Title: Unveiling Concept Attribution in Diffusion Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: generative models

diffusion models

interpretability

concept erasure
Abstract: Diffusion models have shown remarkable abilities in generating realistic and high-quality images from text prompts. However, a trained model remains largely black-box; little do we know about the roles of its components in exhibiting a concept such as objects or styles. Recent works employ causal tracing to localize knowledge-storing layers in generative models without showing how other layers contribute to the target concept. In this work, we approach diffusion models' interpretability problem from a more general perspective and pose a question: \textit{``How do model components work jointly to demonstrate knowledge?''}. To answer this question, we decompose diffusion models using component attribution, systematically unveiling the importance of each component (specifically the model parameter) in generating a concept. The proposed framework, called \textbf{C}omponent \textbf{A}ttribution for \textbf{D}iffusion Model (CAD), discovers the localization of concept-inducing (positive) components, while interestingly uncovers another type of components that contribute negatively to generating a concept, which is missing in the previous knowledge localization work. Based on this holistic understanding of diffusion models, we present and empirically evaluate one utility of component attribution in controlling the generation process. Specifically, we introduce two fast, inference-time model editing algorithms, CAD-Erase and CAD-Amplify; in particular, CAD-Erase enables erasure and CAD-Amplify allows amplification of a generated concept by ablating the positive and negative components, respectively, while retaining knowledge of other concepts. Extensive experimental results validate the significance of both positive and negative components pinpointed by our framework, demonstrating the potential of providing a complete view of interpreting generative models.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose a novel framework, Component Attribution for Diffusion Model (CAD), to interpret how individual model parameters in diffusion models contribute to generating specific concepts (e.g., objects, styles, or explicit content). By leveraging a first-order approximation to compute attribution scores, the authors identify both positive (concept-inducing) and negative (concept-suppressing) components. They further introduce two inference-time editing algorithms, CAD-Erase and CAD-Amplify, to respectively remove or enhance specific concepts in generated images.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \n\n1. Focus on Parameter-Level Analysis: The paper's emphasis on fine-grained, parameter-level attribution is a step beyond layer-based analyses common in prior work. This granular approach could potentially offer deeper insights into diffusion model mechanics.\n\n2. Practical Applications: The proposed CAD-Erase and CAD-Amplify algorithms provide practical tools for concept manipulation, with potential applications in content moderation (e.g., erasing nudity) and enhancing underrepresented concepts.\n\nQUESTIONS:\nWhile the paper introduces parameter-level attribution and negative components, these contributions are incremental rather than transformative. The framework builds on existing methods like causal tracing [2, 3, 27] and linear counterfactual estimation [34], with the primary novelty being the identification of negative components. However, this finding is underexplored, and the overall approach does not sufficiently distinguish itself from prior interpretability work in generative models. The editing algorithms, while practical, are similar in spirit to existing methods like ConceptPrune and UCE, reducing the paper’s originality.\n\nWhile the paper claims novelty in identifying negative components and parameter-level attribution, the core idea of attributing model components builds heavily on prior work like Shah et al. [34] and knowledge localization studies [2, 3, 27]. \n\nThe main score and formulation used (Eq. 4) is not novel [21,27] as acknowledged by the authors.\n \nThe discovery of negative components, while interesting, lacks sufficient exploration to establish its significance or differentiate it from existing interpretability frameworks. The paper does not convincingly demonstrate that CAD provides a transformative advance over established methods.\n\nWeak Theoretical Foundation: The reliance on a first-order approximation for attribution scores (Section 4.2) is empirically validated (Figure 2) but lacks theoretical grounding. The paper explicitly notes the absence of theoretical results (Page 13), and the justification for the approximation’s generalizability is insufficient. Without a deeper analysis of its limitations, the method’s reliability across diverse models and concepts remains questionable.\n\nIncomplete Analysis of Negative Components: The identification of negative components is a key claim, but the paper provides minimal insight into their nature, origin, or role in diffusion models. For example, it is unclear whether these components are training artifacts or intentional suppressors, and their distribution across model layers is not explored. This limits the theoretical and practical impact of the finding.\n\nNarrow Experimental Scope: The evaluation is limited to Stable Diffusion-1.4, which raises concerns about generalizability to other diffusion models or newer variants. The paper does not discuss challenges or adaptations needed for applying CAD to other architectures, undermining its broader applicability.\n\nConcept Entanglement Issues: The paper notes that erasing one concept (e.g., Rembrandt’s style) can affect similar concepts (e.g., Caravaggio’s style, Page 8), indicating entanglement in parameter contributions. This issue is acknowledged but not systematically analyzed or addressed, limiting the practical utility of CAD-Erase for complex or overlapping concepts.\n\nLack of Statistical Rigor: The paper fixes seeds for experiments (Page 15) but does not report error bars, confidence intervals, or statistical significance tests for key metrics (e.g., accuracy, FID, LPIPS). This weakens the reliability of comparisons with baselines and the robustness of the claims.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-16"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Component Attribution for Diffusion Model (CAD), an efficient framework for interpreting how parameters in diffusion models generate concepts. The study reveals that concepts are governed by localized \"positive components\" that induce them, and uniquely, \"negative components\" that suppress them. Based on these findings, the paper presents two lightweight, inference-time model editing algorithms: CAD-Erase, which removes concepts by ablating positive components, and CAD-Amplify, which enhances concepts by ablating negative ones. Extensive experiments validate that this approach can effectively and robustly erase or amplify objects, styles, and explicit content while preserving other knowledge.