Beyond Surface Statistics: Scene Representations in a Latent Diffusion Model (2306.05720v2)

Abstract: Latent diffusion models (LDMs) exhibit an impressive ability to produce realistic images, yet the inner workings of these models remain mysterious. Even when trained purely on images without explicit depth information, they typically output coherent pictures of 3D scenes. In this work, we investigate a basic interpretability question: does an LDM create and use an internal representation of simple scene geometry? Using linear probes, we find evidence that the internal activations of the LDM encode linear representations of both 3D depth data and a salient-object / background distinction. These representations appear surprisingly early in the denoising process$-$well before a human can easily make sense of the noisy images. Intervention experiments further indicate these representations play a causal role in image synthesis, and may be used for simple high-level editing of an LDM's output. Project page: https://yc015.github.io/scene-representation-diffusion-model/

• The paper demonstrates that internal activations in Stable Diffusion encode linear representations of 3D depth and salient object cues.
• Using linear probing, the study reports a Dice coefficient of 0.85 for saliency and an RMSE of 0.47 for depth, confirming strong geometric encoding.
• Intervention experiments confirm that these internal depth representations causally influence the final image synthesis outcome.

Exploring Scene Representations in Latent Diffusion Models

The paper "Beyond Surface Statistics: Scene Representations in a Latent Diffusion Model" by Yida Chen, Fernanda Viégas, and Martin Wattenberg, investigates the internal representations within Latent Diffusion Models (LDMs) to determine whether these models encode simple 3D scene geometries. The research primarily uses Stable Diffusion, an open-source LDM, to understand if these models extract and use depth information and salient-object background distinctions during the process of image synthesis. The methodology, results, and implications of this paper provide valuable insights into the interpretability and potential applications of LDMs in the field of AI and computer graphics.

Methodological Approach

The authors employ linear probing techniques to evaluate whether Stable Diffusion's internal activations encode 3D depth data and salient-object background distinctions. Linear probing involves training a lightweight classifier on the activations of a neural network to infer specific properties, thus providing a lens into the internal representations of the network.

To this end, the authors generate a synthetic dataset comprising 1000 images using Stable Diffusion, paired with relative depth maps and salient-object labels estimated via MiDaS and the TRACER model, respectively. Probing classifiers are trained on intermediate activations from the self-attention layers of the LDM across various denoising steps to predict per-pixel depth and saliency.

Results and Observations

Significant findings of this paper include:

• Linear Representations of Depth and Saliencies: The results show that Stable Diffusion encodes strong linear representations of both continuous depth and salient-object distinctions. The probing classifiers achieved a Dice coefficient of 0.85 for salient-object segmentation and RMSE of 0.47 for depth estimation by the final denoising steps.
• Early Emergence of Depth Representations: A key observation is that these depth and saliency representations emerge very early in the denoising process. Notably, the internal representations developed well before the images became human-interpretable.
• Causal Role of Depth Information: Through intervention experiments, where internal representations of depth and salient objects were manipulated, the paper demonstrated that these representations have a causal effect on the final output, confirming their integral role in the image synthesis process.

Implications and Speculations for Future Research

The findings have several critical implications and open potential avenues for future developments:

1. Understanding Model Behavior: The paper extends the understanding of LDMs, suggesting that these models do not merely rely on surface correlations but form internal geometric representations akin to a rudimentary 3D model.
2. Advancing Interpretability in Neural Networks: The demonstrated causal link between internal activations and output can inform strategies to enhance model transparency and control, potentially leading to more interpretable AI systems.
3. Applications in Graphics and AI: The ability to manipulate internal representations offers practical applications in graphics, such as fine-tuning specific attributes of generated images (e.g., changing the depth or position of objects within synthesized scenes).
4. Expanding Interpretability Research: Future research could explore internal representations of other scene attributes like texture and lighting. Additionally, probing the semantic aspects of the scene can provide insights akin to rediscovering traditional computer graphics pipelines within deep learning models.

Conclusively, this paper contributes significant evidence that Stable Diffusion, and by extension LDMs, encode and utilize internal scene geometry representations for generating coherent images. This insight enriches the conversation around the depth and breadth of neural model learning beyond surface-level statistics and highlights new opportunities for enhancing and leveraging these models in practical applications.