$\textit{Revelio}$: Interpreting and leveraging semantic information in diffusion models

• URL: http://arxiv.org/abs/2411.16725v1
• Date: Sat, 23 Nov 2024 03:54:22 GMT
• Title: $\textit{Revelio}$: Interpreting and leveraging semantic information in diffusion models
• Authors: Dahye Kim, Xavier Thomas, Deepti Ghadiyaram,
• Abstract summary: We study $textithow$ rich visual semantic information is represented within various layers and denoising timesteps of different diffusion architectures. We uncover monosemantic interpretable features by leveraging k-sparse autoencoders (k-SAE) We substantiate our mechanistic interpretations via transfer learning using light-weight classifiers on off-the-shelf diffusion models' features.
• Score: 10.68914376295842
• License:
• Abstract: We study $\textit{how}$ rich visual semantic information is represented within various layers and denoising timesteps of different diffusion architectures. We uncover monosemantic interpretable features by leveraging k-sparse autoencoders (k-SAE). We substantiate our mechanistic interpretations via transfer learning using light-weight classifiers on off-the-shelf diffusion models' features. On $4$ datasets, we demonstrate the effectiveness of diffusion features for representation learning. We provide in-depth analysis of how different diffusion architectures, pre-training datasets, and language model conditioning impacts visual representation granularity, inductive biases, and transfer learning capabilities. Our work is a critical step towards deepening interpretability of black-box diffusion models. Code and visualizations available at: https://github.com/revelio-diffusion/revelio

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