Title: AutoEdit: Automatic Hyperparameter Tuning for Image Editing
Venue: NeurIPS 2025
Accepted: yes
Keywords: Image Editing

Diffusion Model
Abstract: Recent advances in diffusion models have revolutionized text-guided image editing, yet existing editing methods face critical challenges in hyperparameter identification. To get the reasonable editing performance, these methods often require the user to brute-force tune multiple interdependent hyperparameters, such as inversion timesteps and attention modification, \textit{etc.} This process incurs high computational costs due to the huge hyperparameter search space. 
  We consider searching optimal editing's hyperparameters as a sequential decision-making task within the diffusion denoising process. Specifically, we propose a reinforcement learning framework, which establishes a Markov Decision Process that dynamically adjusts hyperparameters across denoising steps, integrating editing objectives into a reward function.
  The method achieves time efficiency through proximal policy optimization while maintaining optimal hyperparameter configurations. Experiments demonstrate significant reduction in search time and computational overhead compared to existing brute-force approaches, advancing the practical deployment of a diffusion-based image editing framework in the real world.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe author proposes AutoEdit, a method that employs reinforcement learning strategies to control hyperparameters during the editing process. This approach enhances time efficiency through Proximal Policy Optimization (PPO) while maintaining optimal hyperparameter configurations.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:  \n1. The authors present an innovative approach by adapting reinforcement learning (RL) to hyperparameter selection in image editing—a relatively unexplored research direction.  \n2. As demonstrated in Table 1, the proposed framework exhibits strong generalizability and can be extended to diverse image editing methods.   \n3. Rigorous experiments are conducted, with extensive results provided in the Supplementary Material to validate the method.  \n  \nWeaknesses:  \n1. The authors declare r as the inversion timestep in Line 120, but Figure 1 labels it as t, causing confusion. This discrepancy is not a typo, as Line 124 explicitly declares \"t > r or t <= r.\" Additionally, the symbol definition for the attention replacement ratio remains ambiguous.  \n2. The paper states that an inversion timestep of 40 yields optimal results for bird image editing (Figure 1). However, the edited image visibly deviates from the original at this timestep, undermining the claim.  \n3. A major weakness is the absence of image reconstruction results. Empirical tests with DDIM-inversion-dependent methods (e.g., P2P, PnP) reveal that faithful reconstruction of the original image is often unattainable—a critical weakness unaddressed in this work.  \n4. Some minor Issues: (1). \"$\\bar{\\alpha_t}$ decreasing to 0\"(Line 90) should be corrected to \"$\\bar{\\alpha_t}$ decreasing from 1 to 0.\" (2). The paper should introduce PPO in Preliminaries, a foundational RL method used in this work. (3). The term $R_{edit}$ is redundant, as it directly uses CLIP scores without modification. Labeling it as a distinct reward function may mislead readers into assuming novelty.\n\nQUESTIONS:\n1. The authors employ DDIM inversion, which inherently introduces errors. However, the paper does not address how these errors are mitigated or their impact on editing quality.  \n2. The study only considers two hyperparameters—inversion timesteps and attention-replacement ratios—while omitting other critical ones, such as the Classifier-Free Guidance (CFG) scale (typically defaulted to 7.5).  \n3. The claimed \"best editing\" results lack clear evaluation metrics in Figure 1. For instance, when editing the bird image at inversion timestep=40, the bird’s posture shifts, and the branches are overly edited. Subjectively, the t=30 result appears visually superior, yet the paper does not justify the preference for t=40.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-04"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the inefficiency in current text-guided image editing methods using diffusion models, which require brute-force tuning of multiple hyperparameters. The authors propose a reinforcement learning framework that models hyperparameter selection as a sequential decision-making process during the diffusion denoising steps. By formulating this as a Markov Decision Process and applying proximal policy optimization, their method dynamically adjusts hyperparameters based on editing objectives. Experimental results show that the approach significantly reduces computational cost and search time compared to traditional methods, making diffusion-based image editing more practical for real-world use.\n\nSTRENGTHS AND WEAKNESSES:\nPros:\n1. The paper presents an interesting formulation of the problem and perspective.\n2. The writing is clear and easy to follow.\n3. The results seem promising in some cases.\n\nCons:\n1. The authors' claim that “Assuming each $h^k$ can take on $N$ possible values, a brute-force search for the optimal configuration incurs a computational complexity of $O(TN^K)$, which is impractical in real-world scenarios.\" (line 45) seems to exaggerate the difficulty of the problem intentionally. Actually, for most of the methods such as prompt-to-prompt, the hyperparameters can only be selected within a small range rather than the whole range. Hence, the claim \"complexity of $O(TN^K)$\" is doubtful in real-world use.\n\n2. The authors define the reward function as two essential editing criteria: prompt alignment and background preservation. However, some editing tasks should follow more than just these two basic criteria, such as style transfer, viewpoint change, etc. The authors should think more comprehensively.\n\n3. The authors' method is based on training-free methods, which require a large amount of work selecting hyperparameters. However, many works are pretrained on large-scale data resources and are becoming the dominant direction. The authors should discuss and compare them.\n\n4. Using the CLIP score as the reward function is not reliable, as many prior works have revealed its weak usage in image editing tasks.\n\nQUESTIONS:\nCan the method be applied to other models like DiT-based image generation models, since Stable Diffusion 1.4 is limited in image generation quality? If so, please include more results.