Multi-Reward as Condition for Instruction-based Image Editing
Authors: Xin Gu
Ming Li
Libo Zhang
Fan Chen
Longyin Wen
Tiejian Luo
Sijie Zhu
Venue: ICLR 2025 Poster (2025)
Keywords: Instruction-based Image Editing
Primary area: generative models

Abstract: High-quality training triplets (instruction, original image, edited image) are essential for instruction-based image editing. Predominant training datasets (e.g., InsPix2Pix) are created using text-to-image generative models (e.g., Stable Diffusion, DALL-E) which are not trained for image editing. Accordingly, these datasets suffer from inaccurate instruction following, poor detail preserving, and generation artifacts. In this paper, we propose to address the training data quality issue with multi-perspective reward data instead of refining the ground-truth image quality. 1) we first design a quantitative metric system based on best-in-class LVLM (Large Vision Language Model), i.e., GPT-4o in our case, to evaluate the generation quality from 3 perspectives, namely, instruction following, detail preserving, and generation quality. For each perspective, we collected quantitative score in $0\sim 5$ and text descriptive feedback on the specific failure points in ground-truth edited images, resulting in a high-quality editing reward dataset, i.e., RewardEdit20K. 2) We further proposed a novel training framework to seamlessly integrate the metric output, regarded as multi-reward, into editing models to learn from the imperfect training triplets. During training, the reward scores and text descriptions are encoded as embeddings and fed into both the latent space and the U-Net of the editing models as auxiliary conditions. During inference, we set these additional conditions to the highest score with no text description for failure points, to aim at the best generation outcome. 3) We also build a challenging evaluation benchmark with real-world images/photos and diverse editing instructions, named as Real-Edit. Experiments indicate that our multi-reward conditioned model outperforms its no-reward counterpart on two popular editing pipelines, i.e., InsPix2Pix and SmartEdit. Code is released at https://github.com/bytedance/Multi-Reward-Editing.

Published as a conference paper at ICLR 2025
MULTI-REWARD AS CONDITION FOR INSTRUCTION- BASED IMAGE EDITING
Xin Gu 1:21 Ming Li' Libo Zhang Fan Chen' Longyin Wen' Tiejian Luo² Sijie Zhu it ByteDance Inc. University of Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences guxin21 1ea221maaee ucas. edu.cn
ABSTRACT
High-quality training triplets (instruction, original image, edited image) are es- sential for instruction- braree image editing. Predominant training datasets (e.g.. InsPix2Pix) are created using text-to-image generative models (e.g.. Stable Diffu- sion, DALL--E) which are not trained for image editing. Accordingly, these datasets suffer from inaccurate instruction following, poor detail preserving, and generation artifacts. In this paper, we propose to address the training data quality issue with mulli-perspective reward data instead of refining the ground-truth image quality. I) we first design quantitative metric system based on best-in-class LVLM (Large Vision Language Model), i.e., GPT-4o in our case, to evaluate the generation quality from 3 perspectives, namely, instruction following, detail preserving, and genera- tion quality. For each perspective, descriptive feedback on the specific failure points in ground-truth edited images, resulting a high-quality editing reward dataset, i.c., RewardEdit20K. 2) We fur- ther proposed a novel training framework to seamlessly integrate the metric output, regarded as multi-reward, into editing models learn from the imperfect training triplets. During training, the reward scores and text descriptions are encoded as embeddings and fed into both the latent space and the U-Net of the editing models auxiliary conditions. During inference, set these additional conditions to the highest score with no text description for failure points, to aim at the best generation outcome. 3) We also build challenging evaluation benchmark with real-world images/photos and diverse editing instructions, named Real-Edit. Experiments indi- cate that our multi-reward conditioned model outperforms its no-reward counterpart on two popular editing pipelines, i.e., InsPix2Pix and SmartEdit. Code released at htttps://ggtti com wtt..//yyyeeeee///eeeeeo Multi Reward Editing
collected quantitative score in 0 5 and text
INTRODUCTION
Text instruction-basee image editing provides natural way for general users to express their requests and customize their assets easily. Predominant state-of-the-ard methods for instruction- image
editing (Brooks et al.) 2023 Zhhang et al.] 2024b|c HHang et al. 2024) follow data-driven pipeline to finetune pre-trained diffusion models (Rombach al. 2022 Ren et al., [025 Peng et al. 2024]
Ye et al. 2025) with editing data triplets, i.e., (instruction, original image, edited image). Creating high-quality dataset of the above triplets is thus essential for successful model training.
Predominant state-of-the-art methods for instruction baasee image editing (Brooks et al. 2023 Zhang et al. 2024b|c c; Huang et al. 2024: Ho & Salimans (022) follow a data-driven pipeline to create the editing triplets, from which they build dataset to fine-tune pre-trained diffusion model (Rombach) et al. 2022). The most widely used InsPix2Pix (Brooks et al. (023) dataset is created with a pre- trained text-to-image Stable Diffusion (SD) model (Rombach et al. 2022), Prompt-to-Pree pooompp (Hertz et al. 2022) and a fine-tuned GPT-3 (Brown) 2020 The dataset can easily scale up to 300k triplets
Corresponding author, sijiezhu @ betebyydane.oom
This work done during the first author's intersship at ByteDance

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Published as a conference paper at ICLR 2025
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(b) Our method adds multi-perspective reward as an input condition to guide editing model.
Figure 1: Existing image editing datasets and our method. Best viewed with zoom-in.
but the quality is unsatisfactory from three perspectives, i.e., instruction following. detail preserving, and generation quality. 1) Instruction following means that the model needs closely and accurately follow the editing request, which regard as the most important factor in instruction-based image editing. Since the SD model was originally trained for image generation tasks, it might fail to apply the correct editing action to the edited image. As shown in Fig. (a), the text instruction is "make the glasses green" but the glasses in the ground-truth edited image are not green, which does not follow the major editing instruction. 2) Detail Preserving indicates how the model preserves identity, background or any other details that are not meant to be changed in the editing instruction. InsPix2Pix adopts tonppt----prmppt to generate edited images which could contain undesired modifications the edited images. For example, the instruction of the first case in Fig. (a) is to edit the color of the glasses, but the color of the clothes and background is also changed in the ground-truth edited image, which could lead to wrong supervision. 3) Generation Quality represents the relative quality of edited images compared to the input images, i.c., to determine whether the editing action introduces quality degradation like artifacts to the real-world input images. It is common for SD models to generate artifacts, especially for images with human small objects. In the third case of Fig. [ (a), the generated "giant squid" in the ground-trute image has serious artifacts (viewed with zoom-in).
MagicBrush (Zhang et al. 2024b) leverages a more powerful text-to-image model (i.e., DALL-E 2) and human workers improve the training data quality on relatively small scale. The background preserving is significantly improved due to mask-based editing. However, for the edited regions inside the mask, the edited image may contain undesired modification or generation artifacts due to occlusion or small objects (see example 5,6 in Fig. DT. HQEdit (Hui et al.) (024) adopts GPT-4V (gpt. (b) and DALL-E (dal) to improve the instruction and generation quality. However, the edited images are usually significantly modified on the regions that are not included in the editing instruction, leading to poor detail preserving on background or identity (see example 7,8 in Fig. [ Hive (Zhang al. 2024c) follows the same procedure of InsPix2Pix create training data triplets, thus having similar ar quality as InsPix2Pix. relatively small-scale human feedback dataset is collected to improve the overall quality of the editing model, but it does not have detailed feedback information for the three perspectives of editing (i.e., following, preserving, and quality). In a nutshell, the majority of training samples in existing datasets remain noisy which could lead inaccurate supervision.
