Title: PrefPaint: Aligning Image Inpainting Diffusion Model with Human Preference
Venue: NeurIPS 2024
Accepted: yes
Keywords: diffusion model

image inpainting

human feedback reinforcement learning
Abstract: In this paper, we make the first attempt to align diffusion models for image inpainting with human aesthetic standards via a reinforcement learning framework, significantly improving the quality and visual appeal of inpainted images. Specifically, instead of directly measuring the divergence with paired images, we train a reward model with the dataset we construct, consisting of nearly 51,000 images annotated with human preferences. Then, we adopt a reinforcement learning process to fine-tune the distribution of a pre-trained diffusion model for image inpainting in the direction of higher reward. Moreover, we theoretically deduce the upper bound on the error of the reward model, which illustrates the potential confidence of reward estimation throughout the reinforcement alignment process, thereby facilitating accurate regularization.
Extensive experiments on inpainting comparison and downstream tasks, such as image extension and 3D reconstruction, demonstrate the effectiveness of our approach, showing significant improvements in the alignment of inpainted images with human preference compared with state-of-the-art methods. This research not only advances the field of image inpainting but also provides a framework for incorporating human preference into the iterative refinement of generative models based on modeling reward accuracy, with broad implications for the design of visually driven AI applications. Our code and dataset are publicly available at \url{https://prefpaint.github.io}.

PrefPaint: Aligning Image Inpainting Diffusion Model with Human Preference

Abstract

In this paper, we make the first attempt to align diffusion models for image inpainting with human aesthetic standards via a reinforcement learning framework, significantly improving the quality and visual appeal of inpainted images. Specifically, instead of directly measuring the divergence with paired images, we train a reward model with the dataset we construct, consisting of nearly 51,000 images annotated with human preferences. Then, we adopt a reinforcement learning process to fine-tune the distribution of a pre-trained diffusion model for image inpainting in the direction of higher reward. Moreover, we theoretically deduce the upper bound on the error of the reward model, which illustrates the potential confidence of reward estimation throughout the reinforcement alignment process, thereby facilitating accurate regularization. Extensive experiments on inpainting comparison and downstream tasks, such as image extension and 3D reconstruction, demonstrate the effectiveness of our approach, showing significant improvements in the alignment of inpainted images with human preference compared with stateof-the-art methods. This research not only advances the field of image inpainting but also provides a framework for incorporating human preference into the iterative refinement of generative models based on modeling reward accuracy, with broad implications for the design of visually driven AI applications. Our code and dataset are publicly available at https://prefpaint.github.io .

1 Introduction

Image inpainting, the process of filling in missing or damaged parts of images, is a critical task in computer vision with applications ranging from photo restoration [1, 2] to content creation [3, 4]. Traditional approaches have leveraged various techniques, from simple interpolation [5-7] to complex texture synthesis [8, 9], to achieve visually plausible results. The recent advent of deep learning, particularly diffusion models, has revolutionized the field by enabling more coherent and contextually appropriate inpaintings [3, 10-12]. Despite these advancements, a significant gap remains between the technical success of these models and their alignment with human aesthetic preferences, which are inherently subjective and intricate. As shown in Fig. 1, the existing stable diffusion-based inpainting model tends to generate weird and discord reconstruction.

Human preference for visual content is influenced by complex and mutual factors, including but not limited to personal background, experiences, and the context in which the content is viewed [13, 14]. This makes the task of aligning inpainting models with human preference particularly challenging, as

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it requires the model not only to understand the content of the missing parts but also to predict and adapt to the diverse tastes of its users.

Inspired by recent advancements in the reinforced alignment of large pre-trained models [15-19], we propose to align diffusion models for image inpainting with human preference through reinforcement learning. Specifically, our approach is grounded in the hypothesis that by incorporating human feedback into the training loop, we can guide the model toward generating inpainted images that are not only technically proficient but also visually appealing to users. Technically, we formulate the boundary of each reward prediction in terms of the model's alignment with human aesthetic preferences, thereby leveraging the accuracy of the reward model to amplify the regularization strength on more reliable samples. Extensive experiments validate that the proposed method can consistently reconstruct visually pleasing inpainting results and greatly surpass state-of-the-art methods.

In summary, the main contributions of this paper lie in:

we make the first attempt to align diffusion models for image inpainting with human preferences by integrating human feedback through reinforcement learning;

we theoretically deduce the accuracy bound of the reward model, modulating the refinement process of the diffusion model for robustly improving both efficacy and efficiency; and

we construct a dataset containing 51,000 inpainted images annotated with human preferences.

