aiqbench analysis
This research introduces REPI, a novel training framework that injects pretrained visual encoder representations directly into diffusion transformers to accelerate the denoising process. By employing a scaffold-to-internalization strategy, the method achieves a massive 43.5x speedup in training efficiency compared to vanilla models while maintaining superior performance across various backbones. The approach demonstrates significant potential for optimizing large-scale generative AI workflows without compromising model quality.
Scaffold Then Internalize: Representation Injection for Diffusion Transformers
arXiv:2609.35292v1 Announce Type: cross Abstract: Recent representation alignment (REPA) methods accelerate diffusion transformer training by aligning projections of the transformer's hidden states with representations from pretrained visual encoders. In this work, we explore a reverse and complementary direction to REPA: rather than projecting diffusion representations into the encoder's space, we inject encoder representations into the diffusion transformer, allowing them to actively participate in the denoising process. To this end, we introduce \textit{REPresentation Injection} (REPI), a training framework based on a scaffold-to-internalization strategy, in which projected encoder representations initially serve as a temporary scaffold and are then progressively internalized by the diffusion transformer. REPI outperforms REPA across a wide range of backbones and is highly complementary to it: combining the two yields substantial gains over either alone. Notably, with only 160K training steps, REPI + REPA matches vanilla SiT trained for 7M steps, a speedup of over $43.5\times$. Code will be available at https://jeneveuxpas.github.io/REPI
Abstract:Recent representation alignment (REPA) methods accelerate diffusion transformer training by aligning projections of the transformer's hidden states with representations from pretrained visual encoders. In this work, we explore a reverse and complementary direction to REPA: rather than projecting diffusion representations into the encoder's space, we inject encoder representations into the diffusion transformer, allowing them to actively participate in the denoising process. To this end, we introduce \textit{REPresentation Injection} (REPI), a training framework based on a scaffold-to-internalization strategy, in which projected encoder representations initially serve as a temporary scaffold and are then progressively internalized by the diffusion transformer. REPI outperforms REPA across a wide range of backbones and is highly complementary to it: combining the two yields substantial gains over either alone. Notably, with only 160K training steps, REPI + REPA matches vanilla SiT trained for 7M steps, a speedup of over $43.5\times$. Code will be available at this https URL
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
arXiv:2609.35292 [cs.CV]
(or arXiv:2609.35292v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.35292 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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