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Published as a conference paper at ICLR 2025
In this paper, we propose to rectify the inaccurate supervision from a different perspective. i.e., introducing perspeeter reward as an auxiliary input condition. 1) Instead of directly refining the quality of ground-truth edited images, we evaluate the training data triplets from three perspectives (i.e., instruction following, detail preserving, generation quality) with GPT-4o (gpt, a to generate scores on a of 0 ~ 5 and text description for unsatisfactory points. With proper prompt engineering, the generated reward/feedback is mostly aligned with humans. We collect 20k multi multi-perspective reward data in total for training, namely RewardEdit-20K. Examples of the scores and text description reward are included in Fig. (b). 2) To integrate reward information into the existing instruction based image editing framework, we first encode the reward score and reward text description separately as embeddings, and then concatenate them to obtain the reward condition. This reward condition is then integrated into the latent noise through an attention mechanism. To further enhance the guidance provided by the reward information, we also feed the reward condition into the U-Net (Ronneberger ct al., 2015) of the SD model. 3) To evaluate the editing models on real-world photos and diverse instructions covering major 7 categories (defined in Sec. [5) create evaluation set with 80 high-quality Unsplash (uns) photos and 560 challenging instructions, which are initially generated by GPT-40 and verified by human annotators. We evaluate the model output from the three perspectives with GPT-4o in terms of yes/no accuracy and score from 0 ~ 5. We also conduct human evaluation with 0 ~ 5 score from three perspectives to further verify the results. Experiments show that the proposed method can be combined with InsPix2Pix and SmartEdit with significant performance improvement.
We summarize the contributions follows: The RewardEdit-20k dataset with multi celti-perspecttve reward data to address the limitations of existing image editing datasets. A novel framework to effectively integrate merti-perspecctve reward information image editing. A real-world image editing evaluation benchmark Real-Edit and introduced a GPT-4o-based image editing evaluation method. + Extensive experiments showing that the proposed method can be combined with existing editing models with a significant performance boost on all three perspectives, achieving ofte-of-theear art performance for both GPT-4o and human evaluation.
additional condition to guide
RELATED WORK
2.1 INSTRUCTION-BASED IMAGE EDITING
Recent instruction-based image editing methods ęZhang al. 2024b Peng et al., 2025 Zhang
et al. 2024c Guo et al. 2024 Gu et al. (024) rely primarily on pre-trained text-to-image diffusion models. These methods leverage the powerful generative capabilities of these models and their understanding of textual descriptions to perform image editing. InsPix2Pix (Brooks et al. (023), as a pioneering work, constructed a large-scale image editing dataset and successfully used instructions to edit images based on the stable diffusion model. MagicBrush (Zhang et al.) 2024b) addressed the issue of unrealistic images in InsPix2Pix by creating a manually annotated dataset to achieve realistic image editing. SmartEdii (Huang et al.) 2024) addressed the limitation of InstructPix2Pix in handling only simple instructions by employing LLav (Liu et al., 2024) to comprehend complex instructions. HIVE (Zhang al.) 2024c) proposed to utilize human feedback to optimize image editing models, aligning them with human preferences. However, the major training data still has a similar quality as the InsPix2Pix dataset, and the noisy supervision problem remains unaddressed.
2.2 REWARD MECHANISM FOR DIFFUSION MODELS
Inspired by the success of reward fine-tuning in large language models (Rafailov et al. 2024 Han et al. 2024 Zhang et al.) 2024a), a series of works have attempted to directly optimize reward model scores (Xu et al. 2024 Fan et al., 2024) or human preference rankings (Wallace et al. 2024 Liang ct al. 2024 to align texl-to-image diffusion models, thus improving the quality, aesthetics, and text- image alignment of the generated images. For text-to- -i-age,, Pony Diffusion employs a Caaed aesthetic ranking method to generate reward scores improve the quality of generated images. For image editing, ByteEdit (Ren et al. 2024) customizes a reward model specifically for inpainting and outpainting editing tasks to identify the consistency of images beyond the mask area before and after
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Published as a conference paper at ICLR 2025
editing. HIVE (Zhang et al. 2024c) trains reward model to generate single reward score for each edited image. The scores are then combined with text instructions and encoded via CLIP (Radford al. 202 to improve editing performance. However. there multiple perspectives determine the quality of an edited image given an input image and instruction, which cannot be covered by one single reward score. Also, adding the reward scores into the text instruction does fully exploit the reward information, as the CLIP text encoder is not sensitive to numbers. It remains challenging to effectively integrate peste-perspective reward information into existing image editing frameworks.
REWARDÉDIT-20K: A MULTI-REWARD DATASET FOR IMAGE EDITING
Collection Process. this section, we discuss our procedure for collecting the Reward dataset. First, randomly selected 20K training triplets from the InsPix2Pix dataset, where each triplet contains an original image, an edited image, and an editing instruction. Then, we used GPT-4o, setting up three types of prompts based on instruction following, detail preserving. and generation quality. GPT-4o was asked to perform evaluations on these three aspects for each triplet. Finally, obtained 20K reward data consisting of reward scores and reward texts. The reward collection process is illustrated in Fig. Due to limited space, we only show the core prompts in the figure, while the complete prompts are provided in the appendix.
Please evaliante whether the edited image follows the instruction. Based on your evaluation, answer the two questions:
Score: 3
Text: There should not be vegetation desert.
Provide your evaluation
accuracy
GPT-4o
2. Describe the aspects of the edit that were not followed well.
Prompt of Following
Following Reward
Original Image
Please evaluate whether the non-edited areas of the edited imuuee consistent with original image. Based on your evaluation, answer the two questions:
Score: 3
malles alsaat
Text: People the road have significantly changed.
Provide your evaluation as a consistency score.
Instruction
Describe where unintended changes occurred the edit.
Prompt of Preserving
Preserving Reward
Please evaluste the edited image based on overall quality and the
Score:
natural appearance. Based your evaluation, questions:
Text: There
noticeable negutive aspects.
Provide your evaluarion as a quality soore.
GPT-40
Edited Image
Deseribe the aspocts that negatively affeet image quality. Prompt Quality
Quality Reward
Figure 2: Generation process of reward data. Given the editing triplets, reward data generated using GPT-4o by setting prompts from different perspectives.
Data statistics. We summarize the statistics of the reward data. Fig shows the distribution of the reward scores, revealing that in all three aspects, samples with scores less than 5 exceed 50%, indirectly indicating that the majority of training samples in the InsPix2Pix dataset remain noisy. Juses word clouds to display the most frequent words in the reward texts. These words reflect the main issues present in the original dataset. For example, the high frequency of 'executed" and "poorly' in the mnstruction-followiin aspect indicates failures in following instructions, 'unintended" and 'change' in the preee--pres aspect reflect inconsistencies in non-edit areas, and 'lighting' and 'shadow" in the quality aspect highlight quality issues in the edited images.
METHODOLOGY
Overview. In this section, first introduce the most general image editing framework (Sec/4.1). Then, we present our framework that uses melti-reward as an input condition (.2c..2)). Finally, we offer detailed explanation of the multi-reward condition module (Sec. 4.3
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Figure 5: The overall framework of our approach. The original image is first encoded into image condition by the VAE encoder. This image condition çj is then concatenated with latent noise Z₃ serve as the query for the reward encoder, with the reward condition eR the key/value. The resulting latent noise, containing reward information, is used as the input for the U-Net module. Meanwhile, the instruction is encoded into text condition er by the text encoder, which fed into each block of the U-Net. To further enhance reward guidance, we incorporate the reward condition after each block. Finally, the U-Net's output decoded by the VAE decoder into the edited image y.
