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[2510.12581] LayerSync: Self-aligning Intermediate Layers
Summary
LayerSync introduces a novel approach to enhance diffusion models by self-aligning intermediate layers, improving training efficiency and generation quality across various domains.
Why It Matters
This research addresses the critical challenge of optimizing diffusion models, which are increasingly used in generative tasks. By eliminating the need for external supervision and pretrained models, LayerSync offers a more efficient and versatile solution, potentially transforming practices in machine learning and computer vision.
Key Takeaways
- LayerSync improves training efficiency and generation quality for diffusion models.
- The method uses self-guidance from intermediate representations, reducing reliance on external supervision.
- Demonstrated significant speedup (over 8.75x) in training flow-based transformers on ImageNet.
- Applicable beyond visual domains, including audio, video, and motion generation.
- No additional data or pretrained models are required for implementation.
Computer Science > Computer Vision and Pattern Recognition arXiv:2510.12581 (cs) [Submitted on 14 Oct 2025 (v1), last revised 18 Feb 2026 (this version, v2)] Title:LayerSync: Self-aligning Intermediate Layers Authors:Yasaman Haghighi, Bastien van Delft, Mariam Hassan, Alexandre Alahi View a PDF of the paper titled LayerSync: Self-aligning Intermediate Layers, by Yasaman Haghighi and 3 other authors View PDF HTML (experimental) Abstract: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. We reconceptualize this paradigm by regularizing diffusion models with their own intermediate representations. 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...