AutoEdit: Automatic Hyperparameter Tuning for Image Editing
Authors: Chau Pham
Quan Dao
Mahesh Bhosale
Yunjie Tian
Dimitris N. Metaxas
David Doermann
Venue: NeurIPS 2025 poster ()
Keywords: Image Editing
Diffusion Model
Primary area: applications

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.

AutoEdit: Automatic Hyperparameter Tuning for Image Editing
Chau Pham
Quan 2
Mahesh Bhosale'
Yunjie Tian
Dimitris Metaxas
David Doermann
1 University at Buffalo
Rutgers University
Abstract
Recent advances in diffusion models have revolutionized text-guided image editing, yet existing editing methods face critical challenges in hyperparameter identifica- tion. 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, etc. This process incurs high computational costs due to the huge hyperparameter search space. We consider searching op- timal editing's 's hyperparameters as a sequential doccision-makinne task within the diffusion denoising process. Specifically, we propose a reinforcement learning framework, which establishes a Markov Decision Process that dynamically ad- justs 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. Experi- ments demonstrate significant reduction in search time and computational overhead compared to existing brute-force approaches, advancing the practical deployment diffusion- image editing framework the real world. Codes can be found htthtps::// http:::///ittan giggthh...... com/ chaupham 179/ AutoEdit.gii
Introduction
Image generation has recently witnessed remarkable advancements through diffusion mod- els
34 3 32 36), driving growing interest in their broad applicability. Within this domain, compt-to-prompp image editing 312 16
13
14 g has emerged as a critical subfield focused on modifying image content according to textual instructions. This task focuses achieving two essential objectives: (1) precisely aligning the modified image with the editing prompt, while (2) mini- mizing unnecessary alterations to the original content beyond those required by the editing instruction. Maintaining this delicate balance between instruction alignment and background preservation the primary challenge.
45
26
Despite notable advancements, current image editing methods (5 12 39 15
16
suffer from
a heavy reliance on manual hyperparameter identification during the editing process. These hy- perparameters span multiple dimensions: inversion timestep configuration 39 15 attention modulation mechanisms [12 16 (including layer-wise modification and semantic reweighting). and
feature blending coefficients [17 As empirically demonstrated in [45), the optimal hyperpa- rameter combination exhibits strong sensitivity across different images. This requires exhaustive trial-and-error iterations identify optimal hyperparameter sets that simultaneously satisfy two
"Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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timestep
replacement ratio
original Image
AutoEdR
0.3
0.6
AutoEdit
silver cat sculture sitting next to minor
eelorfatired bird standing branch
DDPM Inversion
P2P Editing
The cat image achieves best editing step 30 while the bird requires step 40 in timestep experiments, with similar variance observed in P2P [12] cross-attention ratios. Our AutoEdit automatically identifies near-optimal configurations across these parameters, matching manual search performance (last column).
Figure 1: Optimal hyperparameters vary significantly across images:
criteria: (1) achieving instruction- -lligned editing results while (2) preserving original background.
Such hyperparameter search introduces significant computational overhead and creates substantial usability barriers for non-expert users, as evidenced by our quantitative illustration in Figure
Previous work OIR 45 optimize the value of hyperparameter "inversion timestep r", which is the maximum timestep we try to invert image in editing process, for each pair (input images, edit prompt) to obtain better editing result. Specifically, OIR proposes brute-force search strategy that evaluates all possible r E ...,,...,7) (I' total denoising step) and selects the optimal r* to achieve the highest editing score. Furthermore, this approach requires T denoising processes per editing sample, which consume (T number of functional evaluation (NFEs). This method becomes impractical when T is large, which is around 50 100 in case of editing process. However, OIR only focuses choosing hyperparameter r while neglecting other critical hyperparameters.
For each editing method, we define a hyperparameter set H. {h' ,h",,,"... "f....,k'), consisting of K distinct hyperparameters. Assuming each h² can take on N possible values, brute-force search for the optimal configuration incurs computational complexity of (TTN* which impractical in real- world scenarios. To address this, we propose an efficient hyperparameter identification framework that reduces the search complexity from exponential O K) to linear OTT). Conceptually, a diffusion- based editing process can be viewed as denoising procedure influenced by the hyperparameter formulate the editing process as RL-driven Markov Decision
set H. This perspective allows Process (MDP). Specifically, each denoising step t = T - corresponds to a state s; (2t, where H2 is the noisy latent at timestep f. We treat each hyperparameter h² as sequence {ht h- and collectively, H4 = (h+ k represents a set of parallel actions taken at state 8t- The reward function is designed to integrate two essential editing criteria: prompt alignment and background preservation. Optimization performed using Proximal Policy Optimization (PPO) [37]. At each timestep the learned policy selects the optimal hyperparameter set Hr, which is directly applied during editing process. As the result, our technique could save the user from heuristically choosing hyperparamenter.
(1) First formulating optimal hyperparameter identification key challenge in iiffusion-based editing, exposing the computational bottleneck O(TN* of brute force search; (2) A reinforcement learning framework that unifies hyperparameter identification with standard denoising, achieving efficient O(T) optimal hyperparameter search; (3) Empirical validation showing that our method nearly achieves optimal hyperparameter while reducing search time by around three times versus brute-foree baselines.
Our core contributions are threefold:
Related work
Text-prompped Image Editing. Text-based image editing 5
38
modifies image
content based editing instructions, aiming to generate edited images that faithfully adhere to the

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generate images by iteratively denoising editing prompis while preserving most original background from the source image. While Generative
Adversarial Networks (GANs) [27 2T 43 achieved partial success in domain- -ppeciff datasets (e.g.
facial images), they struggle with text-based editing due to low performance of text-to-image GAN. This limitation has been substantially addressed by pretrained text-to-image diffusion models
34
which have enabled the emergence of sophisticated editing frameworks 4 [ 12
8
1
40
38
through three principal paradigms: (1) prompt-toppromp manipulation [ 12]
15
16
17
(2) instruction-basee editing [4 14 311, and (3) image personalization (35 9 However, existing
methods require brute-force search for optimal editing hyperparameters, which is non-trivial process
due to extensive trial-and-ene iterations. Motivated from reinforcement learning in improving image
generation [4 10 46 [29), we propose an RL framework for searching optimal hyperparameter in
text-based image editing.
RL Image Generation. Recent works have demonstrated reinforcement learning (RL. as powerful paradigm for enhancing text-to-image generation [41 23 29]. Pioneering works [24 10 46 29] establish RL frameworks that optimize text prompts through iterative reward feedback, effectively aligning the generated images with the aesthetic score and semantic metric. By designing domain-specific reward mechanisms, these methods could effectively generate high quality images aligning with reward function. Inspired by this line of research but differently, we propose formulating an RL environment for the image editing problem to identify the optimal hyperparameters.
Method
3.1 Preliminaries
Diffusion Models. Diffusion Models 13
28 39
Gaussian noise. They consist of:
Forward Process: Add noise to clean image xp over T steps:
2t (VV, zo. (1 1 a))1,,
with a2 decreasing from to thus øy N(.
Reverse Process: Learn backward transitions
p(1t-1 24) ( ozI),
where
pa
(x. ca( 1)
1-ac
Vas
V1-aa/
The noise prediction network Eg is trained by
Ssimple
Egg, e en ((..)) c0 1
modifies images align with editing prompts while preserving background. Under the prompt-to prompt edit setting, we denote the source image pere, source prompt Pare. edit prompt Peda, and edit region mask M.
Image Editing. Image editing [5]
The editing pipeline contains two stages: 1. Inversion: From source image Iere and source prompt Peres extract noise using an inversion technique. 2. Denoising: Using the editing prompt Pedit and extracted noise set, we gradually denoise the edited image reabtt During the above denoising stage, the users often need to search for optimal hyperparameter (e.g. attention weights, inversion steps) to obtain the edited image aligning to Pedst and preserving background. Formally, for editing method & with hyperparameter set H. we need find
H arg min ((Tedi (E(Ters :Pedit, H)), plarget). H
where D measures how well Jodit matches the edit criteria.
Proximal Policy Optimization (PPO). PPO 37 a popular and reliable method Reinforcement Learning, and is widely applied in LLM training 30), prompt optimization [24] [0]. PPO is a policy
gradient method, where the policy model updated by the expected gradient of the policy output
and the advantage. The common technique in Policy Gradient is to train two seperate networks for
3

