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 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] 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, 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 = "" 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): 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, 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=""" #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): width = gr.Number(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0) height = gr.Number(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0) steps = gr.Number(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0) cfg = gr.Number(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0) 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], 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): width2 = gr.Number(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0) height2 = gr.Number(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0) steps2 = gr.Number(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0) cfg2 = gr.Number(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0) 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: gen_fn(m, t1, t2, n1, n2, n3, n4) if (i < n) else None, inputs=[img_i, num_images, model_choice2, txt_input2, neg_input2, height2, width2, steps2, cfg2], 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() demo.launch()