4.1 PRELIMINARY: GENERAL IMAGE EDITING FRAMEWORK
InsPix2Pix (Brooks al. 2023). one of the pioneering works in the field of instruction-baan image editing, can edit images according to the given instructions. Specifically, given the original image æ, the text instruction and the edited image y. first use the VAE encoder to extract the encoded latent and original image conditioning text encoder to extract the text condition cr- Through the diffusion process, noise added to to generate latent noise 2,, where the noise level increases over timesteps E T. Then, train network that predicts the noise added the noisy latent 2, given the original image conditioning ey and the text instruction conditioning eT. The specific objective of latent diffusion is as follows:
that is, E(y), = E(x). Similarly, use the
LinsPie2Px = B2,0, or..rrree (0. ll <a(t, concat 2,, (r),er))) er)
where is the unscaled noise, is the sampling timestep, 2; is latent noise at step t. SmartEdit (Huang al. 2024), the state-of-the-art instruction-basee image editing model, uses the same architecture as InsPix2Pi but upgrades text encoder from CLIP (Radford et al. 2021b to LLaVA (Liu et al. 2024).
Although methods like InsPix2Pix and SmartEdit have shown compelling results in image editing, they are still affected by noise present in the training data, thus limiting their performance. To address this, we propose using multi celti-perspectiveee rewards as an additional condition to correct the bias introduced by the training data.
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Published as a conference paper at ICLR 2025
4.2 MULTI-REWARD INPUT CONDITION
We adopted an architecture similar to InsPix2Pix and SmartEdit. On this basis, to utilize rewards to guide the model, we designed a multi-reward condition (MRC) module to extract the reward condition and used reward encoder integrate the reward condition into the diffusion process. Additionally, to further enhance the guidance of reward information, we also incorporated the reward condition after each block in the U-Net module. The framework shown in Fig. Given the original image æ, the edited image y is generated under the guidance of the instruction text and the reward data. First, the VAE encoder to extract the latent representation ey of the original image. As in InsPix2Pix, concatenate with latent noise Ze and fuse them through convolution to obtain Zi. Then, we use the proposed MRC module generate a reward condition CR (Details in Sec. 4.3) To utilize the reward condition to guide image editing, we integrate the reward condition en into the encoded latent noise through a reward encoder, which consists of standard transformer encoder block (Vaswani 2017). Specifically, let latent noise Zi serve query and reward condition key/value, this process can be expressed as follows,
Z CA CA Linear;(cn))
where CA u) denotes the transformer encoder block with z generating query and u is the key/value. Linear:() (-) denotes linear projection, which aligns the dimension of the reward condition with the latent noise. To further enhance the guidance of the reward information, condition after each block in the U-Net module. The input to the i block in U-Net is as follows,
also add the reward
UB (2;-1) + Linearz(cR)
where UB,-1(-) (-) denotes i-1 th the blocks in U-Net. Linear2() (+) aligns the dimension of the reward condition with the U-Net. After that, the output the U-Net module is fed into the VAE decoder to generate the edited image y. The specific process can be formulated
Leward
ca(t, concat concat(2,,cil.eer.com cx))
(4)
4.3 MUL.TIREEWAR CONDITION MODULE
We an additional reward condition en from the MRC module to guide the model in generating the desired edited image. The MRC module is responsible for generating the reward condition from the reward text and reward score. For the reward text, the text encoder in Stable Diffusion model to extract text embeddings E,, as follows:
(5) where Ty, Ty: T, are the reward text in terms of following, preserving, and quality, respectively. For the reward scores, we use absolute positional encoding /Vaswani] 2017), which utilizes sine and cosine functions to convert the scores into vectors, and then extract embeddings E,, with an MLP module. The process mentioned above is represented as:
E. Encodertaxt Encoderr..reConnat/T.. Tp, Ty))
Eg
(6)
where Sp, S, are the reward scores terms of following, preserving, and quality, respectively. MLP(-) and PE(-) denote the MLP module and position encoding. Finally, concatenate the text embedding and the score embedding, and add the type embedding to obtain the reward condition en-
5 EVALUATION BENCHMARK AND METRICS
Evaluation Data. To more comprehensively evaluate the model's ability to edit real images based on instructions, we constructed a new image editing evaluation benchmark, Real-Edit, using real- world images. Compared to existing evaluation benchmarks, our proposed test set includes higher-quality images and a greater variety of editing instructions. We first carefully selected 80 high-quality images from the Unsplash website the original images. The categories of these images shown in Fig Then, using GPT-4o, we generated different editing instructions for each image based on its content, including local, remove, add, texture, background, global, and style edits, as shown in Fig 7

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Published as a conference paper at ICLR 2025
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types of editing instructions
Figure 6: Distribution different categories of images in Real-Edit.
Figure 7: An example in Real-Edit.
Please evaluate whether the edited image follows the instruction.
Rased on your evaluation, answer the two questions: edited image the editing instructions? 2. Provide evaluation image edit accuracy score.
Following: Yes Score:
GPT-4o
Original Image
Prompt of Following
Please evaluate whether the non-edited arcas of the edited image consistent with the original image. Based on your evalution, answer two questions:
Chalgge the baokgrownd
Preserving:
keeak soone.
1. Is the unedited area of the edited image consistent with the original?
Score:
Instruction
GPT-4o
2. Provide your evaluation as consistency score.
Prompt of Preserving
Please evaluste edited image based overall quality natural appearance. Based on your evaluation. answer the two
Quality: Yes S
questions:
1. the image clear, free of artifacts, well-composed, and natural?
Score: 4
Edited Image
GPT-40
2. Provide your evaluation as quality score.
Prompt of Quality
Figure 8: Evaluation process. The generated edited image, original image, and instruction are input into GPT. Three prompts are designed to evaluate from three different aspects. For each aspect, determine whether the criteria are met and assign score (ranging from 0 to 5).
Evaluation Metrics. To more accurately evaluate the performance of the editing model, we used GPT-4o evaluate the edited images based on the original images and instructions. The evaluation is conducted from three perspectives follows: (1) Following: Determine whether the edited image has been modified according to the editing instructions. (2) Preserving: Evaluate whether the non-edited aspects of the original and edited images remain consistent. (3) Quality: Focuses the overall quality of the edited image compared to the input image, including aspects such as clarity, composition, and lighting. The detailed evaluation process is illustrated in Fig. 8 Due to limited space, only show the core prompts in the figure, while the complete prompts are provided in the appendix. In the later quantitative results, we have supplemented each edited image with evaluation scores.
EXPERIMENTS
6.1 IMPLEMENTATION DETAILS.
Our method is implemented in Python using PyTorch. The MRC module, reward encoder, and the connected linear layer are randomly initialized. All other modules are initialized from the pre-trained InsPix2Pix model (Brooks al. 2023). During training, we only optimize the MRC module, the U-Net module, the reward encoder, and connected linear layers. And we use the Adam (Kingma) 2014) optimizer with initial learning rate of 5e 5, weight decay of 1e - 2, and warm-up up ratio of O. We resize the images to 256 and apply random cropping during training and resize the shorter side 512 during inference.
6.2 STATE-OF-THE-ARR COMPARISON
To validate the efficacy of method, compared it against other image editing methods. The results on Real-Edit are summarized in Tab. [ Reward-InsPix2PPP which fine-tuned based on reward data, significantly improved all metrics compared to InsPix2Pix: following accuracy increased

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Published as a conference paper at ICLR 2025
Table Comparison with existing state-of-the-art methods on Real-Edit.
Following
Preserving
Quality
Method
Edit Data
Acc
Score
Acc
Score
Ace
Score
KOSMOS-G G (Pan et al.
2023
9M
51%
2.82
9%
1.43
27%
3.20
MagicBrush (Zhang
2024b
0.31M
51%
2.90
70%
3.85
50%
3.67
MGIE (Fu et al. 2023
IM
40%
2.43
45%
2.79
38%
3.35
InstruclDillusion Geng cl al.
