Papers › CleanDIFT: Diffusion Features without Noise
CleanDIFT: Diffusion Features without Noise
Nick Stracke, Stefan Andreas Baumann, Kolja Bauer, Frank Fundel, Björn Ommer
Internal features from large-scale pre-trained diffusion models have recently been established as powerful semantic descriptors for a wide range of downstream tasks. Works that use these features generally need to add noise to images before passing them through the model to obtain the semantic features, as the models do not offer the most useful features when given images with little to no noise. We show that this noise has a critical impact on the usefulness of these features that cannot be remedied by ensembling with different random noises. We address this issue by introducing a lightweight, unsupervised fine-tuning method that enables diffusion backbones to provide high-quality, noise-free semantic features. We show that these features readily outperform previous diffusion features by a wide margin in a wide variety of extraction setups and downstream tasks, offering better performance than even ensemble-based methods at a fraction of the cost.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic correspondence | SPair-71k | GeoAware-SC + CleanDIFT (Zero-Shot) | PCK | 70.0 | #5 of 22 | Archive leaderboard | report |
| Semantic correspondence | SPair-71k | SD+DINO + CleanDIFT (Zero-Shot) | PCK | 64.8 | #7 of 22 | Archive leaderboard | report |
| Semantic correspondence | SPair-71k | DIFT + CleanDIFT (Zero-Shot) | PCK | 61.4 | #10 of 22 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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