No Other Representation Component Is Needed: Diffusion Transformers Can Provide Representation Guidance by Themselves
📌 核心贡献
提出 SelfRepresentation Alignment (SRA)：无需外部表征编码器，直接利用 Diffusion Transformer 自身内部表征提供引导信号。方法简单——将浅层（高噪声条件）的隐表征对齐到深层（低噪声条件）的隐表征，仅在训练阶段生效，性能可媲美依赖外部预训练编码器的方法。ICLR 2026 收录。
📖 摘要
Recent studies have demonstrated that learning a meaningful internal representation can accelerate generative training. However, existing approaches necessitate to either introduce an off-the-shelf external representation task or rely on a large-scale, pre-trained external representation encoder to provide representation guidance during the training process. In this study, we posit that the unique discriminative process inherent to diffusion transformers enables them to offer such guidance without requiring external representation components. We propose SelfRepresentation Alignment (SRA), a simple yet effective method that obtains representation guidance using the internal representations of learned diffusion transformer. SRA aligns the latent representation of the diffusion transformer in the earlier layer conditioned on higher noise to that in the later layer conditioned on lower noise to progressively enhance the overall representation learning during only the training process. Experimental results indicate that applying SRA to DiTs and SiTs yields consistent performance improvements, and largely outperforms approaches relying on auxiliary representation task. Our approach achieves performance comparable to methods that are dependent on an external pre-trained representation encoder, which demonstrates the feasibility of acceleration with representation alignment in diffusion transformers themselves.