2024
0.86M
52%
2.87
54%
3.17
47%
3.58
HIVE Zhang al.
2024c
L.M
54%
2.93
56%
3.36
53%
3.72
HQ-Edit (Hui et al.
2024
0.5M
51%
2.84
16%
1.63
54%
3,84
InsPix2Pix (Brooks et al.
2023)
0.3M
52%
2.94
53%
3.31
50%
3.69
Reward-Ins
0.32M
63%
3.39
58%
3.43
54%
3.80
SmarlEdit (Huang el al.
2024
1.17M
64%
3.50
66%
3.70
45%
3.56
Reward-Smart
1.19M
69 %
3.72
74% %
4.00
49%
3.67
Table 2: Results on MagicBrush test set (%).
Method
CLIP-T
CLIP-T
Table 3: Human evaluation scores for SmartEdit and Seward-SmartEdit on Real-Edit benchmark.
MagicBrush
90.7
MGIE
90.9
30.5
InstructDiffusion
89.2
30.2
Method
Following
Preserving
Quality
InsPix2Pix
85.4
29.2
Sdirt 3.09
3.18
2.58
Neward-InsPix2111
88.9
29.8
Geward-SmartEan
3.34
3.46
2.73
SmartEdit
90.4
30.3
Reward-SmartEdit
91.3
30.5
by 11%, score by 0.45, preserving accuracy by 5%, score by 0.12, quality accuracy by 4%, and score by 0.11. SmartEdit, as the leading image editing model, achieved a new SOTA performance after fine-tuning based reward data. The proposed MRC module needs to be trained separately for each model. Despite using much less additional editing data (0.02M) compared to KOSMOS-G (9M), our method significantly improved both InsPix2Pix and SmartEdit, demonstrating its efficiency.
We also evaluated our method on the common evaluation benchmark MagicBrush (Zhang et al. 2024b), shown in Tab. [ Using reward data for fine-tunin fin-ttuiig still improves the performance the editing model. Specifically. it helps InsPix2Pix improve by 3.5 on CI.IP-I and 0.6 CL.IP-T, and it helps SmartEdit improve by 0.9 on CLIP-I and 0.2 on CLIP-T. These results once again validate the effectiveness of our method.
6.3 HUMAN EVALUATION
To further validate the performance of our method against state-of- oftt-----tttttttt methods, we conduct human evaluation. Specifically, we selected the best-performing SmartEdit and the Reward-Smarthal model fine-tuned using reward data. We collected the edited images they generated on Real-Edit, with 560 samples each. We then recruited 10 professional annotators to evaluate the edited images based on the three aforementioned aspects. The evaluation results shown in Tab. II As indicated in the significantly outperformed the original SmartEdit, further demonstrating
table, Reward-Smant the effectiveness of our method. The human evaluation score is in general lower than GPT-4o scores on all methods (Fig. 13 in Appendix), but the rank of different methods are consistent. We guess that the reason for this discrepancy may be that human evaluators often have higher expectations and subjective perceptions, making them more critical of details and quality.
6.4 ABLATION STUDY
Ablation for two types of reward data. The reward data consists of reward scores and reward text. To explore the effects of these two types of reward information, conducted ablation experiments on Real-Edit. As shown in Tab. 4 our baseline model without reward information (0) achieves a following accuracy of 49%, preserving the accuracy of 38%, and quality accuracy of 32%. When the
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reward score is applied alone, these metrics improve by 12%, 17%, and 21%, respectively When the reward text is used alone, the metrics improve by 11%, 14%, and 19%, respectively (O vs. ). Combining both reward score and reward text yields the best results, with the following accuracy, preserving accuracy, and quality accuracy reaching 63%, 58%, and 54%, respectively (0). These results clearly validate the efficacy of incorporating reward information.
Table 4: Ablation of two types of reward data on Real-Edis
Reward
Following
Preserving
Quality
Score
Text
Acc
Score
Acc
Score
Acc
Score
49%
2.77
38%
2.59
32%
3.15
61%
3.29
55%
3.40
53%
3.70
60%
3.25
52%
3.30
51%
3.25
63%
3.39
58%
3.43
54%
3.80
Ablation for different methods of reward integration. To utilize reward information to guide the image editing model, we integrate the reward condition into the edit model using two methods: attention in the reward encoder and addition in the U-Net. To explore the impact of these methods, we conducted ablation experiments, shown in Tab. 5 When using attention alone, following, preserving. and quality accuracy improved by 11 %, 8%, and 16%, respectively addition alone, the metrics improved by 8%, 18%, and 20% and addition achieved the best performance across all metrics methods for integrating reward conditions.
vy. @). Using vs. @). Combining both attention validating the importance of these
Table 5: Ablation of methods for integrating reward information on Real-Edit.
Following Acc
Preserving
Quality
Attention
Addition
Score
Acc
Score
Acc
Score
49%
2.77
38%
2.59
32%
3.15
60%
3.27
46%
3.11
48%
3.64
57%
3.15
56%
3.44
52%
3.76
63%
3.39
58 %
3.43
54%
3.80
Ablation for different reward scores during inference. During inference, for following, preserving, and quality to 5, and set the reward text to 'None", To investigate whether the model's editing performance is influenced by reward information, as shown in Tab From the results in Tab we observe that as the scores decrease, the model's s editing performance in all three aspects significantly declines. Specifically, when the scores dropped from to O, the accuracy for following, preserving, and quality decreased by 9%, 28%, and 19%, respectively. This indicates that our model can understand the meanings of different scores and achieve a certain degree of controllable generation in the quality of the generated images.
the reward scores
conducted experiments
Table 6: Ablation of different reward scores.
Reward Score
Following Acc
Preserving Acc
Quality Acc
F
P
Q
Score
Score
Score
0
0
0
54%
2.90
30%
2.31
35%
3.37
3
3
3
59%
3.19
42%
2.94
47%
3.64
5
5
5
63% %
3.39
58%
3.43
54%
3,80
6.5 QUALITATIVE COMPARISON
To further qualitatively validate the efffectiveness of our proposed method, we presented the results of our method on InsPix2Pix and SmartEdit, as well as the results other image editing methods, outperform the
as shown in Fig. 9 In Fig. 9 both the Reward- PisPix2Px and original Pix2ixxPix and SmartEdit, and their editing performance is also better compared to other methods, showing the effectiveness of our method.

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Chargge the herats nulsed and riindoww pallorn.
change baskigraondl botch cetinng with the stean. and palin trees leciiin.
Tranaforms showing Light andw sevtring the grawsd..
change biskiground Gcrönd seastat Ltoddsoppe with tie aee viallle in the dlonawng
Convirr image loile painthin Plyle
Instruction
top of cat's insad.
Original Image
MGIE
Scores: 1,
Spores: 4, 4,
Scores:
Scores: 5, 3,2
MagieBrush
Scores
Spores 5,
Scores:
KOSMOS-G
Scores: 1,
Soorn: 7, 1,2
Scores: 4,
Scares: 4,
Scores: 0,
Scores: 5, 3,3
HIVE
Scores: 2,4
Spores: 4, 4,
Scores: 4,3,
Spores: 0, 3.5
Sooresi 5,
Scores: 4, 5,3
HQ-Edit
1,3
Scores: 0, 0,
2,
Scones:
InsPix2Pix
Scores: 5, 1,2
Scores: 3,
Scores: 2,4
Scorus: 1, 2,2,
Seares: 4, 2,
Scores: 3, 4,
Reward- InsPix2Pix
(Ours)
Scures: 3,
Scures: 4, 5,
Scores: 5, 3,3
Scores: 5, 5,5
Scares: 4,
Scores: 5, 5,3
SmartEdit
Scores: 5,
Scures: 3, 5,
Scores: 0, 1,3
Scores: 1, 3,4
Scores: 4,
Scores: 5, 4,
Reward- SmartEdit
(Ours)
Scores: 5,4
Scuros: 5, 5,4
Scures: 5,5
Scores: 5, 3,3
Scores: 5, 5,4
Figure 9; Qualitative results Real-Edit. The three scores below the image given by GPT-4o in three aspects: instruction following, detail preservation, and generation quality.
