark', fill_width=True, css=css) as myface:
    gr.HTML(f"""
        <div style="text-align: center; max-width: 1200px; margin: 0 auto;">
        <div class="center"><h1>Blitz Diffusion</h1></div>
        <p style="margin-bottom: 1px; color: #ffaa66;">
        <h3>{int(len(models))} Stable Diffusion models, but why? For your enjoyment!</h3></p>
        <br><div class="wrapper">11.21 <img src="https://huggingface.co/Yntec/DucHaitenLofi/resolve/main/NEW.webp" alt="NEW!" style="width:32px;height:16px;">This has become a legacy backup copy of old <u><a href="https://huggingface.co/spaces/Yntec/ToyWorld">ToyWorld</a></u>'s UI! Newer models added dailty over there! 10 new models since last update!</div>
        <p style="margin-bottom: 1px; font-size: 98%">
        <br><h4>If a model is already loaded each new image takes less than <b>10</b> seconds to generate!</h4></p>
        <p style="margin-bottom: 1px; color: #ffffff;">
        <br><div class="wrapper">Generate 6 images from 1 prompt at the <u><a href="https://huggingface.co/spaces/Yntec/PrintingPress">PrintingPress</a></u>, and use 6 different models at <u><a href="https://huggingface.co/spaces/Yntec/diffusion80xx">Huggingface Diffusion!</a></u>!
        </p></p></div>
        """, elem_classes="gr-box")
    with gr.Row():
        with gr.Column(scale=100):
            # Model selection dropdown
            model_name1 = gr.Dropdown(label="Select Model", choices=[m for m in models], type="index",
                                      value=current_model, interactive=True, elem_classes=["gr-box", "gr-input"])
    with gr.Row():
        with gr.Column(scale=100):
            with gr.Group():
                magic1 = gr.Textbox(label="Your Prompt", lines=4, elem_classes=["gr-box", "gr-input"]) #Positive
                with gr.Accordion("Advanced", open=False, visible=True):
                    neg_input = gr.Textbox(label='Negative prompt', lines=1, elem_classes=["gr-box", "gr-input"])
                    with gr.Row():
                        width = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0, elem_classes=["gr-box", "gr-input"])
                        height = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0, elem_classes=["gr-box", "gr-input"])
                    with gr.Row():
                        steps = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=33, step=1, value=0, elem_classes=["gr-box", "gr-input"])
                        cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=-1, elem_classes=["gr-box", "gr-input"])
                        seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1, elem_classes=["gr-box", "gr-input"])
                        seed_rand = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary")
            run = gr.Button("Generate Image", variant="primary", elem_classes="gr-button")

    with gr.Row():
        with gr.Column():
            output1 = gr.Image(label=(f"{current_model}"), show_download_button=True,
                               interactive=False, show_share_button=False, format=".png", elem_classes="gr-box")
                
    with gr.Row():
        with gr.Column(scale=50):
            input_text=gr.Textbox(label="Use this box to extend an idea automagically, by typing some words and clicking Extend Idea", lines=2, elem_classes=["gr-box", "gr-input"])
            see_prompts=gr.Button("Extend Idea -> overwrite the contents of the `Your Prompt´ box above", variant="primary", elem_classes="gr-button")
            use_short=gr.Button("Copy the contents of this box to the `Your Prompt´ box above", variant="primary", elem_classes="gr-button")
    def short_prompt(inputs):
        return (inputs)
    
    model_name1.change(set_model, inputs=model_name1, outputs=[output1])
    gr.on(
        triggers=[run.click, magic1.submit],
        fn=send_it1,
        inputs=[magic1, model_name1, neg_input, height, width, steps, cfg, seed],
        outputs=[output1],
        concurrency_limit=None,
        queue=False,
    )
    use_short.click(short_prompt, inputs=[input_text], outputs=magic1)
    see_prompts.click(text_it1, inputs=[input_text], outputs=magic1)
    seed_rand.click(randomize_seed, None, [seed], queue=False)
    
myface.queue(default_concurrency_limit=200, max_size=200)
myface.launch(show_api=False, max_threads=400)
# https://github.com/gradio-app/gradio/issues/6339