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Method overview
TT
00
g
Policy Model
Policy Model
Policy Model
HT
Hl
H1
Hyperparameter identification
Policy Model
src
feat
MLP
He
Unet
f
Tedit
frozen
trainable
Figure 2: Top: Overview of the proposed AutoEdit framework. A policy model is injected predict the step-wise hyperparameter Hy at each denoising step The predicted H. is used with the one-step denoising function g and current state 2; estimate ø--1- Bottom: Architecture of the policy model. Features from the U-Net encoder under the original and edited prompts are extracted and concatenated, followed by several trainable layers predict the policy output.
estimating the policy output mo (policy model) and the other network to estimate the reward to go V/- (value model). In PPO, the policy model updated by:
L( [min(rrr clip(r,, e, e)
where A, is the advantage computed at time of the rollout process, and ri mo(acls)) between current and old policy. The value model is updated by:
is the ratio
L(T) = E. (sV) ft
where R is the expected return, which can be computed through the GAE method.
Hyperparameter Selection. Given specific Pedir and _arc, editing performance heavily depend on hyperparameters H. Naive trial-and-error tuning runs denoising for each H. which is infeasible as k grows. Previous methods like OIR [45 perform T editing trials for f1,2...., T). but (1) searching cost scales to O NFEs and (2) they tune the only hyperparameter, the inversion timestep. We introduce AutoEdit, single-pass denoising framework that efficiently finds H".
3.2 AutoEdit: Automated Hyperparameter Identification
3.2.1
RL Environment Formulation
We formulate the problem of optimizing hyperparameters as RI task by integrating the RL frame- work into the diffusion denoising process. In this section, we define the key components of the RI. environment:
State. In RL environment, we detine St = (Iz, t) as the state, with He is noisy latent at time = + The initial state ST (xr,T)
Action. At each state 8; at timestep t, selecting a hyperparameter configuration treated as an action. Given set of K hyperparameters 7/ = {h' h². ..... K), aim to determine the optimal K configuration H, {br Jk=1 at every to maximize editing performance. This naturally leads us

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to model each hyperparameter h as temporal sequence {h, ht=-11 across the editing trajectory. As a concrete example, consider the case where = {r), with r denoting the inversion timestep. The editing process can be divided into two consecutive stages: denoising from T to using the source prompt Par. followed by denoising from r to using the edit prompt Pedit this formulation, choosing is equivalent to defining a prompt sequence Hi {, where he {Psre= Podit) depending on whether >> r or t S r. Other hyperparameters can be similarly represented as time-dependent sequences similar the inversion timestep r, as elaborated in the Appendix. Therefore, at each timestep i, the configuration H, constitutes a set of parallel actions over all K hyperparameters at state 8t. enabling flexible and
control throughout the editing process. Once the hyperparameter configuration M4 is defined, the next state 2t-1 can be found by applying the diffusion denoising step It-1 ( t, Hz), where 9 is one-step denoising function.
Reward. We define the reward function to align with the measure D, which computed from the edited image Jedkt based on two editing criteria: background preservation and prompt alignment.
Background preservation. The background region, defined as the area outside the editing mask M, should remain consistent with the original image / _ We employ the mean squared error (MSE) to quantify this consistency since it is susceptible to subtle pixel-level changes. The background preservation reward is formulated as:
Rroedit = -MSE((1- - M) @ r 10',,(11 ,1 M) O redit),
where denotes element-wise multiplication.
Prompt Alignment. The content within the mask M of the edited image rodit should be semantically aligned with the edit prompt Pedaz We measure this alignment using the CLIP score [33], which computes the semantic similarity between the CLIP embeddings of the edited image and the text prompt. Denote Redit as the reward of the prompt alignment.
The total reward is computed weighted combination of the prompt alignment and background preservation rewards:
R(H, 1870 Paacss Pedit) = aBedit + BRmooditt
where a and /3 are coefficients that govern the trade-off between edit effectiveness and background preservation. Empirically, we set a 3 30 as the default configuration to achieve an optimal cquilibrium between the two objectives. This balanced weighting ensures robust alignment with the edit prompt while minimizing undesired alterations to the background. The sensitivity of model behavior to variations in a and B3, along with further analysis of this trade-off, is detailed in Section 4.4
Global editing and small region editing For global editing tasks, such as style transfer, we set the mask to M = across the entire image and define the reward as R = Redit, following the evaluation protocol of the PieBench dataset. For small region editing, such as changing eye color, the CLIP score reward is less effective on small regions. To address this, we incorporate a Large Vision-Language Model (LVLM) to compute prompt alignment rewards. Specifically, for each edited image, given the original and edited prompts, we pose a question related to the editing instruction and let the model select the correct answer from multiple choices. The reward is defined as the total number of correct answers. We provide the ablation study and the effectiveness of using LVLM as the reward compared to the CLIP score reward in section 4.4
Termination. The RL process terminates upon completing all T denoising steps. We employ Proximal Policy Optimization (PPO) (37 for training, which comprises two core components: 1) a policy model no (+x) = n (2x, 1) that estimates action probabilities given states 8;, and 2) a value model V- V=(2,, 1) that predicts state rewards. Additional implementation details are provided in Section 3.2.3
We frame hyperparameter optimization as reinforcement learning problem where each denoising timestep corresponds to a unique state. This formulation induces an exponential state space growth (NT possible states), where N represents the possible value of state. To address the challenge of this huge state space while leveraging the constrained range of the hyperparameters in image editing, we propose a two-phase RL strategy: Phase 1 Pretraining: The policy is pretrained using hyperparameters randomly sampled from a predefined prior distribution. This constrains exploration to promising subspace, significantly reducing state space complexity. The resulting model ne, serves as initialization for subsequent learning. Phase Online Learning: Starting from Te,, the policy Te, engages in environment interactions while continuously optimizing through
5

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reward maximization. This stage enables optimal hyperparameter searching within the constrained hyperparameter space from Phase
3.2.2 Phase Policy Initialization
In image editing, we exploit known priors for each hyperparameter, e.g. inversion timesteps [T5
commonly in [20, 45] or attention-replacement ratios [12] commonly in [0.2, 0.8). Let po(H) denote
these priors over H (HT, Hi}. We train the policy model Ty to align its outputs with po by minimizing the objective function:
T
01 = arg min (HH)
L1 no ( (), Hi)
(3)
T
1=1
where L1 penalizes deviation from the sampled He-
3.2.3 Phase 2 Hyperparameters Identification
During online exploration, at each denoising step f, the policy samples Hi To( t) (see Figure 2 This action reconstructs the previous latent -1-1 g( t, Hz), advancing the denoising process. The full hyperparameter sequence H. .Hr...., H1} yields the edited image redit, from which we compute a reward R in equation 2
We then update the policy by minimizing the following objective:
C2
[R(H, Iore
Pares Podit) DKI.].
(1 PareePedit I-Paaaa
where Pdata is the data distribution, and DxL. regularizes the current policy Ao, against the stage-1
policy ne, 330 24 20 Since H4 ( t), we only require O(1) for each denoising step, resulting
in OTT).
We also train the value model V, to predict the cumulative reward at each state by minimizing the error between its estimation and the actual return. In PPO fashion, the policy xy and value model V, alternately optimized to maximize expected cumulative reward. (More information of training and testing phases could be found in the Appendix)
3.2.4 Network Design
We treat each pair (Zz, t) as RL state. We condition the policy on Iı, timestep t, and prompts Perc: Pedit since the optimial hyperparameters depend on the provided prompts, which match with the U-Net input. A pretrained U-Net Encoder U.. extracts two features sore = U_ (1 P.rc)), Sedit U1( L, Pedit). These feature maps are concatenated, then passed through a 2 convolution and spatial average pooling layer to yield Seat- Timestep /. is encoded sinusoidally and linearly projected to fc. We concatenate [foari fi]. process it with two ReLU-activated fully connected layers, then use K separate linear heads to predict K hyperparameters. The value model shares this backbone architecture but output a single scalar output. Only the newly added layers are trained (see Fig. 2).
4 Experiment
4.1
Dataset Setup
Our training data originates from the EditBench collection 22 with task selection strictly aligned to PieBench [16] benchmark objectives. To establish coherent framework for prompt-to- prompt-to-prompt prompt editing, each training datapoint requires four elements: the original image, the original prompt (generated by ChatGPT based on image content), the edit prompt (derived through ChatGPT analysis of edited images and instruction prompts), and the edit mask (object segmentation via SAM 19).. This curation process involves three systematic steps: Object Localization: ChatGPT extracts target editing objects from instruction prompts through cross- modsl analysis. Prompt Engineering: Dual prompts (original/edited) are synthesized by ChatGPT using image-object contextual relationships. Mask Generation: SAM precisely segments identified objects from original images. The resulting dataset contains 2,000 rigorously annotated samples. For evaluation, we adopt the PieBench dataset [16), which contains 700 samples across diverse editing scenarios, ensuring comprehensive capability assessment.