CONCLUSION
We propose a novel framework to rectify the noisy supervision for instruction-based image editing models by adding multi-perspective reward data as additional conditions. We collect 20k multi- perspective reward data, named RewardEdit-200 using subset of InxPix2Pix dataset and GPT-4o. Additionally, presented the Real-Edit benchmark and GPT-4o- boobdsed evaluation method. Exten- sive experiments show that our approach significantly enhances performance across all perspectives, achieving rate-offthe-art results in both GPT-4o and human evaluations.
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APPENDIX
A FAILURE CASE ANALYSIS
To explore the limitations of our method, we collected and analyzed failed cases. The analysis revealed two main limitations of our method. The first limitation is that during testing, even when the given multi-perspectivy reward scores are all 5, the generated edited image does not always achieve a score of 5. This indicates that the reward information does not always perfectly guide the model, especially in some complex cases. The second limitation is that method has difficulty accurately understanding the quantifiers and spatial position words in the instructions, as shown in Fig. TO This may be due to the model's ' insufficient understanding of fine- ane-grained textual features. In future work, we will explore ways to improve the model's 's understanding fine fine-graing grained semantics for image editing.
add while switi sodiamii ng vest to tke tost.
Inabt a seddernnn mext the snowborrder.
Teppiee the midi with delphion.
Original Image
Edited Image
Original Imagee
Edited Image
Original Imagee
Edited Image
Insort wenslon beach alavug the partie the right
Chasuge the clatines right
Replace the lisst the left side witk
Original Edited Image
Original Imagee
Edited
Origisal
Edited
Figure 10: Examples of failure cases. In the first three edited images, the number of objects is incorrect, while in the last three edited images, the spatial positions of the objects are incorrect.
B EXAMPLES IN REWARDEDIT-20K
We show examples from RewardEdit-20K. as shown in Fig. II For each triplet (instruction, original image, edited image), there are three perspectives of rewards: instruction following, detail preserving. and generation quality. Each reward consists of score and text. The reward score reflects the overall quality, while the reward text provides more detailed information.
Palkerng t:Bee: totokk
Pelluring Seane Twee sesting betler wirch
de cun and conarcrpp colee bunn Bicn alanid
ipecis ansrad Bico lighing actitaaioo incettaatioo
Orianal beogs
Mati-genpectic Rovard
Oriaical
Edited Imngg
Mali-genpactre Revard
ii Tess Rechioliigg
an by
An feom mur cilee
Quekky
bocce officct
Mall-gergesthrs
Mati-gcrgectrrs
grtde.
Febiving Suare Test
Poum by Scanc caner how
polln lomee
Oriainal Imae:
Edited
Mallopenpecticc Hovard
Orisinal Imae:
Edited Image
Mash pereggrttttooo Renard
Figure 11: Examples from the RewardFdit-200 dataset.
Best viewed with zoom-in.
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C ADDITIONAL EXPERIMENT RESULTS
C.I EVALUATION BASED ON EXISTING METRICS
We also evaluated Following. Preserving, and Quality based on existing evaluation metrics, as shown Fig. 7 We recalculated the performance of existing methods our method based on the CLIP score and the FID score. Specifically, the CLIP feature similarity between the edited image and the instruction is the Following score, the similarity between the original and edited images the Preserving score, and the FID between the original and edited images is the Quality score. The table shows that our method still achieved promising results and improvements over the baseline. However, these metrics also have limitations: 1) When the editing instruction and the images CLIP/FID score can not accurately represent the ballowin/preservinggguaa
complicated, of the edited image, e.g., CLIP can not distinguish left/right. 2) the range of the following score and preserving score relatively small, which may make it hard to distinguish performance differences between methods.
Table 7: Comparison of different methods based on existing evaluation metrics.
Method
Following (CLIP)
Preserving (CLIP)
Quality (FID)
KOSMOS-G
26.8
86.4
3.01
MagicBrush
25.2
91.9
2.86
MGIE
26.4
87.0
3.09
InstructDiffusion
26.3
86.4
2.89
HIVE
26.3
89.0
3.08
HQ-Edit
28.5
77.2
3.59
InsPix2Pix
27.0
82.3
3.51
Reward-InsPing InsPix2Pix
27.5
83.8
3.31
SmartEdit
26.5
87.7
2.80
Reward-Smarre SmartEdit
26.9
90.0
2.77
C.2 ABLATION STUDY OF EDITING DATA
The 20K samples in our RewardEdit-200 dataset are randomly sampled from InsPix2Pix. Our motiva- tion is that constructing perfect image editing dataset is challenging, and the ground truth in existing image editing datasets often contains issues. Therefore, we propose using multi multi-perspectivn rewards rectify the inaccurate supervision. To more fairly demonstrate the role multi-perspective rewards, we conducted the ablation experiments shown in Tab. 8 The experimental results indicate that, with the same data, using multi multi-pprspective rewards significantly improves performance compared to the baseline, demonstrating the effectiveness of mulli-persppettve rewards.
Table 8: Ablation study of editing data.
Method
Edit Data
Following
Preserving
Quality
0.30M
2.77
2.59
3.15
Baseline
0.32M
2.90
2.88
3.52
Ours
0.32M
3.39
3.43
3.80
C.3 ABLATION STUDY ON CHALLENGING SAMPLES
To investigate whether our reward model can generate better edited images for challenging editing samples in InsPix2Pix. we first randomly selected 500 samples from RewardEdit-20K with scores not exceeding 2. Then, we used our reward model to generate edited images based on the original images and instructions of these samples, and scored them using GPT-4o. The experimental results are shown in Tab, "Original" represents the average scores of the original edited images these samples across three metries, while "Ours" represents the scores of the edited images generated by
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reward model. From the table, it can be observed that the edited images generated by method significantly outperform the original edited images on all three metrics, indicating that our method generate better results for these difficult cases.
Table 9: Comparison of edited images for challenging samples in InsPix2Pix.
Method
Following
Preserving
Quality
Original 1.15
1.69
1.99
Ours 2.92
4.10
3.68
C.4 ABLATION STUDY OF EACH PERSPECTIVE REWARD
We find that analyzing the impact of each perspective reward is beneficial, and we conduct additional experiments by training each perspective separately. As shown in Tab. [T] the following score reached 3.40 with only the instruction following reward, the preserving score reached 3.54 with only reached 3.95 with only the generation quality
the detail preserving reward, and the quality reward. These results demonstrate the effectiveness of each perspective reward.
Table 10:
Ablation of each perspective reward. 'IF", 'DP' and 'GQ' are instruction following, detail preserving and generation quality reward.
IF
DP
GQ
Following
Preserving
Quality
3.40
3.25
3.72
3.23
3.54
4.00
3.20
3.23
3.95
3.39
3.43
3.80
C.5 ABLATION STUDY OF TRAINING RESOLUTION
We chose to train at a resolution of 256 to maintain consistency with other methods (In- sPix2Pix (Brooks et al., (023), SmartEdit (Huang et al.) 2024) and MGIE (Fu et al. (023) both trained on 256), ensuring a fair comparison. Increasing the training resolution from 256 to 512 requires about 4 times computation, Due to limited computation, we are not able to tune the hyperparameters for 512 resolution. We use gradient accumulation keep the overall batch size and all the other hyperparameters unchanged. As shown in Tab. I we did not observe performance improvement compared to 256 resolution.
is hard to keep the mini- size per GPU unchanged.
