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] new_models = ", ".join([m.split("/")[-1] for m in models[0:6]]) text_gen1 = extend_prompt #text_gen1=gr_Interface_load("spaces/phenomenon1981/MagicPrompt-Stable-Diffusion") #text_gen1=gr_Interface_load("spaces/Yntec/prompt-extend") #text_gen1=gr_Interface_load("spaces/daspartho/prompt-extend") #text_gen1=gr_Interface_load("spaces/Omnibus/MagicPrompt-Stable-Diffusion_link") 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): #negative_prompt, #proc1 = models2[model_choice] #output1 = proc1(inputs) output1 = gen_fn(model_choice, inputs, neg_input, height, width, steps, cfg, seed) #negative_prompt=negative_prompt 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 (Exception, asyncio.TimeoutError) as e: print(e) print(f"Task timed out: {models2[model_index]}") 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_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 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; text-align: center; max-width: 1200px; margin: 0 auto; !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;} .text-gray-500 {color: #ffc99f !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;} .gr-input {color: #ffc99f; !important; background-color: #254150 !important;} :root {--neutral-100: #000000 !important;} .gr-button {color: #ffffff !important; text-shadow: 1px 1px 0 rgba(0, 0, 0, 1) !important; background-image: linear-gradient(#76635a, #d2a489) !important; border-radius: 24px !important; border: solid 1px !important; border-top-color: #ffc99f !important; border-right-color: #000000 !important; border-bottom-color: #000000 !important; border-left-color: #ffc99f !important; padding: 6px 30px;} .gr-button:active {color: #ffc99f !important; font-size: 98% !important; text-shadow: 0px 0px 0 rgba(0, 0, 0, 1) !important; background-image: linear-gradient(#d2a489, #76635a) !important; border-top-color: #000000 !important; border-right-color: #ffffff !important; border-bottom-color: #ffffff !important; border-left-color: #000000 !important;} .gr-button:hover {filter: brightness(130%);} """ with gr.Blocks(fill_width=True, css=css) as myface: gr.HTML(f"""

Toy World

Blitz Diffusion - {int(len(models))} Stable Diffusion models, but why? For your enjoyment!


8.1 NEW!Toys to play with: {new_models} and 6 more!


If a model is already loaded each new image takes less than 10 seconds to generate!


Generate 6 images from 1 prompt at the PrintingPress, and use 6 models simultaneusly at Diffusion80XX!

""", 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") with gr.Row(): with gr.Column(scale=100): with gr.Group(): magic1 = gr.Textbox(label="Your Prompt", lines=4, elem_classes="gr-box") #Positive with gr.Accordion("Advanced", open=False, visible=True, elem_classes="gr-box"): neg_input = gr.Textbox(label='Negative prompt', lines=1, elem_classes="gr-box") 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") height = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0, elem_classes="gr-box") 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") 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") seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1, elem_classes="gr-box") run=gr.Button("Generate Image", elem_classes="gr-button") with gr.Row(): with gr.Column(): output1 = gr.Image(label=(f"{current_model}"), 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") see_prompts = gr.Button("Extend Idea -> overwrite the contents of the `Your Prompt´ box above", elem_classes="gr-button") use_short = gr.Button("Copy the contents of this box to the `Your Prompt´ box above", 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], ) use_short.click(short_prompt, inputs=[input_text], outputs=magic1) see_prompts.click(text_it1, inputs=[input_text], outputs=magic1) myface.queue() myface.launch(inline=True, show_api=False)