arXiv:2605.27813v1 Announce Type: cross Abstract: Text-to-image diffusion models generate images through an iterative denoising process, so internal neural layers produce trajectories of activations rather than single static representations. Sparse autoencoders (SAEs) have recently been used to decompose diffusion activations into interpretable feature directions, but most approaches analyze activations at individual timesteps or condition on time rather than learning directly from full activation trajectories. In this work, we introduce residualized temporal SAEs for diffusion activation traj
The rapid advancement and widespread adoption of text-to-image diffusion models necessitate deeper interpretability to ensure reliability and guide further development.
Improved interpretability of diffusion models can unlock new capabilities, enhance safety, and accelerate research in generative AI, impacting various industries leveraging these models.
The ability to analyze full activation trajectories using residualized temporal sparse autoencoders provides a more nuanced understanding of how diffusion models generate images over time, moving beyond static representations.
- · AI researchers
- · Developers of generative AI applications
- · Industries using text-to-image generation
Researchers gain a more robust tool for debugging and understanding complex generative AI models.
The development of more controllable and steerable diffusion models becomes feasible, leading to more precise content generation.
Enhanced interpretability could reduce the 'black box' nature of advanced AI, potentially easing regulatory concerns and fostering greater public trust.
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Read at arXiv cs.LG