Table 11: Ablations of training image resolution.
Resolution
Following
Preserving
Quality
512
3.28
3.20
3.61
256
3.39
3.43
3.80
D ADDITIONAL ANALYSIS AND DISCUSSION
D. ROLE OF REWARD TEXT
We introduced additional reward information because the ground truth existing image editing datasets is inaccurate (see lines 92-1 To rectify these inaccuracies, incorporated reward scores and text (examples in Sec. The reward score a quantitative evaluation that reflects the overall quality. Since the same reward score correspond different types of errors, further included reward text, which provides more detailed error information. Specifically, the negative text introduced can be correction the ground truth, which means that the original ground truth plus the
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negative text forms the true ground truth. To ensure that the negative text serves guide, integrate into the diffusion process as an additional condition.
D.2 LIMITATIONS OF REWARDEDIT-20K
We proposed REWARDEDIT-20K based Ins-Pix2Pis Currently, most image editing models use the Ins-Pix2Pix dataset for training, including the Instructdiffusion (Geng et al. 2024) and SEED-Data-Edit Edit (Ge et al. 2024). Ins-Pix2Pix has become the most widely used dataset the image editing field. Recent methods, such as SmartEdit, Instructdiffusion, and SEED-Data-Edit, typically multiple editing datasets for mixed training. Our improvements in SmartEdit demonstrate that our method is also effective for models trained with mixed datasets. In the future, we will apply the proposed reward data generation method to other datasets to see whether it brings further improvement.
D.3 RELIABILITY GPT-40
The annotation/evallatin from GPT-4o is not as good human annotation. However, human annotation is very expensive and time-consuming, making it unsuitable for large-scale data genera- tion. In contrast, GPT-4o-based data generation scalable with reasonable quality. Moreover, our experiments demonstrate that using multi-view rewards generated by GPT-4o can still significantly improve the model's image editing performance, indicating the reliability of our method. The version of GPT-4o we used is '2024-08-06' After multiple (5 times) tests, found that the fluctuations in the accuracy of the three metrics are within I 1,, and the score fluctuations are within 0.05. This demonstrates the stability of GPTT-4..hn the feature, will also explore fine-tuning specialized evaluation model based on existing open-source MLLMs.
D.4 COMPARISON WITH DPO-DIFFUSION
and our proposed Multi- Reward approach fundamentally aim to optimize the quality of generated images through feedback mechanisms. The main differences between our follows: 1) Granularity of feedback. IPO-DDffusion's
Both
Multi-Reward and DPO-Diffusion preference feedback is expressed as relative preferences, such as 'Image A is better than image B', therefore the feedback signal only has two possible states. In contrast, our Multi-Reward uses absolute numerical values and detailed text description for feedback signals (For examples, see Appendix Section B.). Applicability of feedback. DPO-Diffusion only applicable situations with single feedback value, whereas our approach can simultancously incorporate multi cettti--errppeiieeee feedback information, including instruction following, detail preserving and generation quality. 3) Training stability. We directly use feedback information as an additional condition while still employing the original Diffusion Loss. This approach simple and effective, avoiding the training instability that DPO can introduce to the diffusion model.
E MORE QUALITATIVE EXAMPLES ON REAL-EDIT
We show more visualizations of the examples, as shown in 12 From this figure, we find that the reward-guided models, models without reward guidance. This further demonstrates the effectiveness of our method.
and Reward-SmartEdii, both perform better than the
F HUMAN EVALUATION
F. HUMAN EVALUATION EXAMPLES
Fig. LS shows the scores given by GPT-4o and humans. It can be seen that the human evaluation scores and the GPT-4o scores for edited images generally quite similar, although the human
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Add pair of black sunglasses to the dog
Scores: Transform. the soene to the snowfall season, adding snow ow the ground and falling from the skey
4,
Scores: 4, 5,3
Scores: 5, 5,
Scores: 2, 5,
Scores: 5, 5,
Replace the yellow sports ear with red classic conwrtiibe
Scores: 5,
Scores: 4, 5,
Scores: 4, 5,4
Scores: 5,
Change the urban baokground parte with trees and flowers
Scores: 1,2,2
Scores:
Scores:
Scores:
Change the water texture to Looke Likee Lava
Scores: 4, 1,4
Scores: 4, 4,
Scores: 4, 5.
Scores: S, 5,
Convert the ine_imaae_to a sketchhstylle
Scores: 4, 4,4
Scores: 5, 5,
Scores: 5,
Scores: 5,
Erase all the ears on the street from the imagee
mm/
Scores: 0, 4.4
Scores: 3,
Score:: 3,
Scores: 5,
Original Image
InsPix2Pix
Reward-InsPix2Pix
SmartEdit
Reward-SmartEdit
Figure 12: More quantification results on Real-Edit. The scores below the edited images are the evaluation scores given by GPT-4o.
evaluation scores are overall slightly lower than the GPT-4o scores. However, both tend give higher scores good images and lower scores to poor images.
F.2 HUMAN EVALUATION INTERFACE
To further validate the performance of method against state-of-the-arr methods, conducted human evaluation. The interface is shown in Fig. [4] The orders of "Edited Image 1" and "Edited Image 2" are randomly shuffled that the evaluation is fair the two methods.
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Treeofoora comdoo ledk lige 1o od lo the auleeme officm (o0ee filling
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Itrun Seams
Figure 13: Examples comparing human evaluation and GPT-4o scores.
Original Image
Ediced Image !
Edited Image
Intraded Fellviing
Instrction Follvkig
add a white swan swimming next to the bout.
Figure 14: Human evaluation interface. Given the original image and instruction, as well as the edited images generated by SmartEdit and -Saradd-mmatEdit,, annotators evaluate and score from three aspects.
G COMPLETE PROMPTS WHEN USING GPT-40
G.1 GENERATE REWARD DATA
We used GPT-4o to generate the multi-reward dataset RewardEdit-20K, designing three types of prompts for following, preserving, and quality. The complete prompts are shown in Fig. 15
G.2 EVALUATION
We GPT-4o and design three types of prompts evaluate edited images generated by the model from the aspects of following, preserving, and quality. The complete prompts are shown in Fig. T6
18

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Published as a conference paper at ICLR 2025
System Prompt:
abvnsol exlusiin fidaky ofingge seidy tleir sopeeiffe cilingg instrctions You
driemne batter the odia precucty fellww diretives provited. Hee your ficeed evatuttoo gaide:
mny
Insapity:
mcomeetty inrprrned
Following
dinett Companeee: systemtiially cumpor otned image with the ongiral, thoseng changes ditroed by ieatractioss Broitate thete eeecation
Unenqueeed Changee
image
ietticalonal
Use Prompt:
Hease cralasic the felleviia imaaee edi beae us lhe precided nntracices:
The tis image the onginal maae, and the scend mage is the adrted smage tiningg Instraction:: (inttmntio))
Blssd your ecaltanin. srewer the Sollvernn queetinne
Previde your evalustion willy as image accamey sooe where the inage elit sccurvey sone is integer valse betweer and with indiceting level adherence
insructions.
Disstte the sepects the adn that wow reseated
flese preaeste the reepenus in the nem ota Pytien distonary stiing with keys soort and bad" "corr chosld ineger indicating image edt accuracy scone should cxncres emmence strng zeacnhing nogative acrectt.
