import gradio as gr from all_models import models from prompt import thePrompt, howManyModelsToUse from externalmod import gr_Interface_load, save_image, randomize_seed import asyncio import os from threading import RLock from datetime import datetime preSetPrompt = thePrompt negPreSetPrompt = "[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry, text, fuzziness" 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 get_current_time(): now = datetime.now() current_time = now.strftime("%y-%m-%d %H:%M:%S") return current_time 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 = howManyModelsToUse max_images = howManyModelsToUse inference_timeout = 400 default_models = models[:num_models] MAX_SEED = 2**32-1 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] def random_choices(): import random random.seed() return random.choices(models, k=num_models) async def infer(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1, timeout=inference_timeout): 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)) await asyncio.sleep(0) 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): 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): try: loop = asyncio.new_event_loop() result = loop.run_until_complete(infer(model_str, prompt, nprompt, height, width, steps, cfg, seed, inference_timeout)) 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 def add_gallery(image, model_str, gallery): if gallery is None: gallery = [] with lock: if image is not None: gallery.insert(0, (image, model_str)) return gallery JS=""" """ CSS=""" """ with gr.Blocks(head=CSS + JS) as demo: with gr.Column(scale=2): with gr.Group(): txt_input = gr.Textbox(label='Your prompt:', value=preSetPrompt, lines=3, autofocus=1) neg_input = gr.Textbox(label='Negative prompt:', value=negPreSetPrompt, lines=1) with gr.Accordion("Advanced", open=False, visible=True): with gr.Row(): width = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0) height = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0) 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) cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0) seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1) seed_rand = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary") seed_rand.click(randomize_seed, None, [seed], queue=False) with gr.Row(): gen_button = gr.Button(f'Generate up to {int(num_models)} images', variant='primary', scale=3, elem_classes=["butt"], elem_id=["TheButt"]) random_button = gr.Button(f'Randomize Models', variant='secondary', scale=1) with gr.Column(scale=1): with gr.Group(): with gr.Row(): output = [gr.Image(label=m, show_download_button=True, interactive=False, width=112, height=112, show_share_button=False, format="png", visible=True) for m in default_models] current_models = [gr.Textbox(m, visible=False) for m in default_models] with gr.Column(scale=2): gallery = gr.Gallery(label="Output", visible=False, show_download_button=True,interactive=False, show_share_button=False, container=True, format="png", preview=True, object_fit="cover", columns=2, rows=2) for m, o in zip(current_models, output): gen_event = gr.on(triggers=[gen_button.click, txt_input.submit], fn=gen_fn,inputs=[m, txt_input, neg_input, height, width, steps, cfg, seed], outputs=[o], concurrency_limit=None, queue=False) # o.change(add_gallery, [o, m, gallery], [gallery]) with gr.Column(scale=4): 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.change(update_imgbox, model_choice, output) model_choice.change(extend_choices, model_choice, current_models) random_button.click(random_choices, None, model_choice) demo.launch(show_api=False, max_threads=400)