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Update app.py

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  1. app.py +6 -1
app.py CHANGED
@@ -290,6 +290,11 @@ with gr.Blocks(css=css) as demo:
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  CycleDiffusion (<a href="https://arxiv.org/abs/2210.05559">πŸ“„ Paper link</a> | <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/cycle_diffusion">🧨 Pipeline doc</a>) is an image-to-image translation method that supports stochastic samplers for diffusion models. <br>
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  We also support the combination of CycleDiffusion and Cross Attention Control (CAC | <a href="https://arxiv.org/abs/2208.01626">πŸ“„ Paper link</a>). CAC is a technique to transfer the attention map from the source prompt to the target prompt. <br>
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  </p>
 
 
 
 
 
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  <p>
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  <b>How to use:</b> <br>
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  1. Upload an image. <br>
@@ -304,7 +309,7 @@ with gr.Blocks(css=css) as demo:
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  <b>Notes:</b> <br>
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  1. CycleDiffusion is likely to fail when drastic changes are intended (e.g., changing a large black car to red). <br>
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  2. The value of strength can be set larger when CAC is used. <br>
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- 3. If CAC type is "Replace", the source and target prompts should differ in only one token; otherwise, an error will be raised. <br>
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  4. If CAC type is "Refine", the source prompt be a subsequence of the target prompt; otherwise, an error will be raised. <br>
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  </p>
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  <p>You can skip the queue using Colab: <a href="https://colab.research.google.com/gist/ChenWu98/0aa4fe7be80f6b45d3d055df9f14353a/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://colab.research.google.com/assets/colab-badge.svg"></a></p>
 
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  CycleDiffusion (<a href="https://arxiv.org/abs/2210.05559">πŸ“„ Paper link</a> | <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/cycle_diffusion">🧨 Pipeline doc</a>) is an image-to-image translation method that supports stochastic samplers for diffusion models. <br>
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  We also support the combination of CycleDiffusion and Cross Attention Control (CAC | <a href="https://arxiv.org/abs/2208.01626">πŸ“„ Paper link</a>). CAC is a technique to transfer the attention map from the source prompt to the target prompt. <br>
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  </p>
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+ <p>
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+ <b>Quick start</b>: <br>
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+ 1. Click one row of Examples at the end of this page. It will fill all inputs needed. <br>
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+ 2. Click the "Edit" button. <br>
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+ </p>
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  <p>
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  <b>How to use:</b> <br>
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  1. Upload an image. <br>
 
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  <b>Notes:</b> <br>
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  1. CycleDiffusion is likely to fail when drastic changes are intended (e.g., changing a large black car to red). <br>
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  2. The value of strength can be set larger when CAC is used. <br>
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+ 3. If CAC type is "Replace", the source and target prompts should differ in only one token; otherwise, an error will be raised. This is why we deliberately make some grammar mistakes in Examples.<br>
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  4. If CAC type is "Refine", the source prompt be a subsequence of the target prompt; otherwise, an error will be raised. <br>
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  </p>
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  <p>You can skip the queue using Colab: <a href="https://colab.research.google.com/gist/ChenWu98/0aa4fe7be80f6b45d3d055df9f14353a/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://colab.research.google.com/assets/colab-badge.svg"></a></p>