NOT PROVIDE ANY OTHER OUTPUT TEXT EXPLANATION, Only posiiee the Pythox dictionrr stinng
curppk, your repoose should look
System
You advanced sabed evinaatng
chould unatfarred efimage edit, Socueng spectiicaly amaa of nenticedd aditngg Your evalasioo dould Ectemite sheche have matnaciion Here is your borood evolusioo guile:
fnstuction
Preacr aises Unspciiied Acca: Emore
outlaad is the ciiting inatractiuzs ransin unchanged Aasaa wheteer the calted has maintained
the ongiral svve of these amas wabost any unirorded modittzanoss
Comsidenyy Chock, Spslonutcaally compar the clitel imgge wil the vegginaal feesaing the area that suppooa clanged. Cenfen that thoc with
Preserving
ixcheneo of inslevmn Caaegen: Vently that adited image den. soe conrais asy aterations that steuld not have hem affecter accorting the menuctinn.
altent.
Ovenll Integiity Twvrr ther overal istegity and corpocctiin of are maismiedd poying plose stvetion to presrvation of néginnal clmnsta where to changee wer requoted.
User Prosutt:
Pieesc evalantc the fellewing image all with foos or consistaccy arem flat should samann unchangel sccending to the provized instuctions the ooganal imaee and second iaze clited image. Editigg Imstrctiona: (nstmction).
Blsod os your eraluatonn sunwer the fslloau questiess
Prorite your evaleatice esiily is consiarecy some where the concittirry srone intrer value batazee 8 and tedicaring the highoa level estaitccsy with the erggad moslied sreas.
Deseile the mpects oftlle alli where unionnee changes male.
Please
reapome
l'ython disteeary
beard' and bud Yeare secid
indiating the comiinancy
should be concoe scrtenee aineg doseiting the zepaiine sppects whrre chances wes uninizaded.
po NOT PRovine ANY OTTEE OUTPUT TEXT oa EXp ANATION only prasode the Fyhor derionary ering For ouanale, your coporoe should book like daa sour) 4, tod: XXX in backgpoune and consitant""
Prompt:
rateoiress edited image. Your usk consteize image avalare perfons sppor, concitering spprrs interationn oegio
atarccdd motel specifieally
homoary
Elite
slis Henü soankssly with
The tassiio steld smooth wilhoat nntiodlee
musratcee tucture soler ilamnny Compaction: lisaine ovenll
odns lasous chend mareate voral talance
appcal
Appoopritinns
Exaliste
appoprioe inog
parport
the mmage -Coussatcyy Lightnng und slusloe Aasas the duzna callins Tare sboidd anifaca cetecl funs mand ongiral conditiea, incoraurencis lighing easins
Quality
contesee with alis atmuunal. al Subjec Mutei Ealasamerr. Conitaa los alia affes subjoct sulic ef imuge. mediccations should onlance chanstemnizs.
make
anjezt preseninion witseet oven combateenn
User Prompt:
Plexs: evelanic the puridei image base call quility and astunl appearance:
The smage you arz evalunnng may have been ecinet bur your foces should be as the mage mett Bared sn your evalaation, anoerer the tollowing cuestice::
Provide your evalention silely as quiliy score where the quility soore integer valce between and indconing the highes quilly and nos satuni apocerance.
Desetie apecta of imaze dua sepatively affezi suality. Flaase prnecur the repross
Python dinerory anang with baye 'core" and 'ecan' shoold integer indicatig the quility score, huo doceld concbe sestense sting doseriing segativ copects efthe inage.
po sor PaDviDu
OTIER OUTPUT oit XXPANAAION, Only prasode the Pythe dictonary sting.
For ousnolo your soposse should look like thas sours 4, bud: Some utifaca insge:)?..
Figure 15: Complete prompts for generating reward data. Best viewed with zoom-in.
19

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Published as a conference paper at ICLR 2025
System Prumpt:
You are edvance Al desinned to asses the aocumey of image od beard on given indractions
Your task is lo cuatiiee ad imaze and detertine it has booz moditeed acconding to the poovided lestuctions. Here's how you caz pertorn be erauuton: Focas on the scherrce the edited image the gion instrdd ons The modification should accumnly riflect the requested changss will introdciing dy insecume as or risttaggeitaainn
"he adited image must be conaislent with Ba original lmage and the editing instrutions.
Constber altemative mierpedatooss or cresivee apgmachee thet still most the editing cmana is valid. Asses the accurncy of the eiit comperior the original image and the instructons provided.
Following
User Prampt:
Preise mollssnn fellouring imagwedii based grovided instuctons
"The imagee enigisal imsges and the worrd inage edited image Editaa instrudions: [istractio))
your erabaasoo, ziseeer the followogg two gustiios::
editd tasae follow editnng Instrutionn? Please respond yos
Brovide your solely as mage edit accuncy soom the image edtt accuracy score a visotvalue between 5, indientig highest generate respocue es
dringg ('re'' or Python dictionary sxing lays shorld indheating image acurnry scom.
hcom'
image instructinn NATION, Only procade tython dustonsrry string
PROVIDE shoull losk ('llowwkk
Prompt: designed consetency of image editing
the given
image
original imaga. Herr' how you
ocalaatior:
affected the eéitad image that uneslated given instrccioss. These should consisbent original imageaadd shocld
Focus
conaisiont with the odiginal the cautlated oditing lnstructions.
Preserving
Crmmftrr altmmatve mergmenioos cnwttive that still most conentency criiens volid.
Assess the consiteazy odited compatison odittal
User Prompt:
Pless ecaluse the following image odit lesed on the growiddd indracions and the origznl image:
The first image us the oeiginal image and the seorrd image is the ednel image. Editing Instrctions (intractin)
Basod os your ev avaatonn auswee the following bwo qustioss
1. Does the ane of the adited zzage that usadaaed is the editig instuctioes renais consistatt with the origina image? Please respord with yes or to
Procida your cvlluation solely na image condin veryy at cre when the inage canyy scoee i foot value betoven
with indiciinng the hightut lesil
considtanyy
origiral image.
value of tconalstant ahould string Iyes' or indroaing wheber the ame of edited image that trmiited io the ehting rntructinn memaine concenennt wiit the onginal image, and valiee boor' should indicatio maage condstenzy
Please gesecido the risponsd ia forn of Python ditionary string lays loonsstant and
OUTPUT TEXT EX Only provide Pytac actiooary string coapppl. your resporee should look
IBOVDD
Joussseet yes, 3.00".
Systems Premut:
You are a cosai mode tnited to evaluate the quality of images Your talk is oramine image and evaleate it quality based on various as aspocts such clanty, compostion, lightnng subject nater and warther the odis appear ratuni. Here's low
pation: the ovaluztion:
Pay dess attetion
clinity the image The
shaudd sharp
deta's should be dlar.
Look for any genembed artifacts in the image. There should be to artifiaa pottemss or didbrrioss cazsed by the image generation process.
Asses the oompestoon of the image The amagement of slments should be balaneed and visusly appsiing.
Eveluate the lightin the imaee. The lightng should be epunpeato for the wome and relonce the sulgect
Conscbe the sritedt metter of image The sabjett should be dell--defied and contrbette to the oven'l quilty of the image.
Clock if the adia maae the imsig epperraturl ratural The modificatioss was shorld bland sumilslly with the originni skmerts in thout any obwises signs of tampering
Quality
inporeisteey..
User Prompt:
Frece mollaste the guiltty of the fellowing image beeed on ihs darity the presecoo of any genervted art fadss compontion lighting, subyet ruber and whether the adits spparr natural. Roaed on yourevalation,, antser the fellowing two queiiore Is the image clear, tree of generited artiaess will-conpoed repacly iit, will a a wall-dstine sulject, and do ta edis appear rstard?? Plecae regood with yes' or bal.
