LayerSync: Self-aligning Intermediate Layers

We propose LayerSync, a domain-agnostic approach for improving the generation quality and the training efficiency of diffusion models. Prior studies have highlighted the connection between the quality of generation and the representations learned by diffusion models, showing that external guidance on model intermediate representations accelerates training. Building on the observation that representation quality varies across diffusion model layers, we show that the most semantically rich representations can act as an intrinsic guidance for weaker ones, reducing the need for external supervision. Our approach, LayerSync, is a self-sufficient, plug-and-play regularizer term with no overhead on diffusion model training and generalizes beyond the visual domain to other modalities. LayerSync requires no pretrained models nor additional data. We extensively evaluate the method on image generation and demonstrate its applicability to other domains such as audio, video, and motion generation. We show that it consistently improves the generation quality and the training efficiency. For example, we speed up the training of flow-based transformer by over 8.75 on ImageNet dataset and improved the generation quality by 23.6%. Figure 1: LayerSync improves training efficiency and generation quality via internal representation alignment. Denoising generative models, such as diffusion (Ho et al., 2020; Song et al., 2020; Song & Ermon, 2019) and flow matching models (Lipman et al., 2023), have demonstrated remarkable success in However, this success comes at a significant computation cost. Thus, a new promising line of research has emerged to improve the training efficiency of these models by improving the models' intermediate representations (Y u et al., 2024; Wang et al., 2025; Wang & He, 2025). It has been shown that the quality of a diffusion model's intermediate representations is intrinsically linked to its generative performance. As a result, explicitly guiding these representations accelerates training and improves generation quality (Y u et al., 2024). Building on this insight, the most dominant approach (Y u et al., 2024; Wang et al., 2025) has been to leverage powerful external guidance from large pre-trained models, by aligning the internal features of a diffusion model with those of high-capacity vision models like DINOv2 (Oquab et al., 2023) or vision-language models (VLMs) like Qwen2-VL (Wang et al., 2024).