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4.2 Implementation Details
We choose denoising step 50 by default. During Phase-1 training, we implement parameter initialization policies based on different editing method requirements. 1) For methods requiring inversion timestep searching, we randomly sample the inversion timestep within the range of 0.35 0.95 of the total denoising timesteps T: 2) For methods involving cross-attention or self-attention replacement ratios, we initialize these parameters by uniform sampling from their default range of 0.2 0.8: 3) For scalar hyperparameters (e.g., attention weights in 12), we instruct the policy model to predict their default values. More details of the prior for each type of hyperparameter can be found in the Appendix. In Phase-2, we integrate the Phase-1 model into the training framework to compute the KL. divergence term DKL in Equation following the implementation strategies in [24 30) Both policy and value models are optimized using Adam [18] with fixed learning rate of 5 x 10 For the reward function configuration, we set the coefficients & 30 and / = 30 as default values unless otherwise specified. Consistent with prior works 24 30 the KL divergence coefficient remains = 0.02 across all experiments.
4.3 AutoEdit Improves the Performance over Other Methods
Baselines. We evaluate AutoEdit across multiple image editing frameworks, including training- free methods such as DDIM-Inversion [39], DDPM-Inversion [1]. PnP-Inversion [16,, Prompt-to- Prompt [2]. MasaCtrl [5), and Null-text Inversion 255 well training-based methods such as InstructPix2Pix 4 and UltraEdit (7).. Our experiments span a variety of base models, including SD 1.4. SD 1.5, SDXL, and DIT. The hyperparameter settings for each method such as inversion timestep ranges and attention replacement ratios are systematically reported in the Appendix.
Metrics. Following the PieBench benchmark (16), evaluation protocol measures four key aspects of editing performance: (i) structural consistency, quantified by Structure Distance (SD); (ii) background preservation, assessed with classical metrics including PSNR, SSIM, MSE, and LPIPS; and (iii) semantic alignment, evaluated via CLIP scores (ViT-B/32) computed separately for edited regions and full images. To further validate the quality of edits, particularly on small regions, we additionally incorporate an evaluation based on the judgment of a Large Vision-Language Model (LVLM), referred to as the LLM Score. For each image and its corresponding editing instruction, the LVLM is prompted with a question derived from the instruction, and its response is used to quantify editing quality. Details of question construction and the LLM Score evaluation protocol are provided in the Appendix.
Quantitative Results. We present the main experimental results in Table We report the best performance for the baseline methods with the hyperparameter configuration that yields the highest reward. For DDIM Inversion (39] and DDPM Inversion (15), the best configuration of the inversion timestep r is 35 and 40, respectively. Under these configurations, the models achieve high CLIP scores but struggle with maintaining background consistency. When integrated with AutoEdit, both methods demonstrate significantly improved background preservation, accompanied by only slight drop in CLIP scores. We contend that this minor decrease has minimal impact on editing fidelity due to the inherent insensitivity of the CLIP metric. Moreover, these methods inherently face a trade-off' between editing fidelity and background preservation. Our proposed reward addresses this issue by offering more balanced assessment of editing performance. Additional visual evidence of this trade-off is provided the Appendix. Furthermore, AutoEdit consistently improves performance on the LLM Score metric for both inversion-basee methods (3 15]. For P2P 12), PnP [16], and MasaCtrl 55) AutoEdit leads to enhanced performance in both background preservation and CLIP editing scores, underscoring its effectiveness in guiding hyperparameter identification.
We further evaluate training- based approaches. including InstructPix2Pix 4 and UltraEdit 47], by applying AutoEdit to adaptively determine the CFG coefficient during the denoising process. Com- pared the default fixed value of 7.5, the adaptive CFG strategy improves background preservation metrics while maintaining prompt alignment performance.
Our experiments span diverse set of editing methods and base models, demonstrating the general- ization of AutoEdit. More results of AutoEdit on baweb editing method is in Table
Qualitative Results. We present qualitative results in Figure 3 For each baseline, we visualize the output generated using its default hyperparameters, as specified in Table Overall, AutoEdit consis- tently improves the performance of all editing methods by selecting more suitable hyperparameter
7

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Table 1: The comparison with popular image editing methods. Note that AutoEdit enables
improvement over baselines with only negligible cost.
Base Model
Structure Distance
Background Preservation
CLIP Score
L.M
Method
PSNR
SSIM f MSE LPIPS
Edited
Whole
Score
DDIM-Inversion 39
38.10
21.36
76.67
103.95
146.60
23,30
26.31
0.96
SD 1.4
+ AutoEdit
18.74
24.65
81,28
52.94
95.10
22.65
25.72
1.12
DDPM- InMM-Ineesson
22.12
22.66
78.95
53.33
67. 76
23.02
26.22
1.03
SD 1.4
+ AutoEdit
12.65
27.25
85.17
31.18
50.51
22.52
25.83
1.17
PuP Inversion
[6
11.65
27.22
84,76
35.86
60.67
22.10
25.02
1.10
SD 1.5
+ AutoEdit
11.06
27.85
85.04
33.77
60.12
23.00
25.79
1.19
P2P [12
14.75
25.82
84.02
40,93
61.78
22.29
25.44
1.08
SD 1,4
+ AutoFdit
13.76
26.45
84.08
36.24
60.60
23.88
26.55
1.22
MasaCtrl S
28.38
22.17
79.67
86.97
79.67
21.16
23,96
0.92
SD 1.4
AutoEdit
21.33
23.48
80.06
46.28
71.35
21.75
24,86
0.99
DDPM- Inversooosoo
15
7.12
26. 13
89.88
35.32
65. 62.62
23.0
27.11
1.19
SDXL
+AutoEdit
6.46
27.86
90.50
20.44
53,51
22.9
26.7
1.27
UltraEdit (4 +AutoEdit
10.82
26.5
84.7
46.7
75.8
22.4
25.6
1.20
MM-DiT
7.61
27.3
86.2
37.6
64.9
22.6
25.7
1.26
InstructPix2Pix [4
35.37
20.8
76.4
226.8
157.3
22.1
24.5
0.65
SD 1.5
+AutoEdit
28.68
22.2
78.5
181.4
132.8
22.3
24.7
0.82
Null-text [25]
19.87
23.8
79.9
64.4
109.8
22.3
25.9
1.12
SD 1.4
+AutoEdit
10.91
25.7
82.4
45,4
82.3
22.6
26.3
1.21
configurations. Our qualitative analysis highlights several common failure cases resulting from sub- optimal hyperparameter choices: inaccurate background reconstruction (e.g., the stone background in the cat image for DDIM-Inversion and DDPM-Inversion). unnatural object synthesis (e.g. the distorted rock in P2P), failure to execute the desired edit (e.g., the unaltered stone in MasaCtrl or the unchanged noodles in MasaCtrl, PnP, and DDIM- Inve--nn,ss,on,, and noticeable facial discrepancies (e.g., the altered woman's 's face in DDPM-Inversiin and P2P). These findings demonstrate that improved hyperparameter selection, facilitated by AutoEdit, can substantially enhance editing quality, even for weak baseline methods such as
Policy Model Behavior in Inference. We analyze the behavior of key hyperparameters predicted by the policy model during inference. For the inversion timestep in DDPM-Inverts [5]. where the policy chooses between Pare and Pedia. we define the inversion timestep as the first step f where the policy selects Pedit. Figure 4a shows the distribution of inversion timesteps chosen by the policy model. We observe that the policy most frequently selects between 25 and 40, which aligns with common choices in prior editing methods. similar analysis is conducted for the cross-attention replacement ratio r in P2P [12] with results shown in Figure 4b) We find that is most often selected between 0.3 and 0.6. Importantly, both t and r vary significantly across samples, indicating that AutoEdit adapts its hyperparameter predictions for each specific image to optimize the final reward.
Reward Across Training Episodes. We plot the reward of AutoEdit on DDPM--Ivvrsien the first 3000 training episodes. The results are shown in Figure 4c where episode 0 corresponds to the Phase-1 model Ao,- We observed a consistent increase in reward during PPO training up to episode 2500, after which it slightly declined at episode 3000. Based on this trend, we limit training to the first 2500 episodes (approximately 15 epochs).
15 over
4.4 Ablation Study
Table 2: The necessity of Phase-1 (policy initialization).
Effect of Reward Co-
efficients of and 3.
P1 PSNR 1
SSIM 1 MSE
LPIPS J |Edited t Whole Reward
We investigate the im-
18.2
74.5
208.7
57.9
23.2
26.3
6.12
pact of different values of a and 3 in the re- ward function. Using DDPM-Inversion [5]] as the baseline, we fix a 30 and train AutoEdit with varying values of B. All models are initialized from the same Phase-1 checkpoint A,. 3 the behavior of
22.1
77.4
52.7
69.7
20.7
23.4
5.42
27.2
85.3
31.1
50.5
22.5
25.8
6.25
AutoEdit under different 3 settings. When 8 is small (e.g.. 3 = 10 or / = 20), the reward function
8

