Dale Decatur
@daledecatur.bsky.social
CS PhD student @ UChicago
https://ddecatur.github.io/
https://ddecatur.github.io/
This work was completed in collaboration with @thibaultgroueix.bsky.social, Yifan Wang, @ranahanocka.bsky.social, @vovakim.bsky.social, and @gadelha.bsky.social. Check out our #ICCV2025 poster #153 today during Poster Session #4 from 2:45-4:45 HST!
Paper: arxiv.org/abs/2508.21032
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Paper: arxiv.org/abs/2508.21032
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Reusing Computation in Text-to-Image Diffusion for Efficient Generation of Image Sets
Text-to-image diffusion models enable high-quality image generation but are computationally expensive. While prior work optimizes per-inference efficiency, we explore an orthogonal approach: reducing ...
arxiv.org
October 22, 2025 at 8:23 PM
This work was completed in collaboration with @thibaultgroueix.bsky.social, Yifan Wang, @ranahanocka.bsky.social, @vovakim.bsky.social, and @gadelha.bsky.social. Check out our #ICCV2025 poster #153 today during Poster Session #4 from 2:45-4:45 HST!
Paper: arxiv.org/abs/2508.21032
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Paper: arxiv.org/abs/2508.21032
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While our method can be applied to general purpose image generation, our method achieves the most dramatic savings (saves >75% vs standard diffusion) when examples are structurally similar. Some applications of this are style variation, subject variation, and virtual try-on.
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October 22, 2025 at 8:23 PM
While our method can be applied to general purpose image generation, our method achieves the most dramatic savings (saves >75% vs standard diffusion) when examples are structurally similar. Some applications of this are style variation, subject variation, and virtual try-on.
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Interestingly, we observe that models trained using a text-to-image prior (bottom) generate high frequency details much later in the denoising process than without (top). This makes them ideal for sharing compute with our approach!
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October 22, 2025 at 8:23 PM
Interestingly, we observe that models trained using a text-to-image prior (bottom) generate high frequency details much later in the denoising process than without (top). This makes them ideal for sharing compute with our approach!
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Compared to standard diffusion (left), our method (right) generates images of comparable quality using a fraction of the compute. Exact savings depend on the prompt set, but we show that our method can save up to 74% of the total denoising steps required for standard diffusion!
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October 22, 2025 at 8:23 PM
Compared to standard diffusion (left), our method (right) generates images of comparable quality using a fraction of the compute. Exact savings depend on the prompt set, but we show that our method can save up to 74% of the total denoising steps required for standard diffusion!
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We construct a tree by hierarchically clustering prompts. We then map each denoising step k to a height in this tree, using the mean embedding of each cluster at this height as the condition. The steps gradually diverge from shared embeddings to individual prompt embeddings.
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October 22, 2025 at 8:23 PM
We construct a tree by hierarchically clustering prompts. We then map each denoising step k to a height in this tree, using the mean embedding of each cluster at this height as the condition. The steps gradually diverge from shared embeddings to individual prompt embeddings.
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We take advantage of the coarse-to-fine nature of diffusion generation: early timesteps generate low frequency structure and later timesteps produce high frequency details. Leveraging this, we share intermediate denoising results at early steps between similar examples.
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October 22, 2025 at 8:23 PM
We take advantage of the coarse-to-fine nature of diffusion generation: early timesteps generate low frequency structure and later timesteps produce high frequency details. Leveraging this, we share intermediate denoising results at early steps between similar examples.
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