import gradio as gr from random import randint from all_models import models from externalmod import gr_Interface_load, randomize_seed 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 = 6 default_models = models[:num_models] inference_timeout = 600 MAX_SEED=3999999999 starting_seed = randint(1941, 2024) 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] async def infer(model_str, prompt, seed=1, timeout=inference_timeout): from pathlib import Path kwargs = {} noise = "" kwargs["seed"] = seed task = asyncio.create_task(asyncio.to_thread(models_load[model_str].fn, prompt=f'{prompt} {noise}', **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_fnseed(model_str, prompt, seed=1): if model_str == 'NA': return None try: loop = asyncio.new_event_loop() result = loop.run_until_complete(infer(model_str, prompt, seed, inference_timeout)) except (Exception, asyncio.CancelledError) as e: print(e) print(f"Task aborted: {model_str}") result = None finally: loop.close() return result with gr.Blocks() as demo: gr.HTML( """

For more than 911 models check out HuggingfaceDiffusion!

""" ) with gr.Tab('🤗 Huggingface Diffusion 🤗'): txt_input = gr.Textbox(label='Your prompt:', lines=4) gen_button = gr.Button('Generate up to 6 images in up to 3 minutes total') with gr.Row(): seed = gr.Slider(label="Use a seed to replicate the same image later (maximum 3999999999)", minimum=0, maximum=MAX_SEED, step=1, value=starting_seed, scale=3) seed_rand = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary", scale=1) seed_rand.click(randomize_seed, None, [seed], queue=False) #stop_button = gr.Button('Stop', variant = 'secondary', interactive = False) gen_button.click(lambda s: gr.update(interactive = True), None) gr.HTML( """

Scroll down to see more images and select models.

""" ) with gr.Row(): output = [gr.Image(label = m, min_width=480) for m in default_models] current_models = [gr.Textbox(m, visible = False) for m in default_models] for m, o in zip(current_models, output): gen_event = gr.on(triggers=[gen_button.click, txt_input.submit], fn=gen_fnseed, inputs=[m, txt_input, seed], outputs=[o], concurrency_limit=None, queue=False) #stop_button.click(lambda s: gr.update(interactive = False), None, stop_button, cancels = [gen_event]) 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 = gr.CheckboxGroup(models, label = f'Choose up to {num_models} different models from the 2 available! Untick them to only use one!', value = default_models, multiselect = True, max_choices = num_models, interactive = True, filterable = False) model_choice.change(update_imgbox, model_choice, output) model_choice.change(extend_choices, model_choice, current_models) with gr.Row(): gr.HTML( """