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 default_models = models[:num_models] inference_timeout = 600 MAX_SEED=3999999999 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(model_str, prompt): if model_str == 'NA': return None noise = str('') #str(randint(0, 99999999999)) return models_load[model_str](f'{prompt} {noise}') 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 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}') with gr.Blocks() as demo: gr.HTML( """

""" ) gr.HTML( """

For negative prompts, Width and Height, and other features visit John6666's Printing Press 4!

""" ) with gr.Tab('One Image'): model_choice = gr.Dropdown(models, label = f'Choose a model from the {len(models)} available! Try clearing the box and typing on it to filter them!', value = models[0], filterable = True) txt_input = gr.Textbox(label = 'Your prompt:') max_imagesone = 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) gen_button = gr.Button('Generate') #stop_button = gr.Button('Stop', variant = 'secondary', interactive = False) gen_button.click(lambda s: gr.update(interactive = True), None) with gr.Row(): output = [gr.Image(label = '') 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 = gen_button.click(lambda i, n, m, t: gen_fn(m, t) if (i < n) else None, [img_in, num_imagesone, model_choice, txt_input], o, concurrency_limit=None, queue=False) #stop_button.click(lambda s: gr.update(interactive = False), None, stop_button, cancels = [gen_event]) with gr.Row(): gr.HTML( """