2 Related Work

Reinforcement Learning & Model Alignment. Reinforcement learning [20-22] is a paradigm of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward. The foundational theory of reinforcement learning is rooted in the concepts of Markov decision processes [23], which provide a mathematical structure

for modeling decision-making in environments with stochastic dynamics and rewards [24, 25]. With the advent of deep learning, deep reinforcement learning [21, 26] has significantly expanded the capabilities and applications of traditional reinforcement learning. The integration of neural networks with reinforcement learning, exemplified by the Deep Q-Network [27, 28] algorithm, has enabled the handling of high-dimensional state spaces, which were previously intractable. Subsequent innovations, including policy gradient methods [29, 30] like Proximal Policy Optimization [31] and actor-critic frameworks [32, 33] like Soft Actor-Critic [34], have further enhanced the efficiency and stability of learning in complex environments.

The recent surge in popularity of large-scale models has significantly underscored the importance of reinforcement learning in contemporary AI research and application [15, 16]. As these models, including large language models [35, 36] and deep generative networks [37-39], become more prevalent, reinforcement learning is increasingly employed to fine-tune, control, and optimize their behaviors in complex, dynamic environments. This integration is particularly visible in areas such as natural language processing, where reinforcement learning techniques are used to improve the conversational abilities of chat-bots and virtual assistants, making them more adaptive and responsive to user needs. [17-19] Moreover, in the realm of content recommendation and personalization, reinforcement learning algorithms are instrumental in managing the balance between exploration of new content and exploitation of known user preferences, significantly enhancing the user experience. The growing intersection between large models and reinforcement learning not only pushes the boundaries of what's achievable in AI but also amplifies the need for sophisticated reinforcement learning techniques that can operate at scale, adapt in real-time, and make decisions under uncertainty, thereby marking a pivotal evolution in how intelligent systems are developed and deployed.

Diffusion Model. Diffusion models have recently emerged as a powerful class of generative models [38-42], demonstrating remarkable success in generating high-quality, coherent images [43, 44]. These models work by gradually transforming a distribution of random noise into a distribution of images, effectively 'diffusing' the noise into structured patterns [45, 46]. In the context of image inpainting, diffusion models offer a significant advantage by leveraging their generative capabilities to predict and fill in missing parts of images in a way that is contextually and visually coherent with the surrounding image content [47, 48].

Recent studies have showcased the potential of diffusion models in achieving state-of-the-art results in image inpainting tasks, outperforming previous generative models like Generative Adversarial Networks in terms of image quality and coherence [49, 50]. However, while these models excel in technical performance, there remains a gap in their ability to cater to diverse human aesthetic preferences. Most existing works focus on the objective quality of inpainting results, such as fidelity to the original image and coherence of the generated content, with less attention given to subjective satisfaction or preference alignment.

3 Proposed Method

Due to the random masking within the task of image inpainting, there may not be a definitive causal relationship between the known and inpainted contents, which manifests as a one-to-many issue. Consequently, stringent per-pixel regularization inherently results in unnatural reconstruction, which significantly diverges from samples conforming to human preference. To this end, we propose a reinforcement learning-based alignment process involving human preferences to fine-tune pre-trained diffusion models for image inpainting, aiming to improve the visual quality of inpainted images (Sec. 3.1). More importantly, we theoretically deduce the upper bound on the error of the reward model (Sec. 3.2). Based on this deduction, we formulate a reward trustiness-aware alignment process that is more efficient and effective (Sec. 3.3).

3.1 Reinforced Training of Diffusion Models for Image Inpainting

Diffusion models [38, 40] iteratively refine a randomly sampled standard Gaussian distributed noise, resulting in a generated image. To adjust the distribution of diffusion models, we introduce human feedback rewards to measure and regularize the distribution of model sampling outputs. To be specific, instead of applying standard policy-gradient descent [51] that is hard to converge to high-quality models, inspired by classical methods, e.g., TRPO/PPO [31, 52], which introduces a model trust region to avoid potential model collapse during the training process, we achieve the reinforced

training of a diffusion model as

where R ( · ) represents the reward model, which quantifies the quality of diffusion samples; x is the reconstructed sample; P θ and P θ ′ represent the probabilistic functions of training and reference models parameterized with θ and θ ′ , respectively; ∇ θ calculates the derivative on θ ; D ( ·|· ) is the divergence measurement of the given two distributions for regularizing the distribution shifting; the hyperparameter κ balances the two terms. Inspired by recent work [53, 54], we take the same step to measure the probability of each diffusion step via calculating the probability of perturbation noise, e.g., the DDIM sampling algorithm [40],

