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Representation Conditioning for Generation

Paper recorded by Signals 4 on 2026-10-14 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-14 on arXiv · recorded by Signals 4 on 2026-10-15

Category: cs.CV · 计算机视觉 · first seen 2026-10-15

Abstract

Representation-alignment methods effectively accelerate the diffusion transformer training. REPA, for example, uses a pretrained vision encoder to supervise an intermediate layer through an MLP projection head which is discarded after training. It aims to import semantics from the vision encoder to the diffusion backbone. While this objective is achieved at early layers by back-propagating the ali

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#265 most recent of 756 cs.CV papers we have recorded · ↑ newer: Can Learning to Answer Spatial Questions Improve Novel-View Generation · ↓ older: VITAL: From Visual Motion to Precise Action
Cite this page: Representation Conditioning for Generation: the #265 most recent of 756 cs.CV papers we have recorded (as of 2026-10-14). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/representation-conditioning-for-generation.html
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