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Updated: Jun 4, 2026
06:55
Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
Published on: September 26, 2016
Emergence and Evolution of Interpretable Concepts in Diffusion Models
Berk Tinaz1, Zalan Fabian1, Mahdi Soltanolkotabi1
1Dept. of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA.
Advances in Neural Information Processing Systems
|June 3, 2026
Summary
Sparse Autoencoders (SAEs) reveal interpretable concepts within diffusion models. Early activation patterns predict final image composition, enabling control over image generation stages.
Area of Science:
- Artificial Intelligence
- Computer Vision
- Machine Learning
Background:
- Diffusion models excel at text-to-image generation but remain black boxes.
- Mechanistic interpretability, successful in LLMs, is underexplored in diffusion models.
Purpose of the Study:
- To apply Sparse Autoencoders (SAEs) for understanding diffusion model internals.
- To investigate the interpretability of diffusion model activations and control generation.
Main Methods:
- Leveraged the SAE framework to probe a text-to-image diffusion model.
- Analyzed spatial distributions of activated concepts in model activations.
- Designed intervention techniques to manipulate image composition and style.
Main Results:
- Discovered human-interpretable concepts within diffusion model activations.
- Showed final scene composition can be predicted early in the diffusion process.
- Demonstrated effective control over composition in early stages, style in middle stages, and texture in final stages.
Conclusions:
- SAEs provide valuable insights into diffusion model mechanisms.
- Image composition is established early, while style and texture are refined later.
- This work opens avenues for interpretable and controllable diffusion-based image generation.
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