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 #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=""" #container { max-width: 1200px; margin: 0 auto; !important; } .output { width=112px; height=112px; !important; } .gallery { width=100%; min_height=768px; !important; } .guide { text-align: center; !important; } """ with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=css) as myface: gr.HTML("""