where ϵ ( t ) θ ( · ) is the noise estimate diffusion network; t is the diffusion step; the scalar α t controls the noise to signal ratio; and σ t ϵ t represents a Gaussian noise to increase the sampling diversity. Thus, the result of each reverse step consists of a deterministic component ¯ x t -1 and a probabilistic component ˜ x t -1 . Since x t -1 ∼ N (¯ x t -1 , σ t I ) , we calculate the density of each step P = Φ ( x t -1 -¯ x t -1 σ t ) to approximate the probability, where Φ( · ) denotes the density function of standard Gaussian distribution.

3.2 Bounding Reward Model Error

The precision of the reward model R ( · ) assumes a pivotal function within this learning framework as it directs the optimization trajectory. In this section, we theoretically derive its error upper bound, which can facilitate the reinforced training process in terms of both efficacy and efficiency.

Denote by X = [ x 1 , x 2 , · · · , x N ] ∈ R N × D and y = [ y 1 , y 2 , · · · , y N ] ∈ R N × 1 a set of N inpainted images of dimension D by diffusion models and corresponding ground-truth reward values from human experts, respectively. By dividing the reward model into two parts, i.e., feature extractor F ( · ) and linear regression weights ψ ∈ R D ′ × 1 , we thus can represent it as ⟨F ( x t ) , ψ ⟩ , where ⟨· , ·⟩ calculates the inner product of two vectors. We can formulate the learning process of the last weights as the result of the following optimization process:

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where ∥ · ∥ 2 is the ℓ 2 norm of a vector, λ > 0 , and the second term is used for alleviating the over-fitting phenomenon. Thus, we have

where Z ∈ R N × D ′ = F ( X ) is the set of embeddings of X . ψ ∗ is the ideal weight with y = Z ψ ∗ + ζ and ζ a noise term between Z ψ ∗ and y . Then, the following term also holds:

where z ∈ R D ′ × 1 = F ( x ) is the reward embedding of a typical input sample. Based on CauchySchwarz inequality, there is

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where V = Z T Z + λ I and ∥ z ∥ V -1 := ∥ z T V -1 ∥ 2 . Moreover, based on Theorem 2 of [55], for δ > 0 , we have 1 -δ probability to make following inequality stands

where det ( · ) computes the determinant of the matrix, and B is a scalar. We have

Due to C bound being constant after the training of the reward model, we can conclude that

where sup represents the upper bound. Such a theoretical bound is also experimentally verified in Fig. 2.

3.3 Reward Trustiness-Aware Alignment Process

According to Eq. (10), it can be known that the error of the reward model is bounded by ∥ z ∥ V -1 , there might be a relatively large error for those ∥ z ∥ V -1 quite large. Thus, we further propose a weighted regularization strategy to amplify the penalty strength of those samples in the high-trust region. Specifically, we calculate the amplification factor as

where the hyperparameters k = 0 . 05 and b = 0 . 7 are used for scaling the regularization strength. We then define the overall gradient of the reward trustiness-aware alignment process as

where J ′ ( x ) is the weighted reward loss for sample x . For the updating of the reference model θ ′ , we adopt [53] to update the model in each optimization step. Correctly amplifying the scaling factor can both speed up the convergence speed and reconstruction effectiveness, as experimentally validated in Sec. 5.2.

4 Human Preference-Centric Dataset for Reward

We first randomly selected 6,000, 4,000, 6,000, and 1,000 images with diverse content from ADE20K [56, 57], ImageNet [58], KITTI [59], and Div2K [60, 61] datasets, respectively. We then applied the operations described below to generate prompt images (i.e., incomplete images), which were further fed into the diffusion model for image inpainting named Runway [62], producing inpainted images. To mitigate the potential bias of the reward model on different rewards, we

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Figure 4: WinRate comparison heat-map between

repetitively generated from a given prompt image three distinct inpainted images. Consequently, we obtained 51,000 inpainted images in total, which were scored by human experts following the criteria described below.