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Original image
DDIM-Inversion
DDPM-Inversio
P2P
PnP
MasaCtri
A sitting on grass
AutoEdit
girl sitting at table with pizza noodles and drinks
AutoEdit
Figure 3: We compare the qualitative results of AutoEdit with the default hyperparameter choice of the baseline. Overall, AutoEdit can search for better hyperparameters, resulting in better object editing, background preservation, and more natural images.
60
40
5.8
30
5,6
5.4
10
30 timestap
1300 2009 3000 Episode
replacement
(a)
(b)
(c)
Figure 4: The analysis of a) inversion timestep, b) cross-attention replacement, and c) the reward during the training of the policy model Ag.
emphasizes the CLIP score more. result, AutoEdit achieves higher CLIP scores, the cost of reduced background preservation. Conversely, when 3 is increased to 40, the model prioritizes background preservation, as it becomes more influential in the reward signal. 3 30 strikes good balance between CLIP alignment and background consistency. Figure provides qualitative examples illustrating the effects of different values.
Different choice of the reward. We find that the CLIP score performs poorly on small region image editing tasks with limited edit mask M. such as eye color modification. as the reward fails to reliably capture editing quality. In contrast, Large Vision-Languae (LVLMs) can effectively assess localized edits, example, verify whether the eye color has been correctly changed, as highlighted by recent editing benchmarks [22 44. Since our proposed RI framework is generic, can incorporate any reward function that reflects editing quality. Accordingly, replace the CLIP-baser reward with an LVLM-based evaluation. Our LVLM reward is similar to
result, using CLIP Models

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origin image
B = 10
B= 20
B = 30
B= 40
photo of geat horse and a cat standing on rocks near the ocean
Figure 5: Edited image with different value of
Table 3: Comparison of the behavior of AutoEdit with the different choices of and J. When
decrease the 3, AutoEdit optimizes the CLIP score. Otherwise, tries to preserve the background.
a,
PSNR t
SSIM t
MSE +
LPIPS 4
Edited f
Whole
0 30, ==0=1 10
19.65
77.11
150.5
138.6
24.15
27.34
30, 8 20
23.59
82.15
66.84
82.30
23.44
26.95
a = 30, 3 = 30
27.25
85.17
31.18
50.51
22.52
25.83
30, 3 40
28.53
86.03
24.72
42.80
21.36
24.36
the LLM Score metric, which is described in detail in the Appendix. reports the performance of AutoEdit under LVLM evaluation. We observe consistent improvements in both background preservation metrics and LLM Score, while maintaining strong prompt alignment performance.
Policy Initialization. We conduct experi- ments to highlight the importance of Phase- I training (policy ini- tialization). Using DDPM-Inversin [15] as the baseline, we compare results with and without Phase-1. The outcomes are summarized in Table 2 As shown in the table, omit- ting Phase-1 causes the model to apply overly strong edits while failing preserve the background, resulting in substantially lower background preservation score. We attribute this behavior to the difficulty of exploring the large state space introduced by the 50-step denoising process in DDPM. In contrast, incorporating Phase-1 improves background preservation and CLIP alignment, demonstrating the effectiveness of this initialization phase in guiding policy learning.
Table 4: Comparison of AutoEdit performance with LLM Score the reward function
Method
PSNR SSIM MSE LPIPS) Edited Whole LLM
DDPM Inv
26.1
89.8
35.3
65.6
23.0
27.1
1.19
AutoEdit
27.8
90.5
20.4
53.5
22.9
26.7
1.27
AutoEdit LLM
29.1
91.8
19.1
49.1
22.7
26.6
1.31
Computational Cost. We compare the inference time and number of parameters between the original U-Net and AutoEdit for a single image-prompt pair Table 5 reports the inference time of AutoEdit on Stable Diffusion. Our method introduces only negligible overhead in inference time and model size compared to the original U-Net.
Table 5: Computational cost of AutoEdit is negligible.
in
Method
Inference (ms)
#Params
SD Unet
20.89
86M
+AutoEdit
21.03 (+0.6%) 87.1M (+0.6%
Conclusion
We propose AutoEdit, an RL-based framework for per-image hyperparameter identification in diffusion-modee editing. By casting the denoising process as a reinforcement learning problem. AutoEdit searches within single trajectory. eliminating the exponential overhead of trial-and- Empirical results across multiple editing methods demonstrate that AutoEdit improves editing quality while maintaining runtime efficiency.
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A
AutoEdit baselines
In this section, we provide the details of the hyperparameters used in each baseline method:
Baselines
These methods first invert the image to a latent representation using the source prompt Perc. followed by a denoising process from timestep r to conditioned on the edit prompt Pedit- The only required hyperparameter in this process is the inversion timestep, i... H. = {r}. Due to the reversibility of the DDIM/DDPM framework, each denoising step can be viewed an action H₂ = {he), where h4 {Prre: (Psre:Pedit] ). By default, the inversion timestep is set to r = 35 for DDIM-Inversion and r = 40 for DDPM-Inversion.
DDIM/DDPM Inversion [39 15:
P2P 12] introduces three key hyperparameters: the inversion timestep T, the cross-attention replacement ratio u, and the attention weight w, summarized as H = {r. u, w). The inversion timestep can be handled similarly to the strategy used in DDIM/DDPM-Inversin attention ratio u modifies the attention mechanism in the U-Net by replacing the cross-attention conditioned Pedit with that of Pare during the first u T denoising steps, where T is the total number of steps. This decision is modeled as a binary action at each timestep: whether to replace the cross-attention or not. The attention weight w scales the cross-attention values for the edited word, determining the emphasis placed on that word in the output image. We discretize w by allowing the policy select from a fixed set of values f0.5, 1.0, 1.5, 2.0, 3.0, 5.0). Formally, we define the Pedie): ). hτ E {0, 1} (with 1 indicating replacement), and h3 {0.5, 1.0, 1.5, 2.0, 3.0, 5.0). By default, the inversion timestep is set to r = 10, the cross-attention ratio to u 0.4, and the attention weight to = 1.0.
The cross-
action space at each timestep as 74 = {h, h2 h2), J. where h E {Prrre Pedit
PnP Inversion 16] In this method, two binary hyperparameters. are defined at each denoising step, denoted as Hi {h], h. The first, h E {0. 1}. determines whether to replace the self- attention computation in the edit branch with that from the unconditional branch. The second, he E {0, 1). controls whether to replace the convolutional features of the edit branch with those from the unconditional branch. In the default setting of PnP Inversion, self-attention is replaced during the first 80% of the denoising process, while convolutional features are replaced during the first 50% of the steps.
At each denoising step, the method decides whether to replace the self-attention in the edit branch with that from the unconditional branch. This decision is represented by binary hyperparameter He [. where he E {0, 1}. By default, the replacement applied starting from timestep / 4 of the denoising process.
MasaCtrl [3]:
UltraEdit and InstructPix2Pix (4] Both methods are training-basee based editing method. Our RL framework is used to optimize the CFG coefficient during the sampling process of both methods. The default CFG coefficient set to 7.5.
Null-text inversion (5
Null-text is an inversion method that addresses the mismatch between inversion and denoising process through CFG by optimizing the null embedding. In this method, we use P2P as the editing operation. We only use RL to optimize the inversion timestep and cross attention ratio Similar P2P, we set the default value of r 40 and 0.4.
Phase-1 prior for each type of hyperparameters: The inversion timestep r is randomly sampled from the range [50- 0.657, 50 0.05T".. The cross-attention ratio is similarly sampled from the range [0.2, 0.6]. The prior for the attention weight w is fixed at 1.0. For PnP Inversion [16, the ratios for replacing self-attention and convolutional features randomly sampled from the range [0.2T, 0.87]. Lastly, in MasaCtrl [L self-attention replacement begins at timestep t, where is randomly selected between step 4 and step 20.
Phase-1 loss: Since the parameterizations of H. are discrete, we choose the loss C1 for Phase-1 training to be the cross-entropy loss. After sampling H ~ PoH)), the loss L, is applied at each timestep to enforce the policy model's prediction to match H., the timestep-specific parameterization of H. at t.
14

