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Update app.py
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app.py
CHANGED
@@ -1,4 +1,5 @@
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from diffusers import StableDiffusionPipeline
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import gradio as gr
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import torch
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@@ -37,13 +38,20 @@ if torch.cuda.is_available():
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device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
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def inference(model, prompt, guidance, steps):
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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 = StableDiffusionPipeline.from_pretrained(current_model, torch_dtype=torch.float16)
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if torch.cuda.is_available():
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pipe = pipe.to("cuda")
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@@ -51,30 +59,64 @@ def inference(model, prompt, guidance, steps):
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image = pipe(prompt, num_inference_steps=int(steps), guidance_scale=guidance, width=512, height=512).images[0]
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return image
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css = """
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<style>
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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
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<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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>
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<h1 style="font-weight: 900; margin-bottom: 7px;">
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Finetuned Diffusion
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</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/IfanSnek/JohnDiffusion">John</a>, <a href="https://huggingface.co/nousr/robo-diffusion">Robo</a>.
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</p>
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@@ -86,6 +128,7 @@ with gr.Blocks(css=css) as demo:
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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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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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run = gr.Button(value="Run")
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with gr.Column():
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image_out = gr.Image(height=512)
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run.click(inference, inputs=[model, prompt, guidance, steps], 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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], [prompt, guidance, steps], image_out,
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gr.HTML('''
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<div>
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<p>Model by <a href="https://huggingface.co/nitrosocke" target="_blank">@nitrosocke</a> ❤️</p>
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@@ -113,4 +156,4 @@ with gr.Blocks(css=css) as demo:
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''')
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demo.queue()
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demo.launch()
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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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device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
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def inference(model, prompt, img, guidance, steps):
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if img is not None:
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return img_inference(model, prompt, img, guidance, steps)
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else:
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return text_inference(model, prompt, guidance, steps)
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def text_inference(model, prompt, guidance, steps):
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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 = StableDiffusionPipeline.from_pretrained(current_model, torch_dtype=torch.float16)
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if torch.cuda.is_available():
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pipe = pipe.to("cuda")
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image = pipe(prompt, num_inference_steps=int(steps), guidance_scale=guidance, width=512, height=512).images[0]
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return image
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def img_inference(model, prompt, img, guidance, steps):
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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 = StableDiffusionImg2ImgPipeline.from_pretrained(current_model, torch_dtype=torch.float16)
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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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img.resize((512, 512))
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image = pipe(
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prompt,
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init_image=img,
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num_inference_steps=int(steps),
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strength=0.75,
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guidance_scale=guidance,
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width=512,
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height=512).images[0]
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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/IfanSnek/JohnDiffusion">John</a>, <a href="https://huggingface.co/nousr/robo-diffusion">Robo</a>.
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</p>
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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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img = gr.Image(label="img2img (optional)", type="pil", height=256, tool="editor")
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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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run = gr.Button(value="Run")
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with gr.Column():
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image_out = gr.Image(height=512)
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run.click(inference, inputs=[model, prompt, img, guidance, steps], 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, text_inference, cache_examples=torch.cuda.is_available())
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gr.HTML('''
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<div>
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<p>Model by <a href="https://huggingface.co/nitrosocke" target="_blank">@nitrosocke</a> ❤️</p>
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''')
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demo.queue()
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demo.launch(debug=True)
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