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c9b0019
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Parent(s):
d461b4f
change styles
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app.py
CHANGED
@@ -17,16 +17,6 @@ scheduler = DPMSolverMultistepScheduler(
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lower_order_final=True,
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def is_google_colab():
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try:
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import google.colab
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return True
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except:
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return False
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is_colab = is_google_colab()
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class Model:
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def __init__(self, name, path, prefix):
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self.name = name
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@@ -37,19 +27,9 @@ class Model:
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models = [
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Model("Stable-Diffusion-v1.4", "CompVis/stable-diffusion-v1-4", "The 1.4 version of official stable-diffusion"),
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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", "classic disney style "),
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# Model("Waifu", "hakurei/waifu-diffusion", ""),
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# Model("PokΓ©mon", "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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@@ -173,6 +153,7 @@ css = """
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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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@@ -182,7 +163,9 @@ css = """
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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-
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}
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.finetuned-diffusion-div p {
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margin-bottom: 10px;
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@@ -198,6 +181,11 @@ css = """
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#gallery {
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min-height: 20rem;
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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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f"""
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<div class="finetuned-diffusion-div">
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<div>
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<h1>
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</div>
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<br>
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<p>
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<a href="https://github.com/LuChengTHU/dpm-solver">DPM-Solver</a> (Neurips 2022 Oral) is a fast high-order solver customized for diffusion ODEs, which can generate high-quality samples by diffusion models within only 10-25 steps. DPM-Solver has an analytical formulation and is very easy to use for all types of Gaussian diffusion models, and includes <a href="https://arxiv.org/abs/2010.02502">DDIM</a> as a first-order special case.
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</p>
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<p>
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We use <a href="https://github.com/huggingface/diffusers">Diffusers</a>
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</p>
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<br>
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<p>
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</p>
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</div>
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"""
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)
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# TODO: the colab version is wrong.
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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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# </p>
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# TODO: do not support the custom model
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with gr.Row():
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with gr.Column(scale=55):
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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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# 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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inputs = [model_name, prompt, guidance, steps, width, height, seed, image, strength, neg_prompt]
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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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# TODO: the docs here are wrong.
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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[1+2].name, "jason bateman disassembling the demon core", 7.5, 50],
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# # [models[4+2].name, "portrait of dwayne johnson", 7.0, 75],
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# # [models[5+2].name, "portrait of a beautiful alyx vance half life", 10, 50],
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# # [models[6+2].name, "Aloy from Horizon: Zero Dawn, half body portrait, smooth, detailed armor, beautiful face, illustration", 7.0, 45],
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# # [models[5+2].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), [@haruu1367](https://twitter.com/haruu1367), [@Helixngc7293](https://twitter.com/DGSpitzer) and others.
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Space by
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''')
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demo.launch(debug=is_colab, share=is_colab)
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lower_order_final=True,
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)
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class Model:
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def __init__(self, name, path, prefix):
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self.name = name
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models = [
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Model("Stable-Diffusion-v1.4", "CompVis/stable-diffusion-v1-4", "The 1.4 version of official stable-diffusion"),
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Model("Waifu", "hakurei/waifu-diffusion", "anime style"),
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Model("PokΓ©mon", "lambdalabs/sd-pokemon-diffusers", "pokemon style"),
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Model("Modern Disney", "nitrosocke/mo-di-diffusion", "modern disney style"),
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]
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last_mode = "txt2img"
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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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font-family: 'IBM Plex Sans', sans-serif;
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}
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.finetuned-diffusion-div div {
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display: inline-flex;
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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-top: 15px;
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margin-bottom: 15px;
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text-align: center;
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}
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.finetuned-diffusion-div p {
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margin-bottom: 10px;
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#gallery {
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min-height: 20rem;
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}
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.container {
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max-width: 1000px;
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margin: auto;
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padding-top: 1.5rem;
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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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f"""
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<div class="finetuned-diffusion-div">
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<div>
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<h1>Stable-Diffusion with DPM-Solver (fastest sampler for diffusion models) </h1>
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</div>
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<br>
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<p>
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Acknowledgement: Hardware resources of this demo are supported by HuggingFace π€ . Many thanks for the help!
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</p>
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<br>
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<p>
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This is a demo of sampling by DPM-Solver with several fine-tuned Stable Diffusion models, trained on different styles: <br>
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<a href="https://huggingface.co/CompVis/stable-diffusion-v1-4">Stable-Diffusion-v1.4</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">PokΓ©mon</a>.
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</p>
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<br>
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<p>
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<a href="https://github.com/LuChengTHU/dpm-solver">DPM-Solver</a> (Neurips 2022 Oral) is a fast high-order solver customized for diffusion ODEs, which can generate high-quality samples by diffusion models within only 10-25 steps. DPM-Solver has an analytical formulation and is very easy to use for all types of Gaussian diffusion models, and includes <a href="https://arxiv.org/abs/2010.02502">DDIM</a> as a first-order special case.
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</p>
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<p>
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We use <a href="https://github.com/huggingface/diffusers">Diffusers</a> 𧨠to implement this demo, which currently supports the multistep DPM-Solver scheduler. For more details of DPM-Solver with Diffusers, check <a href="https://github.com/huggingface/diffusers/pull/1132">this pull request</a>.
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</p>
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<br>
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<p>
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Currently, the default sampler of stable-diffusion is <a href="https://arxiv.org/abs/2202.09778">PNDM</a>, which needs 50 steps to generate high-quality samples. However, DPM-Solver can generate high-quality samples within only <span style="font-weight: bold;">20-25</span> steps, and for some samples even within <span style="font-weight: bold;">10-15</span> steps.
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</p>
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<br>
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<p>
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Running on <b>{device}</b>
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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(scale=55):
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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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inputs = [model_name, prompt, guidance, steps, width, height, seed, image, strength, neg_prompt]
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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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gr.Markdown('''
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Models by [@nitrosocke](https://huggingface.co/nitrosocke), [@haruu1367](https://twitter.com/haruu1367), [@Helixngc7293](https://twitter.com/DGSpitzer) and others. Most of the code of this demo are copied from [@anzorq's fintuned-diffusion](https://huggingface.co/spaces/anzorq/finetuned_diffusion/tree/main) β€οΈ<br>
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Space by [Cheng Lu](https://github.com/LuChengTHU). [](https://twitter.com/ChengLu05671218)
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''')
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demo.queue(concurrency_count=1)
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demo.launch(debug=False, share=False)
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