Methods › Computer Vision › Generative Training › ILVR
Iterative Latent Variable Refinement
ILVR
Introduced by Jooyoung Choi et al. in ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Iterative Latent Variable Refinement, or ILVR, is a method to guide the generative process in denoising diffusion probabilistic models (DDPMs) to generate high-quality images based on a given reference image. ILVR conditions the generation process in well-performing unconditional DDPM. Each transition in the generation process is refined utilizing a given reference image. By matching each latent variable, ILVR ensures the given condition in each transition thus enables sampling from a conditional distribution. Thus, ILVR generates high-quality images sharing desired semantics.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models 6 Aug 2021 · 1 repository · arXiv:2108.02938Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
Tasks archive 2025-07-28
4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Denoising | 1 |
| Image Generation | 1 |
| Translation | 1 |
| Unconditional Image Generation | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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