MaskAlign: Token-Subset Representation Alignment for Efficient Diffusion Training
Representation alignment with pretrained vision models has recently shown strong potential for accelerating diffusion transformer training. By aligning intermediate diffusion features with clean-image representations from self-supervised vision encoders, existing methods improve convergence and generation quality. However, such alignment also introduces a non-trivial constraint: diffusion models operate on noisy inputs whose usable information varies across timesteps, while the reference features are extracted from clean images. In this paper, we revisit this mismatch from a token-level perspective. We find that, under full-token representation alignment, tokens with large alignment-gradi...
| Authors | Lianyu Pang, Tianlin Pan, Cheng Da et al. |
|---|---|
| Primary category | cs.CV |
| Published | 7 June 2026 |
| Last updated at source | 7 June 2026 |
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