Emergence and Evolution of Interpretable Concepts in Diffusion Models

• URL: http://arxiv.org/abs/2504.15473v1
• Date: Mon, 21 Apr 2025 22:48:37 GMT
• Title: Emergence and Evolution of Interpretable Concepts in Diffusion Models
• Authors: Berk Tinaz, Zalan Fabian, Mahdi Soltanolkotabi,
• Abstract summary: We use Sparse Autoencoders (SAEs) to probe the inner workings of a popular text-to-image diffusion model.<n>We find that even before the first reverse diffusion step is completed, the final composition of the scene can be predicted surprisingly well.<n>We show that the discovered concepts have a causal effect on the model output and can be leveraged to steer the generative process.
• Score: 24.5360032541275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have become the go-to method for text-to-image generation, producing high-quality images from noise through a process called reverse diffusion. Understanding the dynamics of the reverse diffusion process is crucial in steering the generation and achieving high sample quality. However, the inner workings of diffusion models is still largely a mystery due to their black-box nature and complex, multi-step generation process. Mechanistic Interpretability (MI) techniques, such as Sparse Autoencoders (SAEs), aim at uncovering the operating principles of models through granular analysis of their internal representations. These MI techniques have been successful in understanding and steering the behavior of large language models at scale. However, the great potential of SAEs has not yet been applied toward gaining insight into the intricate generative process of diffusion models. In this work, we leverage the SAE framework to probe the inner workings of a popular text-to-image diffusion model, and uncover a variety of human-interpretable concepts in its activations. Interestingly, we find that even before the first reverse diffusion step is completed, the final composition of the scene can be predicted surprisingly well by looking at the spatial distribution of activated concepts. Moreover, going beyond correlational analysis, we show that the discovered concepts have a causal effect on the model output and can be leveraged to steer the generative process. We design intervention techniques aimed at manipulating image composition and style, and demonstrate that (1) in early stages of diffusion image composition can be effectively controlled, (2) in the middle stages of diffusion image composition is finalized, however stylistic interventions are effective, and (3) in the final stages of diffusion only minor textural details are subject to change.

Related papers

• Dual Diffusion for Unified Image Generation and Understanding [32.7554623473768]
  We propose a large-scale and fully end-to-end diffusion model for multi-modal understanding and generation.<n>We leverage a cross-modal maximum likelihood estimation framework that simultaneously trains the conditional likelihoods of both images and text jointly.<n>Our model attained competitive performance compared to recent unified image understanding and generation models.
  arXiv  Detail & Related papers  (2024-12-31T05:49:00Z)
• ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion Transformer [95.80384464922147]
  ACDiT is a blockwise Conditional Diffusion Transformer.<n>It offers a flexible between token-wise autoregression and full-sequence diffusion.<n>We show that ACDiT performs best among all autoregressive baselines on image and video generation tasks.
  arXiv  Detail & Related papers  (2024-12-10T18:13:20Z)
• Taming Diffusion Prior for Image Super-Resolution with Domain Shift SDEs [36.65594293655289]
  DoSSR is a Domain Shift diffusion-based SR model that capitalizes on the generative powers of pretrained diffusion models. At the core of our approach is a domain shift equation that integrates seamlessly with existing diffusion models. Our proposed method achieves state-of-the-art performance on synthetic and real-world datasets, while notably requiring only 5 sampling steps.
  arXiv  Detail & Related papers  (2024-09-26T12:16:11Z)
• Merging and Splitting Diffusion Paths for Semantically Coherent Panoramas [33.334956022229846]
  We propose the Merge-Attend-Diffuse operator, which can be plugged into different types of pretrained diffusion models used in a joint diffusion setting. Specifically, we merge the diffusion paths, reprogramming self- and cross-attention to operate on the aggregated latent space. Our method maintains compatibility with the input prompt and visual quality of the generated images while increasing their semantic coherence.
  arXiv  Detail & Related papers  (2024-08-28T09:22:32Z)
• LDFaceNet: Latent Diffusion-based Network for High-Fidelity Deepfake Generation [6.866014367868788]
  This paper proposes a novel facial swapping module, termed as LDFaceNet (Latent Diffusion based Face Swapping Network) It is based on a guided latent diffusion model that utilizes facial segmentation and facial recognition modules for a conditioned denoising process. The results of this study demonstrate that the proposed method can generate extremely realistic and coherent images.
  arXiv  Detail & Related papers  (2024-08-04T16:09:04Z)
• ReNoise: Real Image Inversion Through Iterative Noising [62.96073631599749]
  We introduce an inversion method with a high quality-to-operation ratio, enhancing reconstruction accuracy without increasing the number of operations. We evaluate the performance of our ReNoise technique using various sampling algorithms and models, including recent accelerated diffusion models.
  arXiv  Detail & Related papers  (2024-03-21T17:52:08Z)
• On the Multi-modal Vulnerability of Diffusion Models [56.08923332178462]
  We propose MMP-Attack to manipulate the generation results of diffusion models by appending a specific suffix to the original prompt. Our goal is to induce diffusion models to generate a specific object while simultaneously eliminating the original object.
  arXiv  Detail & Related papers  (2024-02-02T12:39:49Z)
• Towards Detailed Text-to-Motion Synthesis via Basic-to-Advanced Hierarchical Diffusion Model [60.27825196999742]
  We propose a novel Basic-to-Advanced Hierarchical Diffusion Model, named B2A-HDM, to collaboratively exploit low-dimensional and high-dimensional diffusion models for detailed motion synthesis. Specifically, the basic diffusion model in low-dimensional latent space provides the intermediate denoising result that is consistent with the textual description. The advanced diffusion model in high-dimensional latent space focuses on the following detail-enhancing denoising process.
  arXiv  Detail & Related papers  (2023-12-18T06:30:39Z)
• Diffusion Models for Image Restoration and Enhancement -- A Comprehensive Survey [96.99328714941657]
  We present a comprehensive review of recent diffusion model-based methods on image restoration. We classify and emphasize the innovative designs using diffusion models for both IR and blind/real-world IR. We propose five potential and challenging directions for the future research of diffusion model-based IR.
  arXiv  Detail & Related papers  (2023-08-18T08:40:38Z)
• Diffusion Models Generate Images Like Painters: an Analytical Theory of Outline First, Details Later [1.8416014644193066]
  We observe that the reverse diffusion process that underlies image generation has the following properties. Individual trajectories tend to be low-dimensional and resemble 2D rotations' We find that this solution accurately describes the initial phase of image generation for pretrained models.
  arXiv  Detail & Related papers  (2023-03-04T20:08:57Z)
• SinDiffusion: Learning a Diffusion Model from a Single Natural Image [159.4285444680301]
  We present SinDiffusion, leveraging denoising diffusion models to capture internal distribution of patches from a single natural image. It is based on two core designs. First, SinDiffusion is trained with a single model at a single scale instead of multiple models with progressive growing of scales. Second, we identify that a patch-level receptive field of the diffusion network is crucial and effective for capturing the image's patch statistics.
  arXiv  Detail & Related papers  (2022-11-22T18:00:03Z)
• Diffusion Models in Vision: A Survey [73.10116197883303]
  A diffusion model is a deep generative model that is based on two stages, a forward diffusion stage and a reverse diffusion stage.<n> Diffusion models are widely appreciated for the quality and diversity of the generated samples, despite their known computational burdens.
  arXiv  Detail & Related papers  (2022-09-10T22:00:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.