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<!DOCTYPE html>
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<head>
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<meta name="description"
content="Personalizing Diffusion Models with Iterative Feedback">
<meta name="keywords" content="FABRIC, Diffusion Models, Iterative Feedback">
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<title>FABRIC 🎨 </title>
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<h1 class="title is-1 publication-title">FABRIC 🎨<br />
<p class="title is-3 publication-title">Personalizing Diffusion Models with Iterative Feedback </p>
</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="#">Dimitri Von Rütte</a>,</span>
<span class="author-block">
<a href="https://elisabettafedele.github.io/">Elisabetta Fedele</a>,
</span>
<!-- <br /> -->
<span class="author-block">
<a href="#">Jonathan Thomm</a>,
</span>
<span class="author-block">
<a href="https://lu-wo.github.io/">Lukas Wolf</a>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block" style="margin-right: 1em;">ETH Zürich, Switzerland</span>
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<span>Paper</span>
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<a href="https://arxiv.org/abs/2307.10159"
class="external-link button is-normal is-rounded is-dark">
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<span>arXiv</span>
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class="external-link button is-normal is-rounded is-dark">
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<i class="fab fa-github"></i>
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<span>Code</span>
</a>
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</section>
<section class="hero teaser">
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<img src="static/images/teaser.jpeg" class="center"/>
<br><br>
<h2 class="subtitle has-text-centered">
<strong>FABRIC</strong> is training-free approach that conditions the diffusion process on a set of feedback images, applicable to a wide range of popular diffusion models.
</h2>
</div>
</div>
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<h2 class="title is-3">Abstract</h2>
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<p>
In this study, we investigate the integration of iterative human feedback into diffusion-based text-to-image models to enhance user experience and output quality. We introduce FABRIC (Feedback via Attention-Based Reference Image Conditioning), a training-free approach that conditions the diffusion process on a set of feedback images, applicable to a wide range of popular diffusion models. We propose a comprehensive evaluation methodology for a rigorous assessment of our approach and demonstrate that generation results improve over multiple rounds of iterative feedback, optimizing user preferences. This work has potential applications in personalized content creation and customization, contributing to the advancement of text-to-image generation research.
</p>
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<h2 class="title is-3">Demos</h2>
<br>
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<p>
Try out the FABRIC demos on <a href="https://huggingface.co/spaces/dvruette/fabric">Gradio</a> or <a href="https://colab.research.google.com/drive/1rWZ4jQHMvjc-l7xYAssa_OUOaAx3XDQT?usp=sharing">Google Colab</a>!
</p>
</div>
<a href="https://huggingface.co/spaces/dvruette/fabric"><img src="static/images/gradio_demo.jpeg" width="700" class="center"/></a>
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</section>
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<h2 class="title is-3">Results</h2>
<br>
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<p>
Coming soon.
</p>
</div>
</div>
</div>
</section> -->
<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
<pre><code>@misc{vonrütte2023fabric,
title={FABRIC: Personalizing Diffusion Models with Iterative Feedback},
author={Dimitri von Rütte and Elisabetta Fedele and Jonathan Thomm and Lukas Wolf},
year={2023},
eprint={2307.10159},
archivePrefix={arXiv},
primaryClass={cs.CV}
}</code></pre>
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It borrows the source code of <a href="https://github.com/nerfies/nerfies.github.io">this website</a>.
We would like to thank Utkarsh Sinha and Keunhong Park.
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