Title: One-Step is Enough: Sparse Autoencoders for Text-to-Image Diffusion Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: sparse autoencoders

SAE

unet

diffusion

mechanistic interpretability

text to image interpretability

activation engineering
Abstract: For large language models (LLMs), sparse autoencoders (SAEs) have been shown to decompose intermediate representations that often are not interpretable directly into sparse sums of interpretable features, facilitating better control and subsequent analysis. However, similar analyses and approaches have been lacking for text-to-image models. We investigate the possibility of using SAEs to learn interpretable features for SDXL Turbo, a few-step text-to-image diffusion model. To this end, we train SAEs on the updates performed by transformer blocks within SDXL Turbo's denoising U-net in its 1-step setting. Interestingly, we find that they generalize to 4-step SDXL Turbo and even to the multi-step SDXL base model (i.e., a different model) without additional training. In addition, we show that their learned features are interpretable, causally influence the generation process, and reveal specialization among the blocks. We do so by creating RIEBench, a representation-based image editing benchmark, for editing images while they are generated by turning on and off individual SAE features. This allows us to track which transformer blocks' features are the most impactful depending on the edit category. Our work is the first investigation of SAEs for interpretability in text-to-image diffusion models and our results establish SAEs as a promising approach for understanding and manipulating the internal mechanisms of text-to-image models.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe proposed method provides an exploration over the learned features for SDXL and distilled diffusion/flow-matching models, where authors provide experiments mainly for SDXL-Turbo and demonstrate the extension on FLUX-schnell. Authors apply SAEs (sparse autoencoders) to the transformer layers of the models and provide analyses on the discovered representations over the features learned by the targeted transformer blocks. Following the SAE training, experiments analyze the explained variance by the targeted blocks across the timesteps. In addition to the variance analysis, authors also provide experiments on the significance of the discovered features on different types of edits, by utilizing a feature interpolation mechanism across the activations on the transformer blocks. Provided experiments show that features in specified blocks are responsible for certain generation patterns and utilized consistently for specified types of edits from PIE-Bench.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n- The paper provides the first study on the application of SAEs for the explainability of features learned by diffusion models and their distilled counterparts.\n- The discovered features are investigated in terms of different types of editing tasks and their visualizations are provided to demonstrate their qualitative effectiveness (see Figure 1).\n- The paper introduces a feature injection scheme to effectively use them for editing tasks\n\n**Weaknesses**\n- The organization of the paper should be improved. The narrative of the paper seems disconnected, which makes it hard to understand.As an example for this, the paper first explains the SAEs trained and the blocks that are selected for it. Then, in the analysis section, the example of feature transport is given. The connection here should be improved for writing, in the current form it feels disconnected (like saying that this is used to transport SAE features in the experiments etc.)\n- The readability of the figures should be improved significantly. As an example for this, Figure 5 reports the impact on the editing performance. However, with the labels for SAE features the plot is almost unreadable. The authors shuld consider revising accordingly.\n- In lines 117 and 118, the authors say that they select 4 layers for their analysis. However, there is no motivation on how they were selected. The ,methodology should either refer to previous studies on the investihgated blocks or provide a convincing reason on why these methods are investigated.\n- While the authors provide editing results with discovered representations, their quality is uncertain. For example, we have no idea on if the representations discovered are repetitve and how unique they are. Furthermore, to show the effectiveness of such representations, the authors should provide qualitative editing results for the categories that the edits are reported.\n- To demonstrate if the discovered representations are beneficial for image editing task, a comparison with existing editing methods is essential (for distilled methods, a comparison with [1] can be provided). Conversely, if edits that cannot be performed with the existing methods can be performed with the proposed method, then this should be stated explicitly.\n- As we have no explicit information on which types of representations are discovered, the limitations of the method is ambiguous. An analysis on the discovered representations will improve the significance of the methodology.\n- With the current state of the paper, it appears that the authors get an existing interpretability method (SAEs) and plug it into an existing method. While this is okay for the explanation of the representations, it makes the novelty of the paper questionable. The authors should provide details on how the adaptation is accomplished (like the selection of the features, how it differs from applying SAEs to LLMs) to further demonstrate the contribution of the paper (or explain their analysis in detail).\n\n[1] Deutch et. al. \"TurboEdit: Text-Based Image Editing Using Few-Step Diffusion Models\", https://arxiv.org/abs/2408.00735\n\nQUESTIONS:\n- How do the authors decide on which transformer blocks to investigate?\n- Are the discovered representations be labeled with text instructions, or are they more ambiguous?\n- In the set of representations discovered, are there any overlaps, or does all of the representations discovered represent a distinct concepts?\n\nThese are the questions that I found to be beneficial for the clarity of the paper, and can improve the significance of the discovered representations. Answers to such questions in the rebuttal period will impact my judgement on the paper in a positive way.