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arXiv Machine Learning

No Channels Left Behind: Understanding Diffusion Acceleration through Residual Gates

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.

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arXiv Computer Vision
Oct 22

Policy-Mask: Budget-Aware Adaptation of Diffusion Features to Discriminative Tasks

Policy‑Mask is a budget‑aware policy that jointly selects a timestep, a diffusion block, and a subset of channels from a frozen diffusion model to optimize downstream discriminative tasks. By normalizing channel scores and training on the downstream objective, it outperforms fixed‑extraction baselines on segmentation, classification, and semantic correspondence, achieving state‑of‑the‑art results on ADE20K, CIFAR‑100, and SPair‑71k. The method also extends to diffusion transformers by mapping U‑Net channels to image‑token hidden dimensions.

By Ivan Listopadov, Maksim Nakhodnov, Aibek Alanov
llmsdiffusioncomputer-vision
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arXiv Computer Vision
Aug 26

On-Policy Self-Distillation in Diffusion Models

The paper introduces DiffusionOPSD, an on‑policy self‑distillation framework that transforms image‑level reinforcement learning rewards into explicit targets for intermediate denoising predictions in diffusion models. By generating trajectories with a frozen behavior policy and constructing bounded positive and negative targets around query states, the method trains a policy to fit these targets before updating the behavior policy via an exponential moving average. Experiments on SD 3.5‑M and Z‑Image‑Turbo show that DiffusionOPSD achieves the best held‑out scores in 19 of 20 reward‑matched settings, outperforms the strongest competitor by up to 44 % and cuts GPU‑hour usage by 40–63 % compared to DiffusionNFT.

By Wei Zhou, Xiongwei Zhu, Lingdong Kong, Bo Chen, Lei Zhang, Yongyuan Liang, Xiaoxia Hou, Ye Tian, Xian Sun, Yingshuo Wang, Linfeng Li, Shengqiong Wu, Leigang Qu, Feng Li, Wei Liu, Julian McAuley, Tat-Seng Chua
diffusionreinforcement-learningefficiencysafety
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arXiv Computer Vision
Oct 16

FocusOPD: Structure-Guided On-Policy Distillation for Diffusion Models

FocusOPD introduces a structure‑guided on‑policy distillation method that adds token‑wise directional feature alignment to deep Transformer layers during early denoising steps. This approach addresses the mismatch between teacher and student representations that persists after standard OPD distillation. Experiments on rectified‑flow backbones show that FocusOPD improves performance on GenEval, PickScore, and LAION‑Aesthetic benchmarks, and ablations confirm that selective deep and early supervision is more effective than full‑network alignment.

By Xinrui Lin, Shumin Wang, Suqi Song, Fan Li, Yanyong Zhang
llmsdiffusionefficiencybenchmarkssafety
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