Title: Personalized Image Editing in Text-to-Image Diffusion Models via Collaborative Direct Preference Optimization
Venue: NeurIPS 2025
Accepted: yes
Keywords: image editing

t2i

personalization dpo
Abstract: Text-to-image (T2I) diffusion models have made remarkable strides in generating and editing high-fidelity images from text. Yet, these models remain fundamentally generic, failing to adapt to the nuanced aesthetic preferences of individual users. In this work, we present the first framework for personalized image editing in diffusion models, introducing Collaborative Direct Preference Optimization (C-DPO), a novel  method that aligns image edits with user-specific preferences while leveraging collaborative signals from like-minded individuals. Our approach encodes each user as a node in a dynamic preference graph and learns  embeddings via a lightweight graph neural network, enabling information sharing across users with overlapping visual tastes. We enhance a diffusion model's editing capabilities by integrating these personalized embeddings into a novel DPO objective, which jointly optimizes for individual alignment and neighborhood coherence.  Comprehensive experiments, including user studies and quantitative benchmarks, demonstrate that our method consistently outperforms baselines in generating edits that are aligned with user preferences.

Personalized Image Editing in Text-to-Image Diffusion Models via Collaborative Direct Preference Optimization

Abstract

Text-to-image (T2I) diffusion models have made remarkable strides in generating and editing high-fidelity images from text. Yet, these models remain fundamentally generic, failing to adapt to the nuanced aesthetic preferences of individual users. In this work, we present the first framework for personalized image editing in diffusion models, introducing Collaborative Direct Preference Optimization (C-DPO), a novel method that aligns image edits with user-specific preferences while leveraging collaborative signals from like-minded individuals. Our approach encodes each user as a node in a dynamic preference graph and learns embeddings via a lightweight graph neural network, enabling information sharing across users with overlapping visual tastes. We enhance a diffusion model's editing capabilities by integrating these personalized embeddings into a novel DPO objective, which jointly optimizes for individual alignment and neighborhood coherence. Comprehensive experiments, including user studies and quantitative benchmarks, demonstrate that our method consistently outperforms baselines in generating edits that are aligned with user preferences.

User 5: likes glossy,

User 6: likes neon, dislikes wood, pink, …

1 Introduction

Text-to-image (T2I) diffusion models have achieved remarkable success in visual content generation, enabling high-quality image synthesis from textual descriptions [32, 31, 18]. Building on this success, recent advancements have begun to repurpose diffusion models for image editing - allowing users to modify a given image via text prompts or other guidance [42, 5]. Despite progress, achieving a desired edit often demands substantial manual effort. Existing editing approaches do not account for the individual user's preferences. They treat image editing as a one-size-fits-all problem: the model does not adapt to a particular user's style, and it assumes the same definition of a 'good' edit for everyone.

In other domains like natural language processing, it is well recognized that different users have unique tastes and requirements, and models can be adapted accordingly [38, 30]. Analogously, in image editing each user may exhibit distinct preferences - one user might favor images with brighter, more saturated colors, while another prefers muted tones and centered composition. Current text-toimage models and editors ignore these nuances, effectively optimizing for an average preference that may not align with any particular user. This lack of personalization means users must continuously correct or fine-tune outputs that do not suit their aesthetic, which limits the practicality of these AI editors in real creative workflows.

In this work, we take the first step towards personalized image editing by proposing a framework that learns and adapts to an individual user's editing preferences. Our method models how each user prefers their images to be modified, capturing aspects such as stylistic choices, color palettes, object attributes, and other consistently favored visual traits. By learning from user-specific preferences, our framework aligns image edits with the user's intent, producing results that better reflect their personal aesthetic. Importantly, we also recognize that collaboration across users can amplify personalization. Users often fall into clusters with overlapping tastes; by sharing preference information among like-minded users, we can generalize better from limited data. Consider a home-decor hobbyist who frequently edits interior photos to give them a rustic living-room feel. They always add a stone fireplace and distressed-leather sofa, but they have never thought to include exposed wooden ceiling beams. Several like-minded users in our preference graph routinely pair the fireplace-and-sofa combo with rough-hewn ceiling beams to complete the rustic look. Even though the target user has never requested the beams explicitly, our collaborative mechanism learns this object-level association from neighboring users' histories and can automatically insert the beams in future edits. The result is an image that remains true to the user's rustic touches while enriching the scene with an additional detail they are likely to appreciate.

In this paper, we propose a novel Direct Preference Optimization (DPO) framework for collaborative personalized editing. Specifically, we introduce a DPO-based learning approach that aligns a diffusion model's image edits with individual user preferences while simultaneously leveraging feedback from a community of users. To enable this capability, we propose a novel synthetic dataset of user editing preferences; a rich corpus of editing examples with annotations linking them to specific users and their satisfaction. We represent the users in this dataset as nodes in a graph, which captures similarities in their editing behavior. This graph-based collaborative learning, together with the DPO objective, allows our model to learn a wide spectrum of personalized editing styles within a single unified model. In summary, our contributions are three-fold:

To the best of our knowledge, we introduce the first formulation of personalized text-toimage editing. We define a new problem setting where an editing model is tailored to an individual user's preferences, moving beyond the one-size-fits-all paradigm in image editing.

