Probability-Flow Distillation: Distribution Matching in Parameter Space
Score distillation methods use pretrained diffusion models as priors for optimizing parameters through differentiable forward models, most notably in text-to-3D generation. Yet the distribution they induce over those parameters is not well understood. Observing that existing distillation methods reduce to one of three: Score Distillation Sampling (SDS), Score Distillation via Inversion (SDI), and Variational Score Distillation (VSD), we extend the particle variational inference view of VSD to the other two. We show that SDS collapses onto the modes of the target, while SDI converges to a contracted version of it, and explain why SDI needs a negative classifier-free guidance scale. Next, w...
| Authors | Rohith Ramanan, A. N. Rajagopalan |
|---|---|
| Primary category | cs.CV |
| Published | 9 May 2026 |
| Last updated at source | 18 September 2026 |
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