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- State-of-the-Art Performance: Achieves SOTA or highly competitive results across diverse benchmarks.\n- Demonstrated Robustness to Attacks: The paper shows that CAD is more robust to white-box attacks than many competing methods.\n\nWeaknesses:\n- No Overhead Analysis: Lacks analysis of the practical computational cost (time, memory) for editing and its effect on the final model's inference speed.\n- Uncertain Generalizability: Experiments are confined to Stable Diffusion 1.4(mostly) and 2.1, leaving its effectiveness on other architectures (like SDXL, SD3) unproven.\n- Crude Intervention Method: The approach of ablating parameters by setting them to zero is a blunt instrument. While effective, it might not be optimal.\n\nQUESTIONS:\n1.On Cost: Please quantify the practical overhead (inference speed, memory) compared to baseline.\n\n2.On Generalizability: How does the \"localization hypothesis\" generalize to newer architectures like SD3 or FLUX? We suggest adding theoretical reasoning or a preliminary analysis.\n\n3.On Optimization: Could the method of directly setting ablating parameters to zero be optimized other than finetuning?\n\nLIMITATIONS:\nyes, authors addressed the limitations and potential negative societal impact of their work", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes **Component Attribution for Diffusion Models (CAD)**, a method for understanding and analyzing generative diffusion models. The core approach identifies parameters within the model that have a positive or negative influence on specific concepts during generation. The authors posit that only a small subset of parameters are relevant to a given concept. Based on this attribution analysis, they introduce two corresponding algorithms designed to **suppress or enhance** the generation of the target concept. The fundamental attribution mechanism involves **perturbing a parameter \\(w\\) (e.g., setting it towards zero)** and measuring the resulting impact on the presence of concept \\(c\\) in the output image \\(x\\). The effect on concept \\(c\\) is quantified using a **discriminative model**.\n\nSTRENGTHS AND WEAKNESSES:\n### **Strengths**\n1.  **Engaging Topic:** The analysis of internal mechanisms in Diffusion Models is a highly relevant and important research direction.\n2.  **Solid Background:** The related works section is well-surveyed, establishing a solid context for the addressed problem.\n3.  **Logical Progression:** The method development follows a logically sound, theoretically grounded progression from attribution analysis to practical application (suppression/enhancement).\n\n### **Weaknesses**\n1.  **Unclear Illustrative Figure:** Figure 1 does not effectively illustrate the *method* itself; it primarily presents a *conclusion* (attribution scores), offering limited help in understanding the CAD procedure.\n2.  **Insufficient and Underwhelming Experiments:**\n    *   Experimental validation is notably limited.\n    *   Results lack robustness: Table 1 fails to highlight superior metrics effectively (e.g., via bolding).\n    *   Evaluation is primarily conducted on **low-resolution images**.\n    *   Model Scope Limitation: Only Stable Diffusion 1.4 (SD v1.4) is tested. The conclusions may not generalize to **more advanced and widely used versions like SD2.x or SD3**, which is a significant omission.\n    *   Poor Empirical Performance: Qualitative results are weak. For instance, Figure 3 (rows 1-2) shows that concept \"erase\" significantly alters background information, indicating the method lacks practical advantage and suffers from unintended side effects.\n3.  **Limited Methodological Novelty:** The core attribution technique (perturbing parameters and measuring output change via a classifier) is **fundamentally simple** and bears strong resemblance to well-established gradient/importance-based attribution methods developed over the past decade (e.g., CAM and its numerous derivatives). The novelty contribution is therefore **significantly lacking**.\n4.  **Misleading Terminology:**\n    *   Referring to individual *parameters* as \"Components\" is inaccurate and potentially confusing. \"Component\" typically implies a composed module or functional unit within a network, not a single scalar weight.\n    *   Labeling a *class token* or a *prompt term* as a \"concept\" is inappropriate. In the field, \"concept\" generally denotes a higher-level, often abstract, semantic entity (e.g., learned by adapters like LoRA), distinct from the direct input tokens used here.\n\nQUESTIONS:\nNone\n\nLIMITATIONS:\nThe authors did not provide such analyses.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper describes the use of a linear counterfactual predictor to attibute concept-related capabilities to sets of diffusion model components. It identifies components with activations magnitudes have both positive and negative relationships with the generation of images containing the concept. The component outputs are then modulated to amplify or attenuate the concept.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\nThe approach is highly effective based the experimental evaluation, which is adequate in my opinion.\n\nThe results provide additional evidence that \"knowledge is localized\", although this observation has been made in prior work on attribution and on fairness.\n\nThe authors claim that this is the first work commenting on the existence of and identifying components whose activations are inversely related to the expression of concepts. A brief literature survey supports this claim. This is an important finding that others in the field should  be aware of. \n\nThe presented attribution method requries only a single forward and backward pass in contrast with approaches generating data to learn $\\alpha_c$.\n\nWeaknesses\n\nThe writing quality is a bit poorer than typical for a NeurIPS submission.\n\nDesign decisions are not very well justified, relative to alternatives. For example, the authors make clear that the first-order Taylor series expansion enables a efficient estimation but do not comment on why their chosen approach is likely to do a better job of this than alternatives. More explanation of the reasons for design decisions would improve the paper.\n\nQUESTIONS:\nIf the contributions were revised to focus on the specific novel contributions of the paper, what would they look like?\n\nDo you claim to be the first to discover that a very small percent of DNN components have nearly all the influence over the expression of a concept?\n\nIs the key novel idea for the counterfactual predictor to use the Taylor series expansion to enable efficient prediction, or is there another primary novel contribution related to the predictor?\n\nDo you claim to be the first to find that there are some components for which higher activations repress a concept?\n\nAre there other novel ideas or discoveries claimed?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}