Wepropose a novel training framework called, Collaborative Direct Preference Optimization, introducing a graph-structured regularization term into the DPO loss, explicitly modeling and leveraging collaborative relationships among user embeddings. This structured collaboration allows the model to capture nuanced preferences implicitly shared among like-minded users.

We curate a novel synthetic dataset comprising 144K editing preferences which includes annotated examples of edits grouped by user. This dataset provides a valuable benchmark for studying personalization in image editing and facilitates quantitative evaluation of models in this setting.

Our source code and dataset are publicly available at http://personalized-editing. github.io .

2 Related Work

Text-to-Image Editing Text-to-image diffusion models [36, 14, 34, 32] are frequently used for both image generation and editing because of their high-fidelity generation capabilities. Instruct Pix2Pix [5] enables flexibility across edit types with user prompts without requiring task-specific supervision, however, its performance can degrade for highly detailed or spatially complex edits. Blended Latent Diffusion [3] and SDEdit [23] provide more fine-grained control with diffusion models whereas ControlNet [42] conditions the model on additional structural inputs to maintain the spatial consistency while also allowing for localized edits.

User Preference Optimization Reinforcement Learning from Human Feedback (RLHF) [26] has been used to optimize Large Language Models (LLMs) [6, 29, 37] and multimodal LLMs [2, 22, 8, 1] to better align with human preferences. It involves training a reward model based on pairs of preferred and rejected data to guide the fine-tuning of the language model. A simpler alternative to RLHF is Direct Preference Optimization (DPO) [30], which directly optimizes the model parameters on preference data rather than the need for a separate reward model or reinforcement learning loop. The applications of DPO have extended beyond LLMs to vision domains. Adaptations such as Diffusion-DPO [38], have been proposed to similarly align diffusion models to generate images in accordance with human preferences. DPO has been used to further align models with individual users rather than an entire population [20].

Personalized Image Generation The expressive capabilities of T2I models have spurred research in user personalization. Recent methods have aimed to fit the preferences of an individual user. ViPER [35] fine-tunes a VLM on a dataset of AI-generated user comments on images to extract information on user preferences. It then includes these preferences in the prompt and classifier-free-guidance scale to align the model with the user during generation. However, the dependence on detailed comments provided by the user limits its ability to tailor specific image features in the absence of high-quality comments. Other methods have employed reward optimization to learn to represent user preferences in generation. PASTA [24] employs a reinforcement learning agent to iteratively refine a text prompt based on sequential user interactions before feeding to a T2I model. PPD [7] uses a VLM to compare liked and disliked images to train additional cross-attention layers using a Diffusion-DPO reward objective. Pigeon [40] trains a mask generator for user history and feeds to a multimodal LLM trained with a DPO objective to generate visual tokens used to guide the diffusion model. Although these methods aim to learn user preferences across individual history, they fail to consider that some user preferences may not exist within a user's own history, but can be inferred from that of similar or relevant users. Furthermore, none of these methods have addressed the task of user-personalized image editing , a much more complex task that requires maintaining consistency across edits while also capturing the nuanced editing preferences of users.

3 Background

The alignment of large language and diffusion models is often framed as a problem of preference learning in which a model is encouraged to rank preferred outputs higher than rejected ones. Traditional pipelines first train a separate reward model from pairwise human-preference data and then maximize the expected reward with reinforcement learning (RLHF) [26]. Although effective, RLHF adds algorithmic complexity, instability, and significant compute overhead.

Direct Preference Optimization. [30] recently introduced Direct Preference Optimization (DPO), a lightweight alternative that removes the explicit reward model and RL stage. Let D = { ( x i , y + i , y -i ) } N i =1 be a dataset of prompts x i paired with a chosen response y + i and a rejected response y -i . Denote by π θ ( · | x ) the conditional distribution of the trainable policy and by π ref ( · | x ) a frozen reference policy (e.g. the pre-SFT checkpoint). DPO maximizes the log-odds that the trainable policy assigns higher probability to y + than to y -, while staying close to the reference. The resulting objective is a simple logistic loss

1.Supervised FineTuning

2.CollaborativeDPO

InstructPix2Pix

"Himalayas

LLM

Mountains

"Changethe

"A desk chair"

Night Sunset"

sunsettoa

GNN

SimilarUsers

"Suggest a single,cohesive

Textinstruction

sunrise"

hu

'Suggesta single,cohesive

t

(n)Na

image-editing instruction"

t

MLP

image-editinginstruction"

LLM

Tref

C-DPO

Tref

+

3.PersonalizedEditing

"Make it neon"

