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| import gradio as gr | |
| import os | |
| import sys | |
| from pathlib import Path | |
| from all_models import models | |
| from externalmod import gr_Interface_load | |
| from prompt_extend import extend_prompt | |
| from random import randint | |
| 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 = 300 | |
| 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=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(models2[model_index].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 asyncio.TimeoutError as e: | |
| print(e) | |
| print(f"Task timed out: {models2[model_index]}") | |
| if not task.done(): task.cancel() | |
| result = None | |
| raise Exception(f"Task timed out: {models2[model_index]}") | |
| except Exception as e: | |
| print(e) | |
| if not task.done(): task.cancel() | |
| result = None | |
| raise Exception(e) | |
| if task.done() and result is not None and not isinstance(result, tuple): | |
| with lock: | |
| png_path = "image.png" | |
| result.save(png_path) | |
| image = str(Path(png_path).resolve()) | |
| return image | |
| return None | |
| def gen_fn(model_index, prompt, nprompt="", height=None, width=None, steps=None, cfg=None, 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: {models2[model_index]}") | |
| result = None | |
| raise gr.Error(f"Task aborted: {models2[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='Yntec/YntecDarkTheme', 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">9.3 <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! 25 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=100, 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=0, 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"]) | |
| 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, queue=False) | |
| see_prompts.click(text_it1, inputs=[input_text], outputs=magic1, 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 | |