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Table Results AutoEdit on a Flow-based image editing method
Method
PSNR
SSIM
CLIP Edit
CLIP Whole
LLM Score
Taming flow 42
23.4
81.5
22.9
26.0
1.22
+AutoEdit
25.7
85.2
23.4
26.1
1.30
Fireflow 6
23.1
82.2
22,4
25.2
1.20
+AutoEdit
26.2
86.2
22.9
25.2
1.27
B More results on flow-based editing
We present the results of AutoEdit applied to flow-based editing methods, shown in Table Specifically, AutoEdit is used to search for the optimal injection timestep, key hyperparameter in both Taming Flow [42] and Fireflow [6]. Overall, AutoEdit enhances baseline performance in terms of both background preservation and prompt alignment, demonstrating its effectiveness in automatically identifying hyperparameters that improve editing results.
C Global editing
For global editing tasks, such style transfer, the editing mask set to M = for the entire im- age. In this case, Rooedit function At reduces to the CLIP score. To assess the effectiveness of AutoEdit in this setting, we report CLIP scores in Table 7 comparing against the baseline methods
Table 7: AutoEdit on global editing task
and the reward
Base Eddeing method CLIP score
SDXL SDXL
DDPM-Inv [15]
26.5
+AutoEdit
28.3
Inversion (I5] and P2P [12]. The results show that AutoEdit consis- tently improves editing performance on the style transfer task relative to the baselines.
SD 1.4 SD 1.4
P2P
25.5
+AutoEdit
26.7
D Convergence of PPO.
We evaluate how closely AutoEdit ap- proaches the optimal reward through PPO training. In this experiment, we compare the reward achieved by Au- toEdit against the best possible reward obtained from combinations of & differ- ent hyperparameter values, where & e 3). We compute the reward for each image using k different hyperpa- rameter settings and select the maximum among them. The & values are selected from the set that yields the highest possi- ble reward.
Table 8: Comparison of AutoEdit reward with baselines using multiple optimal hyperparameter sets. AutoEdit achieves performance comparable the best of three hyperparameters, demonstrating its convergence to a near- optimal reward.
#Trrils
Method
AutoEdit
Optimal
2
3
DDIM-Inversion
5.81
6.03
6.11
6.09
6.17
JDPM-Inversion
6.11
6.21
6.23
6.25
6.32
P2P
6.17
6.31
6.37
6.38
6.45
MasaCtrl
5.47 5.59
5.65
5.65
5.75
E LLM Score
For small-region editing, the CLIP score fails to reliably capture editing quality. As illustrated in Figure when the objective is to change dog's eye color to blue, computing the CLIP score small mask M with the prompt "blue eye" yields a lower score for the correctly edited image. This highlights the inaccuracy of CLIP-based rewards in such scenarios.
Previous evaluation benchmarks 22 have adopted LLM judgment as an evaluation tool. A key advantage of LLLM-based evaluation is its ability to verify whether small region edits are correct, as illustrated Figure 6 Since our framework generic, we replace the CLIP based reward with an LLMdbased evaluation, which denote LLMScore. This score not only measures the quality
15

---

Change the eye color of the dog to blue
black eyes
blue eyes
M
LLM query: Is the eye color of the dog blue?
CLIP: 0.232
CLIP: 0.226
MSE: -0,0011
MSE: -0.0011
Reward: 6.97
Reward:
CLIP evaluation
LLM evaluation
LLMEval:
LLMEval:
LLMScore:
LLMScore: 1.25
Figure 6: The advantage of LLM evaluation compared with CLIP score small editing region
of edited image but also serves directly reward function. More specifically, similar to the original reward, LLMScore consists of two components:
Prompt alignment For each editing instruction, construct a binary ("Yes/No") question to verify whether the required edit is present in the generated image. The edited image is then provided the LLM, which outputs response. We assign Redit if the response is correct and Redit otherwise.
Background preservation For background preservation, we adopt MSE as the primary metric, it is most sensitive small changes in the background region. Accordingly, define the background preservation reward as: Rnoedit 111+MME((( (1 M) c Tedst (1 M) Trre)
The LLM score is computed as LLM Score = Redit + Rooedit- We found a works best in the editing task. For the LLM model, we adopt Qwen VL-2.5-BB the LLM model.
F Trade-off between edit alignment and background preservation in DDIM/DDPM Inversion
DDIM and DDPM Inversion 33 15
trade-off arises between alignment with edit prompt
and preservation of the original background. A high CLIP score typically indicates strong alignment with the edit prompt, but often the cost of background fidelity, and vice versa. This trade-off illustrated in Figure 7 Our proposed reward function offers meaningful evaluation of edited images by explicitly balancing CLIP-based semantic alignment and background preservation (see Figure 77.
G Training and inference algorithm
We present the training and inference of AutoEdit in Algorithm
H Limitation
We illustrate the limitation of our approach in Figure AutoEdit relies on the underlying editing method Consequently, if fails to perform given edit, AutoEdit inherits this limitation. This issue is exemplified by the parrot case shown in Figure
16

---

photo of geat horse and cat standing rocks near the ocean
MSE: 0.111. CLIP: 0.252
MSE: 0.024 CLIP: 0.207
MSE: 0.0054 CLIP: 0.174
Reward: 4.23
Reward: 5.48
Reward: 5.17
Figure 7: We provide the visualization of the trade-off between edit alignment and background preservation in DDIM-Inversion. Our reward can better reflect how good an edited image is.
Algorithm 1: AutoEdit Inference
Input: Editing method & with denoising function g and inversion function 9 gー input image Tærc, origin and edit prompts Perc: Pedit, pretrained policy model Te. pretrained Stable
Diffusion Model
Output: Edit image edit
for t f- to do
Perform diffusion inversion: w/41 9 (s
end
21 edit
for
t + to 1 do
Sampling from the policy: He ~ Ta( ().
Denoise to get the previous noisy sample: of g( ,t, H4)
end
VAE decode: redit VAE(x
return Tedat.
Original image
DDPM-Inversion
AutoEdit
two kissing parrots sitting on stick, city street background
Figure 8: The limitation of our approach. A key limitation of our approach is that if the underlying editing method performs poorly on a given image, AutoEdit is also likely to inherit and suffer from the same image.
17