Generation of Incomplete Images. We considered two distinctive image completion patterns: inpainting and outpainting. For inpainting, we simulated warping holes on images by changing the viewpoints, where we derived the depth of the scene using [75]. Following past practice [76], we defined a camera sequence that forms a sampling grid with three columns for yaw and three rows for pitch. The resulting nine views feature yaw angles within a ±0.3 range (i.e., a total range of 35 ◦ ) and pitch angles within a ±0.15 range (i.e., a total range of 17 ◦ ). As the range of views increases, the task of inpainting becomes progressively more challenging. For outpainting, we randomly masked the boundary of the image through two types of random cropping methods: (1) square cropping, where the size of the prompt ranges from 15% to 25% of the image size ( 512 × 512 ) randomly; (2) rectangular cropping, where the height of the prompt matches the image size, while the width is randomly sampled between 35% and 40% of the image size. Each cropping method accounts for half of the outpainting prompts.

Scoring Criteria. In the scoring process, we employed three criteria, i.e., ( 1 ) structural rationality representing the rationality of the overall structure, whether illogical objects and structures are generated; ( 2 ) feeling of local texture showing whether strange textures are generated that do not conform to the characteristics of the object; and ( 3 ) overall feeling indicating the impression capturing the overall feeling upon first glances at the image. The score value is in the range of 1 to 7, indicating

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from the least to most favorable. The final score for reward model training is derived by averaging those three scores using the weights of [0 . 15 , 0 . 15 , 0 . 7] . We refer readers to the Supplementary Material for more details about dataset process and labeling scheme.

Statistical Characteristics of our Dataset. As illustrated in Fig. 3 (a), the score distribution of images from different datasets is generally uniform. Meanwhile, from Fig. 3 (b), we can see that the details and overall score are independent of each other, showing the correctness of the scoring scheme and the necessity of each score. Finally, Figs. 3 (c) and (d) show the ratio of images with different mask sizes, where it can be seen that the outpainting has a more uniform distribution than warping based hole, which rely on depth map and may not have uniform distribution.

5 Experiments

Evaluation Metrics. We adopted seven metrics to measure the quality of inpainted images, i.e., the predicted reward value by our trained reward model , T2I reward [54], CLIP [70], BLIP [71], Aesthetic (Aes.) [72], Classification Accuracy (CA) [73], and Inception Score (IS) [74]. Specifically, T2I reward, CLIP, BLIP, and Aes. directly measure the consistency between the semantics of inpainted images and the language summary of corresponding prompt images. While, CA and IS indicate the quality of the generative model. Due to the fact that our inpainting reward directly measures the reconstruction quality, we adopted it as the principle measurement of our experiment.

Implementation Details. We partitioned the dataset in Sec. 4 into training, validation and testing sets, containing 12,000, 3,000 and 2,000 prompts (with 36,000, 9,000 and 6,000 images), respectively. The reinforcement fine-tuning dataset contains the prompts from the original reward training dataset. We employed the pre-trained CLIP (ViT-B) checkpoint as the backbone of our reward model R ( · ) with the final MLP channel equal to 256. We utilized a cosine schedule to adjust the learning rate. Notably, we achieved optimal preference accuracy by fixing 70% of the layers with a learning rate of 1 e -5 and a batch size of 5. We trained the reward model with four

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NVIDIA GeForce RTX 3090 GPUs, each equipped with 20GB of memory. With the trained reward model, we subsequently fine-tuned the latest diffusion-based image inpainting model, namely Runway [62], on four 40GB NVIDIA GeForce RTX A6000 GPUs as our PrefPaint. During fine-tuning, we employed half-precision computations with a learning rate of 2 e -6 , and a batch size of 16.

5.1 Results of Image Inpainting

We compared our PrefPaint with SOTA methods both quantitatively and qualitatively to demonstrate the advantage of our method. We compared the

WinRate and Reward under various sampling steps in Table 1 and Fig. 4, where it can be seen that our method actually greatly improves the probability of high-quality human-preferred samples, since with a single inference step, our method achieves more than a 70% WinRate, which is similar to the baseline model ( Runway ) with S = 3 . The higher reward value and lower variance also indicate that our method can consistently generate high-quality samples. Comparisons with other metrics in Table 2 further support the superiority of the proposed method. In addition, we visually compared the inpainted images by different methods in Fig. 5, where it can be seen that our method can generate more meaningful and reasonable content, which is consistent with the style of the prompt region. We refer readers to the Supplementary Material for more details about more comparison results.

Finally, we have also carried out a user study to evaluate our superiority. We have randomly selected about 130 groups of results and conducted a user study involving 10 users. The WinRate map, as depicted in Fig. 11, demonstrates that our reward scoring is highly accurate and capable of assessing the results of inpainting under criteria based on human preference. We also present some examples scored by our reward model in Fig. 8 to illustrate the scoring criteria system.

