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| 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 = 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=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(models_load[model_str].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: {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_fn(model_str, prompt, nprompt="", height=None, width=None, steps=None, cfg=None, seed=-1): | |
| if model_str == 'NA': | |
| return None | |
| 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 | |
| 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; !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 demo: | |
| with gr.Tab('The Dream'): | |
| 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) | |
| with gr.Row(): | |
| gen_button = gr.Button(f'Generate up to {int(num_models)} images in up to 3 minutes total', scale=3) | |
| random_button = gr.Button(f'Random {int(num_models)} 🎲', variant='secondary', scale=1) | |
| stop_button = gr.Button('Stop', variant='secondary', 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, min_width=80, 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]) | |
| 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) | |
| num_images = gr.Slider(1, max_images, value=max_images, step=1, label='Number of images') | |
| with gr.Row(): | |
| gen_button2 = gr.Button('Generate', scale=2) | |
| stop_button2 = gr.Button('Stop', variant='secondary', 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, min_width=80, 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) | |
| 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]) | |
| 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) | |