\n\nLIMITATIONS:\nWhile the authors provide suggestions on possible future work, they have no claims on the limitations if their work. As an example, are there any types of features that require combination of different transformer blocks, that cannot be identified with the current method. Also the usability of these features is another question. While editing results are reported, we have no clue on how successful these edits are or if the representations achieve disentangled edits overall. In the examples provided in Fig. 1, we see local changes such as skin color, but have no idea on more global features can be identified. Such limitations should be addressed in a possible revision.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper explores the use of Sparse Autoencoders (SAEs) for interpreting and controlling text-to-image models, specifically SDXL Turbo. The authors train SAEs on updates made by transformer blocks within SDXL Turbo’s denoising U-net and find that they generalize well to different versions of SDXL models. The learned features from SAEs are interpretable, causally affect the generation process, and reveal block specialization. This is the first study to apply SAEs for interpretability in text-to-image models, demonstrating their potential for understanding and manipulating these models.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The proposed method that leverages a sparse autoencoder to interpret and control diffusion models is interesting.\n2. The paper is easy to understand. The experiments are detailed and comprehensive.\n\nWeakness:\n1. The generalizability of the proposed method in this paper needs further analysis. What conclusions would arise if the original SDXL model or other U-net-based diffusion models, such as SD 1.5, were used? Currently, many diffusion models are based on transformer architectures, so how can this type of analysis be applied to transformer-based models? Analyzing only the SDXL-Turbo model may not provide enough of a technical contribution to be widely applicable.\n2. The relationship with other existing works. The existing research[1] also draws similar conclusions, and the author needs to further explain the connections and differences between their work and the existing literature.\n3. Can the conclusions drawn from these observations further enhance the generation capabilities of diffusion models or expand their potential applications?\n4. Can these properties be leveraged to further enhance these capabilities during the training of diffusion models to improve the generative performance of the diffusion model?\n5. The authors should check the paper carefully before submmision to avoid the typo error.\n\n[1] P+: Extended Textual Conditioning in Text-to-Image Generation\n\nQUESTIONS:\nPlease refer to the weaknesses.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates the application of sparse autoencoders (SAEs) to interpret and manipulate the intermediate representations of text-to-image diffusion models, specifically SDXL Turbo. By training SAEs on the residual updates of transformer blocks in SDXL Turbo’s one-step generation process, the authors successfully decompose complex hidden states into sparse and interpretable features. These features are shown to generalize across multiple denoising steps and even to other models like SDXL Base and FLUX Schnell. Using both qualitative visualizations and a quantitative benchmark adapted from PIEBench, the paper demonstrates that the learned features are not only interpretable but also causally influence the image generation process. Furthermore, the study reveals functional specialization among different transformer blocks, such as composition handling, object editing, and style refinement. This work represents a novel contribution to the mechanistic interpretability of diffusion models and establishes SAEs as a promising tool for understanding and editing their internal computations.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n\n### First application of SAEs to diffusion models: \nThis is the first work to apply sparse autoencoders (SAEs) for interpretability in text-to-image diffusion models, filling a significant gap in the mechanistic understanding of such models, which has previously focused mainly on language models.\n\n### Causal and interpretable feature discovery:  \nThe SAE-learned features are shown to be semantically meaningful and causally affect the image generation process. Manipulating individual features results in consistent and interpretable changes in the generated images.\n\n### Feature-based image editing:  \nThe authors demonstrate that SAE features can be used as an effective image editing interface during generation, by activating or suppressing specific features to achieve desired visual modifications.\n\n### Block-level functional specialization:  \nThe analysis reveals that different transformer blocks in SDXL Turbo (e.g., down.2.1, up.0.0, up.0.1) specialize in different roles, such as layout composition, object detail, or style. This insight contributes to a finer understanding of the generation pipeline.\n\n# Weakness\n\n### Limited baseline & Insufficient evaluation on Diffusion Transformers:  \nThe study focuses primarily on SDXL Turbo, which, while compact and accessible, is not representative of the latest advancements in text-to-image generation. As newer models based on Diffusion Transformers—such as PixArt-series [1,2], SANA-series [3,4], or Stable Diffusion 3 [5]—have demonstrated strong performance, it would be valuable to assess whether the proposed SAE-based interpretability framework generalizes to these architectures. Although the authors include some qualitative examples on FLUX Schnell, the absence of quantitative results (e.g., explained variance, feature transport success) on these models limits the strength of the generalization claims. A more thorough evaluation on modern DiT-based models would significantly improve the impact and applicability of the approach. Including quantitative evaluations on one or more recent DiT-based models would significantly enhance the generality and relevance of the proposed approach, and help demonstrate whether the observed interpretability patterns hold across different model families.\n\n### Missing analysis of convolutional components in SDXL-Turbo:  \nThe paper focuses its analysis exclusively on transformer blocks within the U-Net of SDXL Turbo. However, the architecture also contains convolutional blocks, which play a crucial role in feature extraction and image synthesis. Understanding whether these convolutional components exhibit similarly interpretable or causally manipulable features is essential for a complete interpretability analysis of the model. As it stands, the findings may offer only a partial view of the internal mechanisms underlying image generation. Extending the SAE-based analysis to convolutional blocks would provide a more comprehensive understanding of the U-Net architecture and help reveal whether interpretability techniques can uncover complementary or distinct types of visual concepts encoded in different layer types.