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<a href="https://martin-ev.github.io/vgoq" class="work-item" target="_blank">
<div class="work-info">
<h5>Visual Grounding for Object Questions</h5>
<p>Introduces Visual Grounding for Object Questions (VGOQ), a new task for grounding visual evidence or context that supports answering general questions about objects, beyond directly visible elements.</p>
<span class="work-venue">To appear in CVPR 2026</span>
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<a href="https://ivrl.github.io/covariance-mismatch/" class="work-item" target="_blank">
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<h5>Covariance Mismatch in Diffusion Models</h5>
<p>Investigates the covariance mismatch between noise and data in diffusion models and its impact on image generation.</p>
<span class="work-venue">Preprint 2024</span>
</div>
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</a>
<a href="https://ivrl.github.io/signal-leak-bias/" class="work-item" target="_blank">
<div class="work-info">
<h5>Exploiting the Signal-Leak Bias in Diffusion Models</h5>
<p>Examines and leverages the signal-leak bias in diffusion models for improved image generation.</p>
<span class="work-venue">WACV 2024</span>
</div>
<i class="fas fa-external-link-alt"></i>
</a>
<a href="https://ivrl.github.io/diffusion-in-style/" class="work-item" target="_blank">
<div class="work-info">
<h5>Diffusion in Style</h5>
<p>Customizes Stable Diffusion's output style by adapting the initial noise distribution, making style adaptation more sample-efficient and faster.</p>
<span class="work-venue">ICCV 2023</span>
</div>
<i class="fas fa-external-link-alt"></i>
</a>
<a href="https://ivrl.github.io/vetim/" class="work-item" target="_blank">
<div class="work-info">
<h5>VETIM: Expanding the Vocabulary of Text-to-Image Models only with Text</h5>
<p>Expands text-to-image models' vocabulary by learning new token embeddings from textual descriptions alone, without requiring sample images.</p>
<span class="work-venue">BMVC 2023</span>
</div>
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</a>
<a href="https://ivrl.github.io/ComicsDepth/" class="work-item" target="_blank">
<div class="work-info">
<h5>Estimating Image Depth in the Comics Domain</h5>
<p>Estimates depth in comic book images by converting them to natural images and filtering out text to improve accuracy.</p>
<span class="work-venue">WACV 2022</span>
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<a href="https://arxiv.org/abs/2006.02333" class="work-item" target="_blank">
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<h5>Scene Relighting with Illumination Estimation in the Latent Space</h5>
<p>Transfers lighting conditions between images by estimating and manipulating illumination in the latent space of an encoder-decoder network.</p>
<span class="work-venue">arXiv 2020</span>
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<h5>More works from Image and Visual Representation Lab (IVRL)</h5>
<p>Also check more works from our labmates at the Image and Visual Representation Lab (IVRL) at EPFL.</p>
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<h1 class="title is-1 publication-title">Diffusion in Style</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://martin-ev.github.io/" target="_blank">Martin Nicolas Everaert</a> <sup>1</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=KDiTxBQAAAAJ" target="_blank">Marco Bocchio</a> <sup>2</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=84FopNgAAAAJ" target="_blank">Sami Arpa</a> <sup>2</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=EX3OYP4AAAAJ" target="_blank">Sabine Süsstrunk</a> <sup>1</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=lc2HaZwAAAAJ" target="_blank">Radhakrishna Achanta</a> <sup>1</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">
<sup>1</sup> EPFL, Switzerland, <br><sup>2</sup> Largo.ai, Lausanne, Switzerland
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><a href="https://iccv2023.thecvf.com/" target="_blank">ICCV 2023</a> + <a href="https://patents.google.com/patent/WO2024251351A1/" target="_blank">Patent filed PCT/EP2023/065063</a></span>
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class="external-link button is-normal is-rounded is-dark">
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<span>ICCV 2023 open access</span>
</a>
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<h2 class="title is-3">Abstract</h2>
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<p>
We present Diffusion in Style, a simple method to adapt Stable Diffusion to any desired style, using only
a small set of target images. It is based on the key observation that the style of the images generated by
Stable Diffusion is tied to the initial latent tensor. Not adapting this initial latent tensor to the
style makes fine-tuning slow, expensive, and impractical, especially when only a few target style images
are available. In contrast, fine-tuning is much easier if this initial latent tensor is also adapted. Our
Diffusion in Style is orders of magnitude more sample-efficient and faster. It also generates more
pleasing images than existing approaches, as shown qualitatively and with quantitative comparisons.
</p>
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</div>
</div>
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We perform style adaptation of Stable Diffusion by fine-tuning the denoising U-Net with a style-specific noise distribution instead of the default
white noise distribution.
The parameters of the style-specific noise distribution are derived by computing statistics from a small set of images of the desired style. The same set of target-style images is also used for fine-tuning.
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At inference time, we sample the initial latent tensor from the style-specific noise distribution and use the fine-tuned denoising U‑Net to iteratively denoise it.
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<pre id="bibtex-code"><code>@InProceedings{Everaert_2023_ICCV,
title = {{D}iffusion in {S}tyle},
author = {Everaert, Martin Nicolas and Bocchio, Marco and Arpa, Sami and S\"usstrunk, Sabine and Achanta, Radhakrishna},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2023},
pages = {2251-2261}
}</code></pre>
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