import gradio as gr from all_models import models from externalmod import gr_Interface_load, save_image, 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 max_images = 6 inference_timeout = 300 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) # https://huggingface.co/docs/api-inference/detailed_parameters # https://huggingface.co/docs/huggingface_hub/package_reference/inference_client 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: kwargs["seed"] = randomize_seed() else: kwargs["seed"] = seed 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"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) 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 = "image.png" image = save_image(result, png_path, model_str, prompt, nprompt, height, width, steps, cfg, seed) 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"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 CSS=""" .gradio-container { max-width: 1200px; margin: 0 auto; !important; } .output { width=112px; height=112px; max_width=112px; max_height=112px; !important; } .gallery { min_width=512px; min_height=512px; max_height=1024px; !important; } .guide { text-align: center; !important; } """ with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', fill_width=True, css=CSS) as demo: gr.HTML( """

For simultaneous generations without hidden queue check out Toy World! For more options like single model x6 check out Diffusion80XX4sg by John6666!

""" ) with gr.Tab('Huggingface Diffusion'): with gr.Column(scale=2): with gr.Group(): txt_input = gr.Textbox(label='Your prompt:', lines=4) neg_input = gr.Textbox(label='Negative prompt:', 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 in up to 3 minutes total', variant='primary', scale=3) random_button = gr.Button(f'Random {int(num_models)} 🎲', variant='secondary', scale=1) #stop_button = gr.Button('Stop', variant='stop', interactive=False, scale=1) #gen_button.click(lambda: gr.update(interactive=True), None, stop_button) gr.Markdown("Scroll down to see more images and select models.", elem_classes="guide") with gr.Column(scale=1): with gr.Group(): with gr.Row(): output = [gr.Image(label=m, show_download_button=True, elem_classes="output", 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", show_download_button=True, elem_classes="gallery", interactive=False, show_share_button=True, 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) # Be sure to delete ", queue=False" when activating the stop button o.change(add_gallery, [o, m, gallery], [gallery]) #stop_button.click(lambda: gr.update(interactive=False), None, stop_button, cancels=[gen_event]) 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) with gr.Tab('Single model'): with gr.Column(scale=2): model_choice2 = gr.Dropdown(models, label='Choose model', value=models[0]) with gr.Group(): txt_input2 = gr.Textbox(label='Your prompt:', lines=4) neg_input2 = gr.Textbox(label='Negative prompt:', lines=1) with gr.Accordion("Advanced", open=False, visible=True): with gr.Row(): width2 = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0) height2 = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0) with gr.Row(): steps2 = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0) cfg2 = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0) seed2 = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1) seed_rand2 = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary") seed_rand2.click(randomize_seed, None, [seed2], queue=False) num_images = gr.Slider(1, max_images, value=max_images, step=1, label='Number of images') with gr.Row(): gen_button2 = gr.Button('Generate', variant='primary', scale=2) #stop_button2 = gr.Button('Stop', variant='stop', interactive=False, scale=1) #gen_button2.click(lambda: gr.update(interactive=True), None, stop_button2) with gr.Column(scale=1): with gr.Group(): with gr.Row(): output2 = [gr.Image(label='', show_download_button=True, elem_classes="output", interactive=False, width=112, height=112, visible=True, format="png", show_share_button=False, show_label=False) for _ in range(max_images)] with gr.Column(scale=2): gallery2 = gr.Gallery(label="Output", show_download_button=True, elem_classes="gallery", interactive=False, show_share_button=True, container=True, format="png", preview=True, object_fit="cover", columns=2, rows=2) for i, o in enumerate(output2): img_i = gr.Number(i, visible=False) num_images.change(lambda i, n: gr.update(visible = (i < n)), [img_i, num_images], o, queue=False) gen_event2 = gr.on(triggers=[gen_button2.click, txt_input2.submit], fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, n5: gen_fn(m, t1, t2, n1, n2, n3, n4, n5) if (i < n) else None, inputs=[img_i, num_images, model_choice2, txt_input2, neg_input2, height2, width2, steps2, cfg2, seed2], outputs=[o], concurrency_limit=None, queue=False) # Be sure to delete ", queue=False" when activating the stop button o.change(add_gallery, [o, model_choice2, gallery2], [gallery2]) #stop_button2.click(lambda: gr.update(interactive=False), None, stop_button2, cancels=[gen_event2]) gr.Markdown("Based on the [TestGen](https://huggingface.co/spaces/derwahnsinn/TestGen) Space by derwahnsinn, the [SpacIO](https://huggingface.co/spaces/RdnUser77/SpacIO_v1) Space by RdnUser77 and Omnibus's Maximum Multiplier!") #demo.queue(default_concurrency_limit=200, max_size=200) demo.launch(show_api=False, max_threads=400) # https://github.com/gradio-app/gradio/issues/6339