\n\n# Minor point\nThe expression “One Step is Enough” in the title may not accurately reflect the core contributions of the paper. It could give the impression that the work is focused on accelerating diffusion models or improving generation efficiency, which is not the main focus. Instead, the paper is centered on interpretability using sparse autoencoders. A more precise title would help align reader expectations with the actual scope of the study.\n\n\n\n---\n[1] Chen et al. PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis. ICLR 2024.    \n[2] Chen et al. PixArt-Σ: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation. ECCV 2024.  \n[3] Xie et al. SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformer. ICLR 2025.  \n[4] Xie et al. SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer. ICML 2025.  \n[5] Esser et al. Scaling Rectified Flow Transformers for High-Resolution Image Synthesis. ICML 2024.\n\nQUESTIONS:\n1) While the proposed method is demonstrated on SDXL Turbo, which is a U-Net-based model, it remains unclear whether the approach can be applied to other one- or few-step generation models with different architectures, such as Diffusion Transformers. Have the authors considered how architectural differences may affect the applicability or reliability of SAE-based interpretability?\n\n2) The paper shows that SAE-learned features can influence the generated image, but it is not entirely clear what kinds of semantic or structural properties these features capture. Beyond the ability to manipulate generation, what specific insights into the model's behavior or representations were gained through this analysis?\n\n3) The caption for Figure 1 is somewhat minimal, especially concerning the Flux-schnell example. What do the numbers below the images represent, and how exactly does the image change as a result of the SAE intervention? It would be helpful to explain whether the SAE was trained using one-step intermediate features and then applied during a four-step or multi-step generation process for Flux-schnell, and how this intervention was performed.\n\n4) The authors state that the learned features “generalize” across different denoising schedules and models (from 1-step SDXL Turbo to 4-step and 20-step SDXL). Could the authors clarify what specifically is being generalized? Does this refer to the preservation of semantic meaning in SAE features, reconstruction quality, or transferability of causal influence?\n\n5) The analysis focuses solely on transformer blocks within the SDXL Turbo U-Net, even though the architecture also includes residual convolutional blocks. Given that convolutional and transformer features are known to exhibit different representational biases (e.g., texture vs. shape), it would be informative to know whether similar interpretability or control can be achieved through SAEs trained on convolutional features. Could the authors comment on why these components were not analyzed?\n\n6) The description of the generalization study (e.g., L133 and Figure 1) lacks sufficient detail. Could the authors explain more clearly how the visual examples were constructed? Specifically, how were SAE features trained in the one-step setting reused during multi-step generation, and how was the intervention performed?\n\nLIMITATIONS:\n\"No\" in the main paper.\n--\n- The analysis is limited to transformer blocks within the U-Net architecture, leaving out other critical components such as convolutional blocks, which may have distinct representational characteristics and contribute meaningfully to the generation process.  \n- Although the authors claim that the learned features generalize to multi-step generation and even to different models (e.g., SDXL Base, Flux-schnell), these claims are primarily supported by qualitative results, with limited quantitative evaluation or systematic comparison across architectures.   \n- The study is conducted on a relatively small number of U-Net blocks and features (e.g., four transformer blocks), raising the question of whether the observed interpretability and causal effects extend uniformly across the entire network.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nInspired by the application of sparse autoencoders in interpreting large language models, this work applied it to text-to-image diffusion models by training sparse autoencoders over the cross-attention blocks of one-step residual updates. This work found that training on one-step diffusion is able to explain most of the variance even in 4-step and 20-step diffusion. By using a SAM segmentation model, this work also showed that they can rank the importance of different found features and steer such features to control the generation process.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. Applying sparse autoencoders to text-to-image diffusion models is well-motivated to interpret features.\n2. Experiments show interesting specialization among blocks.\n3. The learned features can be used to steer the generation process.\n\nWeaknesses:\n1. It's not clear how stable the steering effect is. For example, if multiple samples were taken, would all show the same steering effect? Also, if the steering coefficient changes smoothly (from a large negative value to a large positive value), would the steering effect also change smoothly?\n2. It's not clear how well this method generalizes as experiments are mostly done on SDXL-Turbo.\n3. The results on Flux-schnell are not clear. Why does Figure 1 show \"the generalizability of our approach to recent Diffusion-Transformers\" (Line 146)? Why didn't you apply the same steering interventions as those applied to SDXL in each column (or did I misunderstand something here)?\n\nQUESTIONS:\n1. For Figure 1 can you show the effect of changing the steering coefficient smoothly from negative to positive? Would the steering effect change smoothly as well?\n2. What's the benefit of training the sparse autoencoder on 1-step updates? If for most experiments you used 4-step updates, why not directly train it on 4-step updates? Is it an efficiency argument? (but I assume you can use 1/4 of the data to train on 4-step updates with the same cost?)\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}