5.2 Ablation Study

Reward Trustiness-Aware Scheme. We validated the advantages of our reward trustiness-aware training scheme. As shown on the left side of Table 3 b) and e) , we can see that this scheme improves the reward by 1.7% and accelerates the algorithm efficiency of 106.40%. Although a constant amplification, such as c) and d) in Table 3, can expedite the training process even faster than the proposed method, it compromises model accuracy, as evidenced by the reduced WinRate and Reward. This demonstrates the necessity and efficacy of adaptively managing the sampled trajectory and underscores the superiority of the proposed method.

Training Manner of Reward Model. We investigated two types of strategies to train the reward model R ( · ) i.e., classification-driven and regression-driven. Specifically, the former classifies the

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discrete scores of annotated reward samples, while the latter directly makes a regression on the scores. As listed on the right side of Table 3, it can be seen that the regression-driven training generally outperforms the classification-driven one on reward accuracy. Moreover, the larger variances from the regression-driven training show the strong discriminative ability. Based on accuracy and boundary performance, we finally selected a fixed rate of 0.7 and MLP-256 with the regression-driven training manner as the configuration of our reward model.

Parameterize amplification factor γ . To parameterize the amplification factor in Eq. (10), we investigated various functions to parameterize as shown in Table 4. The experimental results indicate that the exponential function provides the best regularization effect. In contrast, the linear function and static constant do not fully exploit the regularization effect of the reward upper boundary.

5.3 Further analysis

Reward errors distribution. We make statistics of reward estimation errors, and the results are shown in Fig. 9. Although the proportion of very large error samples is not large, the incremental performance of our method lies in a more suitable choice of amplification function, as evidenced by Table 4.

Necessity of Our dataset. Although existing metrics such as BERT Score [77] provide a general measure of quality, we emphasize that our dataset is specifically tailored for the task of image inpainting, where human-labeled scores are both essential and more precise. To substantiate this claim, we conduct a comparison between the BERT score and our dataset's score, as illustrated in Fig. 10. The results reveal a significant divergence between human judgments and BERT's preferences, underscoring the necessity and superior accuracy of the proposed dataset for this specific task.

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5.4 Applications of Image FOV Enlargement and Novel View Synthesis

We also applied our approach to two additional tasks: (1) image field of view (FOV) enlargement, where we iteratively extended the boundaries of a typical image strip in the horizontal direction to create a wider FOV image; and (2) novel view synthesis, where we warped a given image using the predicted depth image through the method in [75] to generate a novel viewpoint and subsequently applied diffusion models to fill the holes/missing regions of the Warped view. As depicted in Fig. 6 (a)-a oil painting and (b)-a realistic photography, our method yields natural and visually pleasing results that can be seamlessly integrated with the prompt regions. As illustrated in Figs. 7, our method can generate more reasonable novel views and a visually appealing reconstruction. We refer readers to the Supplementary Material for more details about more application visual demonstrations.

6 Conclusion and Discussion

We have presented PrefPaint, an innovative scheme that leverages the principles of the linear bandit theorem to align diffusion models for image inpainting with human preferences iteratively. By integrating human feedback directly into the training loop, we have established a dynamic framework that not only respects the subjective nature of visual aesthetics but also continuously adapts to the evolving preferences of users. We conducted extensive experiments to demonstrate the necessity for alignment in the task of image inpainting and the significant superiority of PrefPaint both quantitatively and qualitatively. We believe that our method, along with the newly constructed dataset, has the potential to bring significant benefits to the development of visually driven AI applications.

PrefPaint aligns with a distribution that corresponds to the preferences of a specific group. However, it is important to recognize that individual preferences for image styles vary. Therefore, after achieving alignment with the general preferences of a group, it is advisable to develop personalized rewards and a corresponding reinforced alignment model to ensure complete alignment with the preferences of each user. Recently, the implementation of a reward-free alignment process, as discussed in [16], has gained popularity. Consequently, exploring reward model-free training methods for alignment in diffusion-based models represents a promising avenue for future research. Furthermore, there is an opportunity to explore the potential applications of our in-painting algorithm for additional 3D reconstruction tasks.

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Appendix

Table of Contents

A Dataset Details

In this section, we visualize our labeling platform and the corresponding labeling scheme.

