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| import gradio as gr | |
| from config import howManyModelsToUse,num_models,max_images,inference_timeout,MAX_SEED,thePrompt,preSetPrompt,negPreSetPrompt | |
| from all_models import models | |
| import asyncio | |
| from externalmod import gr_Interface_load, save_image, randomize_seed | |
| import os | |
| from threading import RLock | |
| lock = RLock() | |
| HF_TOKEN = os.getenv("ohgoddamn") | |
| default_models = models[:num_models] | |
| def get_current_time(): | |
| from datetime import datetime | |
| now = datetime.now() | |
| current_time = now.strftime("%y-%m-%d %H:%M:%S") | |
| return current_time | |
| def load_fn(models, HF_TOKEN): | |
| global models_load | |
| models_load = {} | |
| for model in models: | |
| if model not in models_load: | |
| try: | |
| m = gr_Interface_load(f'models/{model}', hf_token=HF_TOKEN) | |
| models_load[model] = m.fn # Store the callable | |
| except Exception as error: | |
| print(error) | |
| models_load[model] = lambda **kwargs: None | |
| async def infer(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1, timeout=120, hf_token=None): | |
| print(f"{prompt}\n{model_str}\n{timeout}\n") | |
| kwargs = {} | |
| if height > 0: kwargs["height"] = height | |
| if width > 0: kwargs["width"] = width | |
| if steps > 0: kwargs["num_inference_steps"] = steps | |
| if cfg > 0: kwargs["guidance_scale"] = cfg | |
| kwargs["negative_prompt"] = nprompt | |
| theSeed = randomize_seed() if seed == -1 else seed | |
| kwargs["seed"] = theSeed | |
| if hf_token: | |
| kwargs["token"] = hf_token | |
| try: | |
| task = asyncio.create_task(asyncio.to_thread(models_load[model_str], prompt=prompt, **kwargs)) | |
| result = await asyncio.wait_for(task, timeout=timeout) | |
| except asyncio.TimeoutError as e: | |
| print(f"Timeout: {model_str}") | |
| if not task.done(): task.cancel() | |
| raise Exception(f"Timeout: {model_str}") from e | |
| except Exception as e: | |
| print(f"Exception: {model_str} -> {e}") | |
| if not task.done(): task.cancel() | |
| raise Exception(f"Inference failed: {model_str}") from e | |
| if result is not None and not isinstance(result, tuple): | |
| with lock: | |
| png_path = model_str.replace("/", "_") + " - " + get_current_time() + "_" + str(theSeed) + ".png" | |
| image = save_image(result, png_path, model_str, prompt, nprompt, height, width, steps, cfg, theSeed) | |
| return image | |
| return None | |
| def gen_fn(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1, inference_timeout2=120): | |
| try: | |
| loop = asyncio.new_event_loop() | |
| result = loop.run_until_complete(infer(model_str, prompt, nprompt, | |
| height, width, steps, cfg, seed, inference_timeout2, HF_TOKEN)) | |
| except Exception as e: | |
| print(f"gen_fn: Task aborted: {model_str} -> {e}") | |
| raise gr.Error(f"Task aborted: {model_str}, Error: {e}") | |
| finally: | |
| loop.close() | |
| return result | |
| ''' | |
| def load_fn(models,HF_TOKEN): | |
| 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}) | |
| async def infer(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1, timeout=inference_timeout): | |
| print(f"{prompt}\n") | |
| print(f"{model_str}\n") | |
| print(f"{timeout}\n") | |
| kwargs = {} | |
| if height > 0: kwargs["height"] = height | |
| if width > 0: kwargs["width"] = width | |
| if steps > 0: kwargs["num_inference_steps"] = steps | |
| if cfg > 0: cfg = kwargs["guidance_scale"] = cfg | |
| if seed == -1: | |
| theSeed = randomize_seed() | |
| else: | |
| theSeed = seed | |
| kwargs["seed"] = theSeed | |
| task = asyncio.create_task(asyncio.to_thread(models_load[model_str].fn, prompt=prompt, negative_prompt=nprompt, **kwargs, token=HF_TOKEN)) | |
| print(f"await") | |
| await asyncio.sleep(20) | |
| try: | |
| result = await asyncio.wait_for(task, timeout=timeout) | |
| except asyncio.TimeoutError as e: | |
| print(e) | |
| print(f"infer: Task timed out: {model_str}") | |
| if not task.done(): task.cancel() | |
| result = None | |
| raise Exception(f"Task timed out: {model_str}") from e | |
| except Exception as e: | |
| print(e) | |
| print(f"infer: exception: {model_str}") | |
| if not task.done(): task.cancel() | |
| result = None | |
| raise Exception() from e | |
| if task.done() and result is not None and not isinstance(result, tuple): | |
| print(f"{result}") | |
| with lock: | |
| png_path = model_str.replace("/", "_") + " - " + get_current_time() + "_" + str(theSeed) + ".png" | |
| image = save_image(result, png_path, model_str, prompt, nprompt, height, width, steps, cfg, theSeed) | |
| return image | |
| return None | |
| def gen_fn(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1, inference_timeout2=120): | |
| try: | |
| loop = asyncio.new_event_loop() | |
| result = loop.run_until_complete(infer(model_str, prompt, nprompt, | |
| height, width, steps, cfg, seed, inference_timeout2)) | |
| except (Exception, asyncio.CancelledError) as e: | |
| print(e) | |
| print(f"gen_fn: Task aborted: {model_str}") | |
| result = None | |
| raise gr.Error(f"Task aborted: {model_str}, Error: {e}") | |
| finally: | |
| loop.close() | |
| return result | |
| ''' |