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from diffusers import StableDiffusionPipeline |
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from diffusers import StableDiffusionImg2ImgPipeline |
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from diffusers import AutoencoderKL, UNet2DConditionModel |
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import gradio as gr |
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import torch |
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models = [ |
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"nitrosocke/Arcane-Diffusion", |
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"nitrosocke/archer-diffusion", |
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"nitrosocke/elden-ring-diffusion", |
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"nitrosocke/spider-verse-diffusion", |
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"nitrosocke/modern-disney-diffusion", |
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"hakurei/waifu-diffusion", |
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"lambdalabs/sd-pokemon-diffusers", |
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"yuk/fuyuko-waifu-diffusion", |
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"AstraliteHeart/pony-diffusion", |
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"nousr/robo-diffusion", |
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"DGSpitzer/Cyberpunk-Anime-Diffusion", |
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"sd-dreambooth-library/herge-style" |
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] |
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prompt_prefixes = { |
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models[0]: "arcane style ", |
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models[1]: "archer style ", |
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models[2]: "elden ring style ", |
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models[3]: "spiderverse style ", |
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models[4]: "modern disney style ", |
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models[5]: "", |
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models[6]: "", |
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models[7]: "", |
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models[8]: "", |
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models[9]: "", |
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models[10]: "dgs illustration style ", |
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models[11]: "herge_style ", |
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} |
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current_model = models[0] |
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pipes = [] |
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vae = AutoencoderKL.from_pretrained(current_model, subfolder="vae", torch_dtype=torch.float16) |
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for model in models: |
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unet = UNet2DConditionModel.from_pretrained(model, subfolder="unet", torch_dtype=torch.float16) |
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pipe = StableDiffusionPipeline.from_pretrained(model, unet=unet, vae=vae, torch_dtype=torch.float16) |
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pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(model, unet=unet, vae=vae, torch_dtype=torch.float16) |
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pipes.append({"name":model, "pipeline":pipe, "pipeline_i2i":pipe_i2i}) |
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device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶" |
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def inference(model, img, strength, prompt, neg_prompt, guidance, steps, width, height, seed): |
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generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None |
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if img is not None: |
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return img_to_img(model, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator) |
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else: |
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return txt_to_img(model, prompt, neg_prompt, guidance, steps, width, height, generator) |
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def txt_to_img(model, prompt, neg_prompt, guidance, steps, width, height, generator=None): |
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global current_model |
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global pipe |
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if model != current_model: |
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current_model = model |
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pipe = pipe.to("cpu") |
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for pipe_dict in pipes: |
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if(pipe_dict["name"] == current_model): |
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pipe = pipe_dict["pipeline"] |
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if torch.cuda.is_available(): |
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pipe = pipe.to("cuda") |
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prompt = prompt_prefixes[current_model] + prompt |
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results = pipe( |
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prompt, |
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negative_prompt=neg_prompt, |
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num_inference_steps=int(steps), |
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guidance_scale=guidance, |
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width=width, |
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height=height, |
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generator=generator) |
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image = results.images[0] if not results.nsfw_content_detected[0] else Image.open("nsfw.png") |
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return image |
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def img_to_img(model, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator): |
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global current_model |
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global pipe |
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if model != current_model: |
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current_model = model |
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pipe = pipe.to("cpu") |
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for pipe_dict in pipes: |
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if(pipe_dict["name"] == current_model): |
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pipe = pipe_dict["pipeline_i2i"] |
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if torch.cuda.is_available(): |
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pipe = pipe.to("cuda") |
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prompt = prompt_prefixes[current_model] + prompt |
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ratio = min(height / img.height, width / img.width) |
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img = img.resize((int(img.width * ratio), int(img.height * ratio))) |
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results = pipe( |
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prompt, |
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negative_prompt=neg_prompt, |
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init_image=img, |
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num_inference_steps=int(steps), |
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strength=strength, |
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guidance_scale=guidance, |
