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arXiv Machine Learning By Junha Park, Jaemyung Yu, Jaehui Hwang, Jong-Seok Lee, Dongyoon Han

No Channels Left Behind: Understanding Diffusion Acceleration through Residual Gates

Read the original on arXiv Machine Learning →

The paper investigates how diffusion transformer acceleration methods such as REPA and dispersive regularization affect the AdaLN‑Zero residual gate α, which controls conditioned updates in the residual stream. It finds that these methods reorganize α, making accelerated models more sensitive to its perturbations and aligning gate geometry with semantic class relationships. Introducing a normalized participation ratio (nPR) to measure gate channel organization, the authors show that broader gate participation correlates with higher generation quality and propose a teacher‑free radial objective that encourages this broader participation, improving FID on ImageNet.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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