\n\nThe baseline methods are a little bit old. Please include more new works.\n\nLIMITATIONS:\nPlease see the weaknesses and questions.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-22"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes AutoEdit, which can automatically searches the optimal hyperparameters in image editing. This is achieved by establishment of a Markov Decision Process which intergrates serveral editing objectives into a reward function. Extensive experiments illustrate the effectiveness of the proposed method.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n- The motivation of this paper is clear. Hyperparameter-tuning is a long-last challenging problem in the image editing tasks. Designing methods to alleviate this problem is meaningful.\n- The proposed method is novel and interesting, treating the problem of optimizing hyperparameters as a RL task.\n- The experiment result seems impressive.\n\nWeakness:\n- For each specified image-editing method, AutoEdit requires employing PPO to train the model, which may lead to a substantial computational resource consumption. Could you provide the detailed information of the resources and times consumed during the training process of AutoEdit?\n- The Background preservation is considered in the reward function. If I understand correctly, this can result in a reduction of global editing capability (e.g., editing the style of the image or changing the season of a image from Spring to Winter).\n- All of the experiments are conducted on the U-Net-based method (such as P2P and PnP), these approaches are now somewhat outdated. Rectified flow–based DiT architectures have recently achieved state-of-the-art performance in image generation (such as FLUX), and corresponding rectified-flow image-editing methods have been proposed (such as RF-Solver[1] and FireFlow[2]). Could you integrate your method into the RF-DiT model to demonstrate its generalizability?\n\n\n[1]. Taming rectified flow for inversion and editing, ICML 2025.\n\n[2]. FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing, ICML 2025.\n\nQUESTIONS:\nSee weakness.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors introduce a reinforcement learning (RL) framework designed to automatically tune hyperparameters for diffusion-based image editing. The paper frames the challenge of finding the best settings for parameters like inversion timesteps and attention modifications as a sequential decision-making task. By establishing a Markov Decision Process within the diffusion denoising process, the proposed method uses a policy model to dynamically adjust hyperparameters at each step, optimizing the losses obtained by edit quality and background preservation. This approach finds near-optimal configurations in a single pass, avoiding the costly trial-and-error of traditional brute-force searches.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The hypothesis and problem definition is clear, the optimal hyperparameters for image editing are highly dependent on the specific image and edit, making manual tuning inefficient. It systematically formulates this issue by identifying the exponential complexity of brute-force search (and proposes an RL-based solution that reduces this complexity to a linear.\n\n- The proposed approach is time-efficient, reducing the O(TN^K)) time complexity into O(T).\n\n- The motivation for the work is strong and clearly described. The authors address the computational costs and usability barriers associated with the manual, trial-and-error hyperparameter searches required by existing image editing methods. By aiming to automate this process, the work is well-motivated to improve the practical deployment and accessibility of advanced image editing tools.\n\nWeaknesses:\n- The proposed RL formulation is dependent on a predefined edit mask M. The reward function is explicitly designed to calculate background preservation on the unmasked region, so it limits the models that are compatible with the approach. Is there any extension for non-masked based editings? (1-M) and prompt alignment on the masked region M relies on a mask inherently limits the framework's applicability to editing tasks that can be clearly segmented, such as object replacement or inpainting, while excluding global edits like style changes that do not have a discrete mask.\n\n- The framework's reliance on a segmentation mask may also limit its effectiveness for very fine-grained attribute edits (e.g., changing a person's eye color). Generating a precise mask for such a small, detailed region can be challenging, and the CLIP-based reward signal calculated on that tiny area may not be robust enough to guide the policy effectively. The paper's methodology seems better suited for object-level edits where clear segmentation is more feasible.\n\n- The experiments, while applying AutoEdit to several editing methods, implement all of them on a single underlying diffusion model: Stable Diffusion 1.4. This makes it difficult to assess how well the AutoEdit framework generalizes to other diffusion models, such as SDXL or other versions of SD architectures. A more comprehensive analysis would demonstrate its effectiveness across a variety of base models.\n\n- The comparison is made against a limited set of baselines, with the most recent being MasaCtrl, which was published in 2023. The field of image editing has advanced since, with newer and more powerful models being released (see Questions). Failing to compare against more recent state-of-the-art methods limits the conclusions that can be drawn about AutoEdit's performance relative to the current landscape.\n\nQUESTIONS:\n- How can it be integrated into other image editing tasks (such as shape changes, local attribute editing, etc.)\n- What about its performance on various models, such as different versions of SD, and how are them compared to more recent baselines (such as TF-ICON , IMPRINT, MIGC)?\n\n\nTF-ICON: https://github.com/Shilin-LU/TF-ICON\n\nIMPRINT: https://openaccess.thecvf.com/content/CVPR2024/papers/Song_IMPRINT_Generative_Object_Compositing_by_Learning_Identity-Preserving_Representation_CVPR_2024_paper.pdf\n\nMIGC: https://openaccess.thecvf.com/content/CVPR2024/papers/Zhou_MIGC_Multi-Instance_Generation_Controller_for_Text-to-Image_Synthesis_CVPR_2024_paper.pdf\n\nLIMITATIONS:\nyes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-29"}