A.1 Labeling Scheme

The reward score contains the following 3 terms:

Structural Rationality . It measures the correctness of the model understanding of image content. Score (0-2): Failure generation, e.g. strange and weird objects; random patterns completed; inappropriate strings; abrupt bar completions. Objects are generated but obviously do not belong to the scene, e.g. There are shelves unique to homes on the side of the highway; there is a marble floor blocking the front of the washing machine. Score (3-5): The understanding of the scene is roughly correct, e.g. the inpainted sky next to the sky pattern; the sea behind the beach. Score (6-7): The overall structure of the generated scene is reasonable and do not seem to be any major problems. To illustrate the scoring scheme, we show some examples and corresponding reasons in Table A-1.

Prompt with Reconstruction

Prompt with Reconstruction

Feeling of Local Texture. It measures whether the texture of the object is correct and consistent with objective facts. Score (0-2): Failure generation (e.g. strange and weird objects; random patterns

completed; inappropriate strings; abrupt bar completions). The generated objects do not conform to objective facts (e.g. a dog has two heads; a person without an upper body). The texture of the generated objects is very poor (e.g. two car bodies stuck together; poor quality sky). Score (3-5): The generated objects are generally reasonable (e.g. a complete leopard, but with very long legs; a complete corn, but the corn kernels are strange; there are flaws in the complete object;). The generated texture is not obtrusive but will have flaws if you zoom in. Score (6-7): Details are complete and reasonable. We also show some examples and corresponding reason in Table A-2.

Prompt with Reconstruction

Prompt with Reconstruction

BRAHIA

BRAHIMA

Overall Feeling. The overall feeling given by the picture, whether it is reasonable and consistent with the objective facts. We directly show some examples and corresponding reasons in Table A-3.

A.2 Labeling Platform

The visual demonstration of labeling platform is shown in Fig. A-1. In the right-side, we aggregate three reconstruction results with the prompt images. The labeling is annotated in the left-side, where for each image, people need to label 3 scores by aforementioned 3 different criteria, ranging from 0 to 7.

Prompt with Reconstruction (from left to right are Recon-1, 2, 3 and the prompt )

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A.3 License

The license of the proposed dataset is CC BY-NC 4.0 , which allows creators to share their work with others while retaining certain rights but gives a restriction on commercial use.

B More Application Visual Demonstrations

To further verify the effectiveness of the proposed method, this section further visualizes more scenes for the application of the proposed method (as demonstrated in Sec.5.4).

B.1 Application of Image FOV Enlargement

For the task of Image FOV Enlargement, the results are shown in Fig. B-2. We randomly select some picture from Internet and apply center crop on those pictures. We can see that no matter the style of prompt image, e.g., nature photography, oil painting or Chinese painting, the proposed method can consistently generate meaningful and visually pleasing results, which demonstrates the superority of the proposed method.

(a)Outpainting

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B.2 Application of Novel View Synthesis

We also visualize more novel view synthesis examples on the KITTI and DIV2K datasets in Figs. B-3 and B-4. The warping-induced inpainting hole is much more irregular than the FOV Enlargement, which makes the task more challenging. However, the proposed method successfully filled up the missing region. The resulting high-quality reconstructions verify the superiority of the proposed method.

a)

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C Reward Scoring Statistics & Reward Normalization Configuration

C.1 Score Distribution across Different Categories

We count the ratio of prompt with different semantic class in Fig. C-5. Most methods, including SOTA diffusion-based methods, e.g., stable diffusion or Runway, have a large ratio of samples with negative rewards. However, the proposed method achieves the most positive rewards, as shown in the last figure, which validates the necessity and effectiveness of applying such an alignment task to the diffusion in-painting model.

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C.2 Normalization Factors

To facilitate the training of reward model, we normalize the score with s -mean var , where s is raw score data. To remove the data bias for training the reward model, the normalization factors are calculated within different datasets and inpainting patterns. We show the resulting factors in Table C-4. The relatively balanced factors indicate that our labels do not have large biases on different datasets or inpainting pattern.

D More Visualizations

D.1 More Visualizations of Comparisons

In this section, we visually compare the reconstruction results of different methods. As shown in Figs. D-6 and D-7. The comparison of different methods in Figs. D-6 and D-7 indicate that the proposed method could generate high-quality reconstruction compared with other SOTA methods. Notably, the results from our approach show improved clarity and detail retention, which are essential for practical applications.

Prompts

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D.2 More Visualizations of Multiple Sampling

To verify the robustness and consistency of the inpainting model after alignment with our method. We run 5 times with different random seeds on the same prompt to derive different reconstruction results. The results shown in Fig. D-8 demonstrate the stability of the proposed method. We can see the proposed method generate high-quality and visually pleasing results under different conditions.

