No Channels Left Behind: Understanding Diffusion Acceleration through Residual Gates
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabThe paper studies why training acceleration methods such as REPA and dispersive regularization speed up diffusion transformers. It reports that these methods substantially reorganize the AdaLN-Zero residual gate, and that accelerated models are more sensitive to perturbations of this gate. The authors introduce a normalized participation ratio to measure gate channel organization, finding broader participation tracks better generation quality. Based on this, they propose a teacher-free radial objective using feature-norm regularization, which improves FID over dispersive loss on ImageNet 256x256 with SiT-B/2.
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- Primary sourceHugging Face Daily Papers
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The paper studies why training acceleration methods such as REPA and dispersive regularization speed up diffusion transformers. It reports that these methods substantially reorganize the AdaLN-Zero residual gate, and that accelerated models are more sensitive to perturbations of this gate. The authors introduce a normalized participation ratio to measure gate channel organization, finding broader participation tracks better generation quality. Based on this, they propose a teacher-free radial objective using feature-norm regularization, which improves FID over dispersive loss on ImageNet 256x256 with SiT-B/2.
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Evidence is reported for a single architecture and benchmark (SiT-B/2 on ImageNet 256x256); source claims not independently checked.
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Key findings from the stored brief
- REPA and dispersive regularization substantially reorganize AdaLN-Zero residual gates in diffusion transformers.
- Across REPA teachers, projectors, and radial targets, broader gate participation accompanies better generation quality.
- A teacher-free radial objective using feature-norm regularization improves FID over dispersive loss on ImageNet 256x256 with SiT-B/2.
Evidence is reported for a single architecture and benchmark (SiT-B/2 on ImageNet 256x256); source claims not independently checked.
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Why this may matter
It proposes residual-gate reorganization as a measurable axis of diffusion training acceleration, offering an alternative explanation to supervision or feature-geometry accounts and a simpler, teacher-free training objective.
Limits and open questions
Evidence is reported for a single architecture and benchmark (SiT-B/2 on ImageNet 256x256); source claims not independently checked.
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