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Algorithm 2: PPO Training of AutoEdit
Input: Editing method & with denoising function & and inversion function g policy model g Phase 1 model To, ppetrained Stable Diffusion Model, coefficient
3 = 0.02, = 0.999, = 0.95, = 0.2
Inversion:
a -aaees Podit ~ Pdata
for ( to T do
Perform diffusion inversion: #f49 (, end
|
PPO exploration phase:
for / 4 to 1 do
Get the distribution of action at current policy model: o? = T0z (affdii t).
Get the distribution of action Phase-1 policy model: me To, (agdle ,
Sampling the hyperparameter: Hr ~ m².
Compute the value: 1/2 (a ,).
Denoise to get the previous noisy latent: xt = glatdit, ,1, 1,74)
end
Reward computation:
Get the edit image pedit V Aa(ddiitt
Compute the reward = a Redit (ledit Pedsts M) + Rrondst (Tedit Jedit fore, M)
Compute the return value and advantage recursively from the end of the denoising trajectory:
Ao
for t <- to to do 5 = - 71--1-
Compute the advantage: A, 8 + 7dAr-1
Compute the return: = A: + 1t
end
Training value model: - [[[[[V] (Ee, re)
Training the policy model:
Compute ratio u, = exp(log To, (xit, 1) log( m=
Clip objective: Letip = min( A,t,, A,clip(u: 1 e, + 6))
Policy loss: Lng LelCoi - [Txz(=i||=
Update f22 r based on gradient optimization. return To,
I User study
We conducted a user study involving 50 participants to evaluate the performance of AutoEdit. Each survey question presented side-by-side comparison between AutoEdit and the same editing method with two different sets of hyperparameters. The survey comprised a total of 30 questions. Overall, 82%78% of the responses favored the results produced by AutoEdit over the baseline, indicating clear human preference for the outputs generated by our method.
J Additional qualitative examples
We present additional qualitative examples comparing the editing baselines with and without AutoEdit in Figures TO
Furthermore, we compare AutoEdit against the same methods using different hyperparameter settings in Figures 14 and 15 Overall, AutoEdit achieves consistently strong editing results across various hyperparameter configurations, thereby significantly reducing the manual tuning effort required by practitioners. We also include the qualitative results of AutoEdit applying to the Flux-based editing [42 6 methods, as shown in Figure 13
12
18

---

Original image
DDIM-Inversion
DDPM-Inversion
P2P
PnP
MasaCtrl
yellow crochet sitting branch
sitting beads putting hands
AutoEdit
square cake with orange frosting wooden plate
AutoEdit
cat dog sitting wooden chair
AutoEdit
Figure 9: Additional qualitative samples
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---

Original image
DDIM-Inversion
DDPM-Inversion
P2P
PnP
MasaCtri
group of pink red flowers hanging from tree
AutoEdit
cartoon boy girl walking his dog the
AutoEdit
painting woman hold pink flowers teddy bear
AutoEdit
watercolor painting of a woman sitting in a living room
AutoEdit
Figure 10: Additional qualitative results
20

---

Original image
DDIM-Inversion
DDPM-Inversion
P2P
PnP
MasaCtrl
woman with gold blue makeup
Fishes Sharks the ocean
AutoEdit
A beautiful woman with garland hat on her head
AutoEdit
An orange cat play with black and white cat big pink yarn ball on the grass
Figure 11: Additional qualitative examples
21

---

Original image
DDIM-Inversion
DDPM-nnversion
P2P
PnP
MasaCtrl
tiger sitting
M
zebra grass field
Rile
black with tree head and branches his face and eyes closed
detailed painting of calm laughing beautiful woman with stars in her hair
Figure 12: Additional qualitative examples
22

---

closed eyes
grassy floor
hand
Ghibli anime style
AutoEdit
horse
forest
cartoon
grassy land
AutoEdit
blue
wearing crown
Ghibii anime style
looking at camera
AutoEdit
Figure 13: Qualitative Results on the Flux-based editing. Top row: The baseline with the default hyperparameter choice. Bottom row: AutoEdit performance. The prompt indicates the modification to the original image. This demonstrates the AutoEdit capability to reduce the running required to choose good hyperparameter.
23