E More experimental results

E.1 Comparisons with Additional inpainting Methods

We have experimentally compared with some inpainting Methods, i.e., PowerPaint [78], BrushNet [79] and Hdpaint [80] . For all these methods, we assessed performance using their publicly released models. As shown in the Table E-1 and Table E-2, our method significantly outperforms all

the compared methods, particularly in perceptual metrics such as T2I and our Reward score. Besides, BrushNet employs roughly twice the number of parameters as our model, and HdPaint requires 15 times more computational time (experimented on NVIDIA GeForce RTX A6000 GPUs), making it impractical for real-world applications.

E.2 Comparisons with other reinforcement learning methods.

we have experimentally compared our method with some other reinforcement learning methods in Table E-3. Specifically, we simply summarize the implementation of each method. Human Preference Score [13] learns a negative prompt to map the diffusion process to low-quality samples. Then, in the inference process, the negative sample is utilized in the classifier-free guidance (CFG) to push the generation trajectory away from low-quality samples. ImageReward [54] trains a reward model and then applies the reward model as a loss metric to end-to-end optimize the diffusion model accompanied by a reconstruction loss. We also conduct the ablation study on reward training strategy in Table 3 in our paper. Our method employs a regression-driven training strategy, while ImageReward a classification-drive strategy. DPOK [81] simultaneously optimizes the whole trajectory of a reverse diffusion process and utilizes the KL divergence to panel the regularization, avoiding a large distribution shift. D3PO [82] adopts the RL strategy from direct performance optimization (DPOK), directly optimizes the model on the reward labeling data to minimize the probability of low-quality samples and increase the probability of high-quality samples.

E.3 Compared with SD xl ++

Currently, their is also a kind of diffusion-inpainting network like SD xl ++ [66], which requires a complete image to redraw the mask region and can generate a visually pleasing result. However, their performance would be dramatically reduced without the complete image. To validate the characteristics of SD xl ++, we carry out experiments to examine its performance.

F Details & Reward

F.1 Training Curve

We present training curves of various methods for comparison. The curves for '1.4BaseLine' and '1.4Boundary' (Ours) demonstrate our modification's acceleration, reaching 0.35 rewards first. Our approach converges with the fewest training iterations.

0.5

0.4

MeanRewards

0.3

0.2

BaseLine

(WR=69.57%)

0.1

1.4BaseLine-(WR=65.95%)

1.4Boundary(WR=71.27%)

0.0

Q

1000

2000

3000

4000

5000

Steps

F.2 Details between Eq. 4-5

According to the [55], we derive a similar process for the linear reward regression on feature space, where Z denotes the latent of an image.

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Peer reviews:
comments: SUMMARY:
This paper tries to align diffusion models for image inpainting with human aesthetic standards via a reinforcement learning framework.

STRENGTHS:
1. This paper proposes aligning diffusion models for image inpainting with human preferences by integrating human feedback through reinforcement learning, which improves the quality of generated images.
2. This paper presents a dataset containing 51,000 inpainted images annotated with human preferences, addressing the lack of evaluation datasets for image inpainting.
3. The proposed method can be applied to various applications, such as image extension and novel view synthesis, providing visually impressive results.

WEAKNESSES:
1. There has been a lot of work introducing human preferences into diffusion models, such as Human Preference Score (ICCV 2023), ImageReward (NeurIPS 2023), DPOK (NeurIPS 2023), and D3PO (CVPR 2024).

   a. This paper does not fully elaborate on the work related to diffusion models with human preferences.

   b. It is necessary to supplement the comparison with these methods, including differences in methodology and advantages in experimental results.

2. A user study is necessary to evaluate the quality of the generated results.

QUESTIONS:
See Weaknesses.

LIMITATIONS:
The authors outlined potential future directions in the conclusion.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2024-07-12

