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Update app.py
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app.py
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@@ -1,6 +1,4 @@
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from diffusers import AutoencoderKL, UNet2DConditionModel
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from diffusers import StableDiffusionPipeline
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from diffusers import StableDiffusionImg2ImgPipeline
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import gradio as gr
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import torch
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from PIL import Image
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Model("Custom model", "", ""),
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Model("Arcane", "nitrosocke/Arcane-Diffusion", "arcane style "),
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Model("Archer", "nitrosocke/archer-diffusion", "archer style "),
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]
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last_mode = "txt2img"
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current_model = models[1]
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current_model_path = current_model.path
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# pipe = None
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if is_colab:
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pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16)
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@@ -87,7 +84,6 @@ def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, g
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if is_colab or current_model == models[0]:
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pipe = StableDiffusionPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16)
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else:
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# pipe = pipe.to("cpu")
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pipe.to("cpu")
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pipe = current_model.pipe_t2i
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@@ -119,7 +115,6 @@ def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, w
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if is_colab or current_model == models[0]:
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pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16)
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else:
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# pipe = pipe.to("cpu")
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pipe.to("cpu")
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pipe = current_model.pipe_i2i
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@@ -192,7 +187,7 @@ with gr.Blocks(css=css) as demo:
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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">
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</p>
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<p>Don't want to wait in queue? <a href="https://colab.research.google.com/gist/qunash/42112fb104509c24fd3aa6d1c11dd6e0/copy-of-fine-tuned-diffusion-gradio.ipynb"><img data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" src="https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667"></a></p>
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Running on <b>{device}</b>{(" in a <b>Google Colab</b>." if is_colab else "")}
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@@ -205,7 +200,9 @@ with gr.Blocks(css=css) as demo:
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with gr.Column(scale=55):
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with gr.Group():
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model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name)
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with gr.Row():
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prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="Enter prompt. Style applied automatically").style(container=False)
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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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model_name.change(lambda x: gr.update(visible = x == models[0].name), inputs=model_name, outputs=
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custom_model_path.change(custom_model_changed, inputs=custom_model_path, outputs=None)
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# n_images.change(lambda n: gr.Gallery().style(grid=[2 if n > 1 else 1], height="auto"), inputs=n_images, outputs=gallery)
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prompt.submit(inference, inputs=inputs, outputs=image_out)
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generate.click(inference, inputs=inputs, outputs=image_out)
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# ex.dataset.headers = [""]
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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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from diffusers import AutoencoderKL, UNet2DConditionModel, StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
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import gradio as gr
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import torch
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from PIL import Image
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Model("Custom model", "", ""),
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Model("Arcane", "nitrosocke/Arcane-Diffusion", "arcane style "),
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Model("Archer", "nitrosocke/archer-diffusion", "archer style "),
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Model("Elden Ring", "nitrosocke/elden-ring-diffusion", "elden ring style "),
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Model("Spider-Verse", "nitrosocke/spider-verse-diffusion", "spiderverse style "),
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Model("Modern Disney", "nitrosocke/mo-di-diffusion", "modern disney style "),
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Model("Classic Disney", "nitrosocke/classic-anim-diffusion", ""),
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Model("Waifu", "hakurei/waifu-diffusion", ""),
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Model("Pokémon", "lambdalabs/sd-pokemon-diffusers", ""),
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Model("Pony Diffusion", "AstraliteHeart/pony-diffusion", ""),
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Model("Robo Diffusion", "nousr/robo-diffusion", ""),
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Model("Cyberpunk Anime", "DGSpitzer/Cyberpunk-Anime-Diffusion", "dgs illustration style "),
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Model("Tron Legacy", "dallinmackay/Tron-Legacy-diffusion", "trnlgcy ")
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]
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last_mode = "txt2img"
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current_model = models[1]
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current_model_path = current_model.path
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if is_colab:
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pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16)
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if is_colab or current_model == models[0]:
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pipe = StableDiffusionPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16)
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else:
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pipe.to("cpu")
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pipe = current_model.pipe_t2i
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if is_colab or current_model == models[0]:
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pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16)
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else:
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pipe.to("cpu")
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pipe = current_model.pipe_i2i
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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">Spider-Verse</a>, <a href="https://huggingface.co/nitrosocke/modern-disney-diffusion">Modern Disney</a>, <a href="https://huggingface.co/nitrosocke/classic-anim-diffusion">Classic Disney</a>, <a href="https://huggingface.co/hakurei/waifu-diffusion">Waifu</a>, <a href="https://huggingface.co/lambdalabs/sd-pokemon-diffusers">Pokémon</a>, <a href="https://huggingface.co/AstraliteHeart/pony-diffusion">Pony Diffusion</a>, <a href="https://huggingface.co/nousr/robo-diffusion">Robo Diffusion</a>, <a href="https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion">Cyberpunk Anime</a>, <a href="https://huggingface.co/dallinmackay/Tron-Legacy-diffusion">Tron Legacy</a> + any other custom Diffusers 🧨 SD model hosted on HuggingFace 🤗.
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</p>
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<p>Don't want to wait in queue? <a href="https://colab.research.google.com/gist/qunash/42112fb104509c24fd3aa6d1c11dd6e0/copy-of-fine-tuned-diffusion-gradio.ipynb"><img data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" src="https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667"></a></p>
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Running on <b>{device}</b>{(" in a <b>Google Colab</b>." if is_colab else "")}
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with gr.Column(scale=55):
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with gr.Group():
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model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name)
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with gr.Box(visible=False) as custom_model_group:
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custom_model_path = gr.Textbox(label="Custom model path", placeholder="Path to model, e.g. nitrosocke/Arcane-Diffusion", interactive=True)
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gr.HTML("<div><font size='2'>Custom models have to be downloaded first, so give it some time.</font></div>")
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with gr.Row():
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prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="Enter prompt. Style applied automatically").style(container=False)
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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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model_name.change(lambda x: gr.update(visible = x == models[0].name), inputs=model_name, outputs=custom_model_group)
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custom_model_path.change(custom_model_changed, inputs=custom_model_path, outputs=None)
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# n_images.change(lambda n: gr.Gallery().style(grid=[2 if n > 1 else 1], height="auto"), inputs=n_images, outputs=gallery)
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prompt.submit(inference, inputs=inputs, outputs=image_out)
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generate.click(inference, inputs=inputs, outputs=image_out)
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ex = gr.Examples([
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[models[1].name, "jason bateman disassembling the demon core", 7.5, 50],
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[models[4].name, "portrait of dwayne johnson", 7.0, 75],
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[models[5].name, "portrait of a beautiful alyx vance half life", 10, 50],
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[models[6].name, "Aloy from Horizon: Zero Dawn, half body portrait, smooth, detailed armor, beautiful face, illustration", 7.0, 45],
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[models[5].name, "fantasy portrait painting, digital art", 4.0, 30],
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], [model_name, prompt, guidance, steps, seed], image_out, inference, 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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