---

Original image
10
20
30
AutoEdit
Around square with orange frosting on wooden plate
eat dog sitting wooden chair
blue light, black and white eat dog is playing with flowers
Figure 14:
Compare DDPM-Inversinn with different values of inversion timestep. general,
AutoEdit can achieve par or better performance without tuning the inversion timestep
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Original image
0.2
0.6
AutoEdit
round square cake with orange frosting on wooden plate
a eat dog sitting on a wooden chair
cup of coffee with drawing of tulip lion putted on the wooden table
stream road in a lush green forest with rocks
Figure 15: Compare P2P with different values of cross attention ratio. AutoEdit can be on par or better in terms of editing results without needing to tune the hyperparameters
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Reviews:
[{"review_id": "HRFRTg217y", "reviewer": "Reviewer_qdGc", "summary": "The 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.", "questions": "- 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", "limitations": "yes.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "Rebuttal provides extra results regarding the method's generalization issues, which was my primary concern, therefore, i raised my score to 4.", "rebuttal": "Thank you for your valuable review. We will respond to your concerns as follows:\n\n**Weakness 1**: The dependent of mask $M$ in the reward computation\n\nOur training objective is based on an editing metric. For non-masked editing tasks such as style transfer, the background preservation term (MSE) is excluded from the reward computation.\n\nFor global editing tasks (e.g., style transfer or style change), we use only the prompt alignment metric as the reward function. This choice aligns with the evaluation protocol used in Pie Bench, our benchmark dataset.\n\nAdditionally, we provide a comparison table highlighting our method outperforms the baseline in the style transfer task.\n| Base model |    Editing Method   | CLIP Score |\n|:----------:|:-------------------:|:----------:|\n| SDXL       | DDPM Inv            | 26.5       |\n| SDXL       | DDPM Inv + AutoEdit | **28.3**       |\n| SD 1.5     | P2P                 | 25.5       |\n| SD 1.5     | P2P + AutoEdit      | **26.7**       |\n\nThe experiment demonstrates the effectiveness of AutoEdit on the style transfer task.\n\n**Weakness 2**: The framework's reliance on the segmentation mask limits its effectiveness for very fine-grained attribute edits\n\nThe performance of image editing depends heavily on both the reward function and the capabilities of the base model. In this work, we propose a general reinforcement learning (RL) framework for hyperparameter selection in editing tasks. This framework is flexible and can accommodate various reward functions.\n\nWe acknowledge that relying solely on the CLIP score within the masked region may be insufficient for capturing fine-grained edits, such as changes in small or detailed regions (e.g., eye color). To address this limitation, we incorporate large language model (LLM)-based evaluation to assess editing quality. Specifically, we employ a multi-step approach using GPT-4o to generate and compute rewards as follows:\n\n- **Step 1**: Given an editing instruction (e.g., \"change the eye color to blue\"), we prompt GPT-4o to generate a corresponding question (e.g., yes/no or multiple-choice) along with a ground truth answer. For example: Question: \"What is the color of the eye?\" Choices: [\"black\", \"blue\"]; Answer: \"blue\".\n- **Step 2**:  To compute the reward, we query the LLM for both foreground editing and background preservation. For foreground editing, we use the question and ground truth answer from Step 1 and require GPT-4o to identify the correct choice. For background preservation, we ask the LLM to compare the edited image with the original and assign a score (0, 0.5, or 1) based on the quality of background consistency. The total reward is the sum of these two components.\n\nWe conduct experiments using SDXL with DDPM-Inversion, tuning the inversion timestep based on the newly introduced LLM-based reward. The following table presents a comparison between our original CLIP-based reward and the new LLM-based reward, with LLM score is the GPT-4o evaluation score, which is similar to the computation of the reward function.\n\n|          Base model          | Editing method | PSNR | SSIM |  MSE | LPIPS | CLIP (edited) | CLIP (whole) | LLM score |\n|:----------------------------:|:--------------:|:----:|:----:|:----:|:-----:|:-------------:|:------------:|:---------:|\n| SDXL                         | DDPM Inv       | 26.1 | 89.7 | 30.5 | 66.2  | **23.4**          | **27.1**         | 1.04      |\n| SDXL + AutoEdit              | DDPM Inv       | 27.8 | 90.5 | 20.4 | 53.5  | 23.1          | 26.9         | 1.15      |\n| SDXL + AutoEdit + LLM reward | DDPM Inv       | **29.4** | **92.2** | **19.1** | **44.1**  | 22.9          | 26.7         | **1.20**      |\n\nOur AutoEdit framework consistently improves both background preservation metrics and LLM-based judgment scores, while exhibiting only a slight decline in CLIP score. We argue that this minor decrease in CLIP score does not compromise the overall editing performance of AutoEdit.\n\n**Weakness 3**: Concerns about the SD 1.4 model:\n\nWe conduct experiments using SDXL with DDPM-Inversion, tuning the inversion timestep accordingly. The table below presents a comparison between Auto-Edit and baseline methods on the SDXL base model:\n\n|       Method      | PSNR | SSIM |  MSE | LPIPS | CLIP Score (edited) | CLIP score (Whole) | LLM score |\n|:-----------------:|:----:|:----:|:----:|:-----:|:-------------------:|:------------------:|:---------:|\n| SDXL              | 26.1 | 89.7 | 30.5 | 66.2  | **23.4**                | **27.1**               | 1.04      |\n| SDXL + AutoEdit   | **27.8** | **90.5** | **20.4** | **53.5**  | 23.1                | 26.9               | **1.15**      |\n| SD 1.4            | 22.6 | 78.9 | 53.3 | 67.6  | **23.0**                | **26.2**               | 1.01      |\n| SD 1.4 + AutoEdit | **27.2** | **85.2** | **31.1** | **50.5**  | 22.5                | 25.8               | **1.10**      |\n\nOur results demonstrate that AutoEdit significantly improves background preservation quality, with only a slight reduction in CLIP score. However, we argue that this minor decrease in CLIP score does not substantially impact the overall editing quality. Furthermore, evaluations using large language models (LLMs) indicate that AutoEdit considerably enhances the editing capabilities of baseline methods.\n\n**Weakness 4**:  Concern about the limited set of editing methods.\n\nThank you for your suggestion. The baseline you mentioned pertains to image composition tasks, which follow a different setup compared to image editing, the primary focus of our work. Nonetheless, our proposed RL framework is general and can be readily applied to diffusion-based image composition tasks for hyperparameter optimization.\n\nFor example, in the TF-ICON paper, our RL framework can be parameterized to optimize the injection timesteps of the composite self-attention maps, denoted as $\\tau_A$ and $\\tau_B$—a set of hyperparameters explicitly defined in the method. The reward function can be constructed based on the quality of foreground integration and background preservation in the composited image.\n\nWe also conduct experiments on a recent editing method [1]. In this setting, we leverage our RL framework to determine the optimal attention injection timestep. The performance of Auto-Edit under this configuration is presented in the following table.\n\n|         Method         | PSNR | SSIM | CLIP Score (edited) | CLIP score (whole) | LLM score |\n|:----------------------:|:----:|:----:|:-------------------:|:------------------:|:---------:|\n| Taming Flow            | 23.4 | 81.5 |**22.9**                | **26.0**               | 1.02      |\n| Taming Flow + AutoEdit | **26.1** | **85.4** | 22.7                | 25.6               | **1.13**      |\n\nOur experiments demonstrate that Auto-Edit enhances the editing performance of the baseline methods. Specifically, we observe improvements in the LLM-based evaluation metric and background preservation scores. While there is a slight decrease in the CLIP score, this suggests that our method effectively discovers hyperparameters that balance foreground fidelity and background consistency, resulting in overall improved editing quality.\n\n**Question 1**: How can it be integrated into other image editing tasks (such as shape changes, local attribute editing, etc.)?\n\nOur method is applicable to a wide range of editing tasks due to the flexibility of its reward function. For instance, the CLIP score can be used as a reward for style transfer tasks, while LLM-based evaluation can be employed to assess correctness in local attribute editing.\n\nIt is important to note that certain challenging editing tasks may also require stronger base models and novel editing techniques. Our approach focuses on the overarching editing problem by selecting optimal hyperparameters for each editing method (see Fig. 3 and Table 1 in our main paper).\n\n**Question 2**: What about its performance on various models, such as different versions of SD, and how are they compared to more recent baselines (such as TF-ICON, IMPRINT, MIGC)?\n\nPlease refer to the response to weakness 4.\n\n[1]Taming Rectified Flow for Inversion and Editing (ICML 2025)."}, {"review_id": "lw2PKzJVBn", "reviewer": "Reviewer_j63C", "summary": "This 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.", "questions": "See weakness.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength:\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.", "was_revised": true, "final_justification": "The rebuttal addresses my concerns. As a result, I would like to maintain the score as borderline accept.", "rebuttal": "Thank you for your valuable review. We will respond to your concerns as follows:\n\n**Weakness 1**: Training time of AutoEdit\n\nFor all experiments in the main paper, the total training time was approximately 10 hours on NVIDIA A6000 GPUs, covering both Stage 1 and Stage 2 training.\n\nFor the additional experiments with SDXL conducted during the rebuttal phase, we use NVIDIA A100 GPUs with a total training time of approximately 12 hours.\n\n**Weakness 2**: Concern about the mask for background preservation for the global editing task (style transfer)\n\nFor the global editing task, we only employ the CLIP score as the reward function, which is similar to the evaluation protocol of the PieBench dataset. We present the results of the style transfer task in the following table:\n\n| Base model |    Editing Method   | CLIP Score |\n|:----------:|:-------------------:|:----------:|\n| SDXL       | DDPM Inv            | 26.5       |\n| SDXL       | DDPM Inv + AutoEdit | **28.3**       |\n| SD 1.5     | P2P                 | 25.5       |\n| SD 1.5     | P2P + AutoEdit      | **26.7**       |\n\nFrom this table, our proposed AutoEdit can improve the editing results on the Style Transfer task compared to the baseline model.\n\n**Weakness 3**: Lack of editing on recent methods\n\nThank you for your suggestion. Due to the time constraint, we are only able to perform AutoEdit for the Rf-solver. In this setting, we leverage our RL framework to determine the optimal attention injection timestep. The performance of Auto-Edit under this configuration is presented in the following table:\n\n|         Method         | PSNR | SSIM | CLIP Score (edited) | CLIP score (whole) | LLM score |\n|:----------------------:|:----:|:----:|:-------------------:|:------------------:|:---------:|\n| Taming Flow            | 23.4 | 81.5 | **22.9**                | **26.0**               | 1.02      |\n| Taming Flow + AutoEdit | **26.1** | **85.4** | 22.7                | 25.6               | **1.13**      |\n\nOur proposed AutoEdit can improve the performance of Taming Flow on background preservation and LLM score, where LLM score is the evaluation of the editing quality provided by a GPT-4o model with a slight decrease in terms of CLIP score. However, we argue that the small decrease of CLIP score does not affect the  editing capability of AutoEdit."