Training acceleration methods for diffusion transformers, such as REPA and dispersive regularization, are commonly explained by additional supervision, spatial-structure priors, or improved feature ge
Training acceleration in diffusion transformers is driven by reorganization of AdaLN-Zero residual gates, and a simple radial objective promoting broader gate participation improves generation quality without auxiliary teachers.
Training acceleration methods for diffusion transformers, such as REPA and dispersive regularization, are commonly explained by additional supervision, spatial-structure priors, or improved feature geometry. We revisit this view and uncover a striking internal change: the AdaLN-Zero residual gate α, which directly controls how conditioned updates enter the residual stream, is substantially reorganized by these methods. This observation motivates our hypothesis that acceleration arises, at least in part, from reshaping the residual gating pathway rather than solely from the feature structures explicitly imposed by auxiliary objectives. Supporting this view, accelerated models are markedly…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
No Channels Left Behind: Understanding Diffusion Acceleration through Residual Gates was first seen on Hugging Face · Daily papers on Nov 2, 2026. Since then it has appeared in 14 checks on 2 different days. Its best position was #14 of 30, reached on Nov 2, 09:28 UTC. It dropped off the list after Nov 3, 11:28 UTC.
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