Provide youree porealorai an image qualiy seem when image qualry y scom is vale betwen and with indrating the lighest level of qualit. should bes string (yed' or 'wo) inficating
Pawsse genende reçoran in Eie form of tylee ficborryy string with keys 'dear' boom! The val whether the image most the catecta and bhe value of soor should be thost indratio the image quality scom.
DO NOT PROVIDE ANN OTUUR OUTFUT TUXT OR NATIOO Only prositte the Pylbon dieboraryy string For Foresantto, your risposse abocld look like this "'daarr" yeu' Nemre 4.0%"*
Figure 16: Complete prompts for evaluation. Best viewed with zoom-in.
20


Reviews:
[{"review_id": "cGxgOsUrcz", "reviewer": "Reviewer_RGo1", "summary": "The authors introduce a high-quality editing reward dataset to address the problem of editing effectiveness due to training data quality issues with instruction-based image editing methods. This dataset contains assessment scores and reward texts, which are introduced as additional conditions to the instruction-based editing model to improve the model's ability. The authors also introduce an evaluation set to assess the quality of the instruction-based image editing model from multiple dimensions. Qualitatively and quantitatively, it is demonstrated that the present method effectively improves the quality of the instructional editing model.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tA novel reward-based instruction editing framework that introduces evaluation scores for VLLM as well as rewarding textual feedback to improve the capability of instruction editing models. \n2.\tThe creation of Real-Edit provides a standardized approach to evaluate instructional editing methods in different scenarios.\n3.\tThe method shows superior performance in both quantitative metrics and qualitative results, indicating robust editing capabilities.", "weaknesses": "1. Both the quantitative assessment in Table 1 and the training strategy are based on the same scoring strategy for the GPT-4o, making the evaluation of the results overly dependent on the a priori of the VLLM and making it difficult to objectively validate the strengths of this method. One solution to this dilemma is to use other assessment metrics (e.g., CLIP scores) on the three dimensions to be adopted for comparison with other methods.\n2. While experiments have shown that the introduction of additional reward text can improve image editing, unfortunately, there is no analysis of why introducing negative text in this way could help guide the diffusion process towards more effective editing.\n3. Are there limitations to the introduction of the dataset REWARDEDIT-20K. Different instructional editing models may have been obtained by tuning on different training sets [1][2]. While this paper achieved an advantage on Ins-Pix2Pix and its improved version SmartEdit, is it still desirable to train other models using REWARDEDIT-20K obtained from the Ins-Pix2Pix training set?\n[1] Instructdiffusion: A generalist modeling interface for vision tasks, CVPR 2024\n[2] SEED-Data-Edit Technical Report: A Hybrid Dataset for Instructional Image Editing, arXiv 2024", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3Nf0fvkFnz", "reviewer": "Reviewer_X16E", "summary": "This paper aims to correct the noise supervision in instruction-based image editing models by using multi-view reward data as an additional condition. To achieve this, the authors collected a dataset named RewardEdit-20K, which contains 20,000 instances of multi-view reward data.", "questions": "1. The human evaluations showed slightly lower scores than those generated by GPT-4o, though both showed consistency in ranking. Could the authors provide insights into why this discrepancy exists?\n\n2.Given the reliance on the RewardEdit-20K dataset, a more detailed release plan for this dataset (and possibly pretrained models) could be helpful.\n\n3. While the approach shows significant improvements, an analysis on instances where the multi-reward mechanism fails or provides subpar results would be beneficial. Understanding the limitations could offer insights for future iterations or refinements of the method.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper intorduce  a multi-view reward mechanism, instead of directly improving the quality of ground-truth images, the authors utilized GPT-4o to evaluate the training data from three key perspectives: instruction adherence, detail preservation, and generation quality.\n\n2.The RewardEdit-20K dataset and the Real-Edit evaluation benchmark.", "weaknesses": "1. The multi-view reward mechanism used in this paper relies entirely on GPT-4o’s evaluation. Although GPT-4o demonstrates strong capabilities in understanding and generating natural language, it may not fully capture the subtle nuances of human perception regarding image editing quality.\n\n2. The cost for this reward is expensive as it using GPT4-o .", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UHeIpW77EZ", "reviewer": "Reviewer_eTqX", "summary": "This paper identifies significant issues related to data quality in the current Image Edit dataset. It proposes a pipeline for data cleaning and scoring using MLLM and introduces a cleaned dataset along with a training framework designed for multi-reward scenarios.", "questions": "See above, especially W.1", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "- This paper discusses the issues present in the current dataset and utilizes MLLM for data cleaning. Through experiments and comparisons, it demonstrates the effectiveness of the cleaned dataset and the new training methods.\n- Good writing and detailed experiments make this paper compelling.", "weaknesses": "- W.1: The paper lacks a comparison with RL in T2I methods like DPO-Diffusion[1]. If I understand correctly, I believe that the Multi-Reward Framework is conceptually similar to methods like DPO-Diffusion. Therefore, I think it is reasonable and necessary to articulate the comparisons and distinctions between these approaches, especially the method difference.\n- W.2: The article lacks some novelty. Of course, high-quality and abundant data can effectively enhance model performance, so I am uncertain about the extent to which the proposed Multi-Reward Framework improves upon traditional methods. For example, in Table 1, additional editing data (0.02M) was used for training. I believe it is fair to compare it with the exact same data using the same baseline method; otherwise, it is difficult to convince me whether the improvement comes from the high-quality data or the architecture. Perhaps experiments on the unprocessed REWARDEDIT-20K data could be added for comparison.\n\n[1] Wallace, B., Dang, M., Rafailov, R., Zhou, L., Lou, A., Purushwalkam, S., Ermon, S., Xiong, C., Joty, S., & Naik, N. (2023). *Diffusion Model Alignment Using Direct Preference Optimization*. arXiv preprint arXiv:2311.12908.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "l4USDFNLtv", "reviewer": "Reviewer_sJ5c", "summary": "This paper introduces a dataset and benchmark for assessing image editing performance across multiple dimensions. The authors also utilize these multi-dimensional scores as rewards to enhance the effectiveness of image editing models.", "questions": "1. Could you elaborate on the dimensionality of $c_R$ and $Linear(c_R)$, and explain how lines 281 and 287 can be implemented, given that both use the same notation of $Linear(c_R)$?\n2. In line 369, is there a specific reason for training at a resolution of 256? Wouldn't training and inference at 512 yield better results?\n3. Are the trained modules shared or trained separately between InstructPix2Pix and smartEdit?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper underscores a valuable methodology for evaluating image editing datasets, which is highly beneficial in practical applications.\n2. It proposes the use of evaluation scores to boost the performance of image editing models, which is novel in image editing.", "weaknesses": "1. Although the use of GPT-4o for evaluation is efficient in practice, it lacks guaranteed consistency. The evaluation results could be influenced by underlying changes in GPT-4o, rendering the results unreliable. An alternative solution to this issue could be to train an independent evaluation model.\n\n2. The RewardEdit-20K dataset continues to utilize images generated by InstructPix2Pix's model. For some challenging editing samples, scores cannot exceed 2 points. Even if the reward model can enhance performance in other samples, can it generate better results for these difficult cases generated by InsPix2Pix?\n\n3. There is a lack of detailed analysis on the individual impact of each perspective reward on the improvement of editing models. Including such insights in an ablation study could strengthen the paper's contribution.\n\n4. The practice of incorporating additional \"reward scores\" to enhance image generation quality is already established within the stable diffusion community (see https://civitai.com/articles/4248/what-is-score9-and-how-to-use-it-in-pony-diffusion). A more thorough discussion linking the proposed multi-reward framework to existing methodologies would enrich the manuscript's contribution.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)