TIDE : Temporal-Aware Sparse Autoencoders for Interpretable Diffusion Transformers in Image Generation

• URL: http://arxiv.org/abs/2503.07050v1
• Date: Mon, 10 Mar 2025 08:35:51 GMT
• Title: TIDE : Temporal-Aware Sparse Autoencoders for Interpretable Diffusion Transformers in Image Generation
• Authors: Victor Shea-Jay Huang, Le Zhuo, Yi Xin, Zhaokai Wang, Peng Gao, Hongsheng Li,
• Abstract summary: TIDE (Temporal-aware Sparse Autoencoders for Interpretable Diffusion transformErs) is a novel framework that enhances temporal reconstruction within DiT activation layers across denoising steps.<n>TIDE employs Sparse Autoencoders (SAEs) with a sparse bottleneck layer to extract interpretable and hierarchical features.<n>Our approach achieves state-of-the-art reconstruction performance, with a mean squared error (MSE) of 1e-3 and a cosine similarity of 0.97.
• Score: 34.73820805875123
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion Transformers (DiTs) are a powerful yet underexplored class of generative models compared to U-Net-based diffusion models. To bridge this gap, we introduce TIDE (Temporal-aware Sparse Autoencoders for Interpretable Diffusion transformErs), a novel framework that enhances temporal reconstruction within DiT activation layers across denoising steps. TIDE employs Sparse Autoencoders (SAEs) with a sparse bottleneck layer to extract interpretable and hierarchical features, revealing that diffusion models inherently learn hierarchical features at multiple levels (e.g., 3D, semantic, class) during generative pre-training. Our approach achieves state-of-the-art reconstruction performance, with a mean squared error (MSE) of 1e-3 and a cosine similarity of 0.97, demonstrating superior accuracy in capturing activation dynamics along the denoising trajectory. Beyond interpretability, we showcase TIDE's potential in downstream applications such as sparse activation-guided image editing and style transfer, enabling improved controllability for generative systems. By providing a comprehensive training and evaluation protocol tailored for DiTs, TIDE contributes to developing more interpretable, transparent, and trustworthy generative models.

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