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper makes the first attempt to align diffusion models for image inpainting with human preferences by integrating human feedback through reinforcement learning. The authors theoretically deduce the accuracy bound of the reward model, modulating the refinement process of the diffusion model to robustly improve both efficacy and efficiency. Additionally, they construct a dataset containing 51,000 inpainted images annotated with human preferences.\n\nSTRENGTHS:\n1. This paper provides theoretical insights into the upper bound of the error of the reward model, ensuring the reliability and accuracy of the reinforcement learning process.\n2. This paper presents a dataset for image inpainting tasks that incorporates human aesthetic preferences. This dataset will facilitate further research into the evaluation of image inpainting models and aid in generating high-quality images that better align with human aesthetics.\n\nWEAKNESSES:\n1. Since reward models and feedback learning trained based on human preferences have been introduced in Text-to-Image Generation (T2I), it seems they are simply applying the same process from T2I to Image Inpainting. This work should clarify the distinctions from T2I to highlight its innovation.\n2. The core contribution of this paper is Equation 11; however, its rationale, implementation details, and effectiveness lack explanation and validation. First, the selection of hyperparameters $k$ and $b$ is not addressed, despite their importance. Second, there is a lack of detailed explanation from Equation 11 to its implementation in the model. Third, the choice of using $e$ instead of other functions needs to be validated. Finally, the description of the ablation study on $k$ in Table 3 is unclear, making it difficult to assess its effectiveness. There is also a lack of ablation analysis for the $b$ and $e$.\n3. The weights assigned to the three scores in the manuscript are [0.15, 0.15, 0.7]. Why are these scores combined into a weighted score instead of being used individually? Furthermore, how were these weights determined? The basis for this weighting is unclear.\n4. What does the \"Rank\" metric in Table 2 signify? It lacks explanation.\n5. There are details missing regarding the dataset annotation. Such as the number of annotators employed, whether training was required for them, and the amount of time each individual spent on the annotation task.\n\nQUESTIONS:\nPlease refer to the weaknesses.\n\nLIMITATIONS:\nThe authors provide the future work in the last section.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper is the first to use reinforcement learning in diffusion-based image synthesis. This significantly improves the quality since image synthesis is usually a one-to-many mapping, which may not be suitable for conventional learning methods. To generate reward functions for RL, this paper also gathers and releases a new dataset on image synthesis with human preference annotation. Additionally, the paper computes the theoretical upper bound on the error of the reward for more efficient RL training.\n\nSTRENGTHS:\n1. Release a new dataset for image inpainting and outpainting benchmarks with human preference annotation. The generation process of the dataset is well-documented.\n2. First incorporate RL with diffusion-based method on image inpainting and outpainting.\n3. The authors provide extensive experiments, supplementary materials with results on multiple datasets, and a project page to demonstrate their results.\n\nWEAKNESSES:\n1. Minor question on the motivation of amplification factor.\n2. Possible missing references.\n\nQUESTIONS:\n1. In L152, how often do these \"relatively large errors\" occur? Will the relatively smaller errors balance out these large errors? Since the amplification $\\gamma$ greatly improves the performance, does this suggest that the errors occur frequently so that the static $\\gamma$ do not handle these very well? From Fig.2, it is shown that the range of $\\| z \\|_{v^{-1}}$ is [0, 1]. Does amplification $\\gamma$ for $k=0.05$ and $b=0.7$ have a range of [1.9, 2.0] (which seems narrow and thus static)?\n\n2. How does this paper compare to [1]? [1] leverage the BERT model for reward scoring. How do you justify the choice of human evaluation instead of foundation models for reward function? Do you have ablation experiments to backup the decision?\n[1] Black, Kevin, et al. \"Training diffusion models with reinforcement learning.\" arXiv preprint arXiv:2305.13301 (2023).\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-13"}

comments: SUMMARY:
This paper attempt to align diffusion models for image inpainting with human aesthetic standards through reinforcement learning framework. To train the model, this paper construct a dataset containing 51,000 inpainted images annotated with human preferences. Extensive experiments on inpainting comparison and downstream tasks, such as image extension and 3D reconstruction, is provided in this paper.

STRENGTHS:
1. This paper is well presented and easy to read.
2. This paper provids detailed experiments to validate the effectiveness of the proposed method.
3. This paper have collect a small scale dataset for human preference of image inpainting results, which can be useful for the ressearch field.

WEAKNESSES:
1. This paper seems to use a common reinforcement learning pratice on text-to-image diffusion model. What is the novelty of the proposed method? Why would this practice better than simply finetuning the model on high-quality (human prefered) inpainting data?

2. There are missing citations and comparisons with the following methods:
   - A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting
   - BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion
   - Hd-painter: High-resolution and prompt-faithful text-guided image inpainting with diffusion models

3. Can you provide an ablation of how the training data quantity influence final results? The training dataset only contain 17,000 images and 51,000 inpainted samples, which is quite small in diffusion model training.

QUESTIONS:
See weekness

LIMITATIONS:
The authore have include limitation in the paper, but I would recommend the authore discuss more on the limitation and negative societal impact.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 2
DATE: 2024-07-14