}, {"review_id": "T3PH7i9Hf9", "reviewer": "Reviewer_opyq", "summary": "This 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.", "questions": "Can 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.", "limitations": "Please see the weaknesses and questions.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Pros:\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.", "was_revised": true, "final_justification": "After reading the rebuttal from the authors, there are some issues that have not been addressed, especially the motivation, the lack of related works and comparative experiments. Hence, I lean towards maintaining my original rating.", "rebuttal": "Thank you for your valuable review. We will respond to your concerns as follows:\n\n**Weakness 1**: Concern about the limited search space\n\nWe acknowledge that hyperparameters can be selected from a predefined range. However, for different images, it is still necessary to identify the optimal value within that range. When dealing with a large number of images, this selection process can become time-consuming, even within a constrained range. Moreover, the editing results can vary significantly depending on the specific values chosen within that range. The complexity analysis is based solely on theoretical assumptions. Empirically, we also provide an experiment (see Table 4 in the main paper), which shows that, in practice, our proposed AutoEdit method is approximately equivalent to just three rounds of trial-and-error using the conventional approach.\n\n**Weakness 2**: Concern about the reward function for several editing tasks\n\n- For style transfer editing, we omit the background preservation terms in our reward computation, consistent with the evaluation protocol of the Pie-Bench dataset.\n- Regarding viewpoint changes, this task is more akin to 3D reconstruction than image editing, and thus falls outside the scope of our work. In most viewpoint-change scenarios, 3D-aware diffusion models or score distillation methods are preferred. In such cases, reinforcement learning could potentially be integrated into the training process to produce more consistent viewpoint generation.\n\n**Weakness 3**: AutoEdit with training-based approaches:\n\nTraining-based editing methods can be regarded as specialized generation pipelines that condition on both the original image and the instruction prompt, rather than relying solely on text-to-image models. For example, in [1] and [2], our reinforcement learning framework can be employed to optimize the classifier-free guidance (CFG) coefficient during the denoising process.\n\n**Weakness 4**: CLIP score function is not a reliable measure\n\nWe acknowledge that relying solely on the CLIP score may be insufficient for capturing fine-grained edits, such as changes in small or detailed regions (e.g., eye color). To address this limitation, we incorporate large language model (LLM)-based evaluation to assess editing quality. Specifically, we employ a multi-step approach using GPT-4o to generate and compute rewards as follows:\n\n- **Step 1**: Given an editing instruction (e.g., \"change the eye color to blue\"), we prompt GPT-4o to generate a corresponding question (e.g., yes/no or multiple-choice) along with a ground truth answer. For example: Question: \"What is the color of the eye?\" Choices: [\"black\", \"blue\"]; Answer: \"blue\".\n- **Step 2**: To compute the reward, we query the LLM for both foreground editing and background preservation. For foreground editing, we use the question and ground truth answer from Step 1 and require GPT-4o to identify the correct choice. For background preservation, we ask the LLM to compare the edited image with the original and assign a score (0, 0.5, or 1) based on the quality of background consistency. The total reward is the sum of these two components.\n\nWe conduct experiments using SDXL with DDPM-Inversion, tuning the inversion timestep based on the newly introduced LLM-based reward. The following table presents a comparison between our original CLIP-based reward and the new LLM-based reward, with the LLM score being the GPT-4o evaluation score, which is similar to the computation of the reward function:\n\n|       Method      | PSNR | SSIM |  MSE | LPIPS | CLIP Score (edited) | CLIP score (Whole) | LLM score |\n|:-----------------:|:----:|:----:|:----:|:-----:|:-------------------:|:------------------:|:---------:|\n| SDXL              | 26.1 | 89.7 | 30.5 | 66.2  | **23.4**                | **27.1**               | 1.04      |\n| SDXL + AutoEdit   | **27.8** |**90.5** | **20.4** | **53.5**  | 23.1                | 26.9               | **1.15**      |\n| SD 1.4            | 22.6 | 78.9 | 53.3 | 67.6  | **23.0**                |**26.2**               | 1.01      |\n| SD 1.4 + AutoEdit | **27.2** |**85.2** | **31.1** | **50.5**  | 22.5                | 25.8               | **1.10**      |\n\nExperimental results show that our method consistently enhances the performance of baseline approaches in terms of background preservation metrics, while exhibiting only a slight decline in CLIP score. We argue that this minor reduction does not compromise the overall editing capability. Furthermore, evaluations using GPT-4o indicate that our proposed method effectively improves the editing quality over the baseline.\n\n**Question 1**: Can the method be applied to other models like DiT-based image generation models, since Stable Diffusion 1.4 is limited in image generation quality?\n\nThank you for your thorough review. Our framework can be extended to DiT-based image generation models. Specifically, we conduct an experiment using [3], where Auto-Edit is employed to automatically select the optimal attention injection timestep. The results are presented in the following table:\n\n|         Method         | PSNR | SSIM | CLIP Score (edited) | CLIP score (whole) | LLM score |\n|:----------------------:|:----:|:----:|:-------------------:|:------------------:|:---------:|\n| Taming Flow            | 23.4 | 81.5 | **22.9**                |**26.0**              | 1.02      |\n| Taming Flow + AutoEdit | **26.1** | **85.4** | 22.7                | 25.6               | **1.13**      |\n\nOur proposed AutoEdit can improve the performance of [3] on background preservation and LLM score, where LLM score is the evaluation of the editing quality provided by a GPT-4o model with a slight decrease in terms of CLIP score. However, we argue that the small decrease of the CLIP score does not affect the editing capability of AutoEdit.\n\n[1]InstructPix2Pix: Learning to Follow Image Editing Instructions\n\n[2] SmartEdit: Exploring Complex Instruction-based Image Editing with Multimodal Large Language Models\n\n[3]Taming rectified flow for inversion and editing, ICML 2025."}, {"review_id": "DPQviWdGsl", "reviewer": "Reviewer_MdPi", "summary": "The 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.", "questions": "1. 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.", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:  \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.", "was_revised": true, "final_justification": "The author solved my main problem, and I raised the score to 4", "rebuttal": "Thank you for your valuable review. We will respond to your concerns as follows:\n\n**Weakness 1**: Notation confusion\n\nThank you for your detailed review. In Figure 1, the inversion timestep should be replaced by $r$ in DDPM Inversion; however, $r$ is cross cross-attention ratio in the P2P method. We will correct Figure 1 and provide a clearer notation in the revised paper.\n\n**Weakness 2**: Suboptimal timestep in the Fig. 1\n\nThank you for your valuable feedback. In editing tasks, the perception of the \"best\" edited image can be subjective and may vary from person to person. We selected that particular image as the optimal result based on a user survey, in which the majority of participants preferred it over the image generated at timestep t=30.\n\n**Weakness 3**: Reconstruction problem in DDIM Inversion:\n\nOur proposed framework does not aim to solve the reconstruction error problem inherent to inversion methods, as this is closely tied to the design of the inversion process itself. Instead, we focus on enhancing the quality of edited images within a given inversion-based editing method by selecting optimal hyperparameters. Consequently, in the case of DDIM-Inversion editing, our approach achieves improved background preservation performance (see Table 1).\n\n**Weakness 4**: Minor issues\n\nThank you for your detailed review. We will address the issue in line 90 and include the PPO preliminaries in the revised version of the paper. Additionally, we will revise the redundant terms to improve clarity and reduce potential confusion for readers.\n\n**Question 1**: 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.\n\nWhile DDIM inversion does introduce reconstruction errors, selecting a more suitable editing start time $r$ for each specific case can help better preserve background information while still achieving high editing quality. Our reinforcement learning framework assists in automatically selecting the optimal hyperparameters to effectively balance the trade-off between background reconstruction and prompt alignment.\n\n**Question 2**: 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):\n\nThank you for your feedback. We also conducted an experiment using AutoEdit to adaptively search for the optimal CFG scale at each timestep. The results are presented in the following table, with the baseline's default CFG scale set to 7.5.\n\n|    Base model   | Editing method | PSNR | SSIM |  MSE | LPIPS | CLIP (edited) | CLIP (whole) | LLM metrics |\n|:---------------:|:--------------:|:----:|:----:|:----:|:-----:|:-------------:|:------------:|:-----------:|\n| SDXL            | DDPM Inversion | 26.1 | 89.7 | 30.5 | 66.2  | **23.4**          | **27.1**         | 1.04        |\n| SDXL + AutoEdit | DDPM Inversion | **29.9** | **92.2** | **15.1** | **26.3**  | 22.6          | 26.4         | **1.15**        |\n\nExperimental results show that our method significantly improves background preservation compared to the baseline, while only sacrificing a small amount of CLIP score. Moreover, our proposed AutoEdit framework boosts LLM-based evaluation metrics—where editing performance is assessed through the judgment of a large language model on the edited images—demonstrating the effectiveness of AutoEdit in searching for optimal editing hyperparameters.\n\n**Question 3**: 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\nPlease refer to the answer to weakness 2."}]

Author rebuttal:

Meta-review: This paper presents AutoEdit, a RL framework to automate hyperparameter tuning in image editing. This method avoids slow manual tuning by treating the problem as a sequential decision task. The core idea seems novel. However, the initial submission had some weaknesses. It relied on segmentation masks, which limits its use. The experiments were also tested on older models, e.g. SD1.4. 

During the discussion, reviewers raised important points. Reviewers qdGc, j63C, and opyq questioned the method's generalizability to different models and editing tasks. They also pointed out the limitations of using a CLIP-based reward function. In response, the authors provided many new experiments. They showed AutoEdit works with newer models like SDXL and DiT architectures, and on more tasks like instruction-based editing. They also introduced an LLM-based reward system. These new results convinced most reviewers. After reading all discussions, I generally agree with the reviewers and recommend accepting the paper, as the authors thoroughly addressed the reviewers’ concerns. Please incorporate the SDXL and SD3 results into the revision.
Decision: Accept (poster)