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width=width, |
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height=height, |
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generator=generator) |
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image = results.images[0] if not results.nsfw_content_detected[0] else Image.open("nsfw.png") |
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return image |
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css = """ |
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<style> |
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.finetuned-diffusion-div { |
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text-align: center; |
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max-width: 700px; |
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margin: 0 auto; |
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} |
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.finetuned-diffusion-div div { |
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display: inline-flex; |
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align-items: center; |
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gap: 0.8rem; |
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font-size: 1.75rem; |
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} |
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.finetuned-diffusion-div div h1 { |
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font-weight: 900; |
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margin-bottom: 7px; |
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} |
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.finetuned-diffusion-div p { |
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margin-bottom: 10px; |
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font-size: 94%; |
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} |
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.finetuned-diffusion-div p a { |
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text-decoration: underline; |
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} |
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</style> |
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""" |
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with gr.Blocks(css=css) as demo: |
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gr.HTML( |
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""" |
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<div class="finetuned-diffusion-div"> |
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<div> |
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<h1>Finetuned Diffusion</h1> |
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</div> |
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<p> |
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Demo for multiple fine-tuned Stable Diffusion models, trained on different styles: <br> |
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<a href="https://huggingface.co/nitrosocke/Arcane-Diffusion">Arcane</a>, <a href="https://huggingface.co/nitrosocke/archer-diffusion">Archer</a>, <a href="https://huggingface.co/nitrosocke/elden-ring-diffusion">Elden Ring</a>, <a href="https://huggingface.co/nitrosocke/spider-verse-diffusion">Spiderverse</a>, <a href="https://huggingface.co/nitrosocke/modern-disney-diffusion">Modern Disney</a>, <a href="https://huggingface.co/hakurei/waifu-diffusion">Waifu</a>, <a href="https://huggingface.co/lambdalabs/sd-pokemon-diffusers">Pokemon</a>, <a href="https://huggingface.co/yuk/fuyuko-waifu-diffusion">Fuyuko Waifu</a>, <a href="https://huggingface.co/AstraliteHeart/pony-diffusion">Pony</a>, <a href="https://huggingface.co/sd-dreambooth-library/herge-style">Hergé (Tintin)</a>, <a href="https://huggingface.co/nousr/robo-diffusion">Robo</a>, <a href="https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion">Cyberpunk Anime</a> |
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</p> |
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</div> |
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""" |
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) |
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with gr.Row(): |
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with gr.Column(): |
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model = gr.Dropdown(label="Model", choices=models, value=models[0]) |
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prompt = gr.Textbox(label="Prompt", placeholder="Style prefix is applied automatically") |
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run = gr.Button(value="Run") |
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gr.Markdown(f"Running on: {device}") |
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with gr.Tab("Options"): |
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neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image") |
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guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15) |
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steps = gr.Slider(label="Steps", value=50, maximum=100, minimum=2) |
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width = gr.Slider(label="Width", value=512, maximum=1024, minimum=64) |
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height = gr.Slider(label="Height", value=512, maximum=1024, minimum=64) |
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seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1) |
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with gr.Tab("Image to image"): |
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image = gr.Image(label="Image", height=256, tool="editor", type="pil") |
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strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5) |
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with gr.Column(): |
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image_out = gr.Image(height=512) |
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inputs = [model, image, strength, prompt, neg_prompt, guidance, steps, width, height, seed] |
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prompt.submit(inference, inputs=inputs, outputs=image_out) |
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run.click(inference, inputs=inputs, outputs=image_out) |
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gr.Examples([ |
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[models[0], "jason bateman disassembling the demon core", 7.5, 50], |
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[models[3], "portrait of dwayne johnson", 7.0, 75], |
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[models[4], "portrait of a beautiful alyx vance half life", 10, 50], |
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[models[5], "Aloy from Horizon: Zero Dawn, half body portrait, smooth, detailed armor, beautiful face, illustration", 7, 45], |
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[models[4], "fantasy portrait painting, digital art", 4, 30], |
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], [model, prompt, guidance, steps], image_out, img_to_img, cache_examples=False) |
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gr.Markdown(''' |
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Models by [@nitrosocke](https://huggingface.co/nitrosocke), [@Helixngc7293](https://twitter.com/DGSpitzer) and others. ❤️<br> |
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Space by: [![Twitter Follow](https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social)](https://twitter.com/hahahahohohe) |
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![visitors](https://visitor-badge.glitch.me/badge?page_id=anzorq.finetuned_diffusion) |
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''') |
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demo.queue() |
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demo.launch() |