T0

LM

"Makethe

User

Text

"Makeitrainbow"

y

(u, Ic,t)

chairneon"

EDIT

Embedding

Embedding

where σ ( · ) is the sigmoid, β > 0 is a temperature controlling conservatism, and ∆ θ and ∆ ref are defined as

Text-to-image diffusion models have become the leading framework for high-fidelity image generation from natural language inputs. These models iteratively denoise random noise into coherent images conditioned on text prompts, leveraging powerful encoders such as CLIP [28]. Foundational works like DALL·E 2 [31], Imagen [34], and Stable Diffusion [32] have shown remarkable capabilities in producing semantically aligned and visually diverse images. These systems benefit from training on large-scale datasets using latent diffusion architectures that balance quality and efficiency. Extensions such as InstructPix2Pix [5] and SDEdit [23] introduce mechanisms for editing existing images through text-based instructions. However, these approaches remain general-purpose and do not adapt to individual users' stylistic preferences, limiting their applicability in personalized editing scenarios.

4 Methodology

We introduce Collaborative Direct Preference Optimization (C-DPO), a novel training framework that extends DPO by incorporating user-specific embeddings and graph-based collaboration for personalized image editing. Section 4.1 details our collaborative loss, which aligns edits with both individual and neighbor preferences, along with our user conditioning and training strategy. Section 4.2 describes personalized image editing via our diffusion-based backend. Fig. 2 gives an overview of our methodology.

4.1 Collaborative Direct Preference Optimization (C-DPO)

Direct Preference Optimization (DPO) has recently emerged as a stable and efficient alternative to RLHF for aligning models with human preferences. However, in its standard form, DPO is fundamentally limited for the task of personalized image editing. First, it lacks cross-user collaboration: all preference pairs are treated as if originating from a single anonymous user, leading the model to converge toward an average policy that ignores the diverse and often overlapping aesthetic preferences of real users. Second, as shown in Eq. 1, DPO assumes unimodal textual conditioning, which restricts its ability to incorporate structured, non-textual information such as user relationships or historical preferences encoded in a knowledge graph. These shortcomings prevent standard DPO from leveraging either personalization or collaboration, both of which are essential for effective user-specific image editing.

4.1.1 Modeling User-Edit Preferences in a Graph

To overcome the limitations of vanilla DPO, we extend the editing policy to explicitly condition on user identity and incorporate collaborative structure through a graph-based model of user preferences. We define the personalized editing policy as:

where u ∈ U denotes the user identifier, I c is the caption describing the base image, and t is the high-level editing instruction. Each user is represented by a learnable embedding g ϕ ( u ) ∈ R d , which encodes the user's stylistic preferences and conditions the model to generate personalized outputs.

To enable collaboration across users with overlapping preferences, we construct a heterogeneous, bipartite, undirected user-preference graph:

where U is the set of users initialized with a learnable embedding vector, P denotes preference attributes (e.g., color palettes, textures) initialized with a dense text embedding derived from a pretrained language model, and E consists of edges between users and their preferred or rejected attributes. This structure captures shared editing behavior and supports collaborative inference through shared interactions.

We encode the graph using a lightweight graph neural network (GNN), which aggregates neighbor information to compute a contextualized embedding h u for each user [12, 17]. Our GNN consists of a 2-layer GraphSAGE encoder and a decoder composed of 2 linear layers. We pretrain the GNN on an auxiliary supervised edge classification task by predicting user-attribute links as positive or negative, encouraging the encoder to learn semantically meaningful and stylistically distinct user embeddings.

Acore benefit of our approach is that it generalizes to new users without requiring retraining, enabling both scalability and practicality. GraphSAGE inductively learns a set of aggregation functions that takes as input a node's neighborhood. When an unseen user arrives at inference, they are first added as a node with a zero-initialized feature vector in the existing graph and connected via edges to liked and disliked attributes. This new user's neighborhood is passed through the learned aggregation functions to compute the user's node embedding. While periodic fine-tuning as the graph expands may further refine embedding quality, it is not necessary for inference. Further GNN details can be found in Appendix B.

To quantify similarity between users, we compute a normalized similarity score between users u and v based on the number of shared one-hop neighbors (i.e., common preference attributes):

where P ( u ) ⊆ P is the set of attributes associated with user u . Here, P ( u ) denotes the set of preference attributes connected to user u , and the denominator is the maximum number of shared neighbors across all user pairs in the graph. These scores are later used as edge weights in our collaborative preference objective, allowing the model to softly attend to information from stylistically similar users. By conditioning the editing policy on graph-informed embeddings and user identity, our framework supports both fine-grained personalization and generalization through collaborative structure, addressing the core deficiencies of traditional DPO for personalized image editing.

4.1.2 Collaborative DPO Loss

To align the editing policy with both individual user preferences and insights from like-minded users, we introduce a collaborative extension to the DPO objective. The core idea is to preserve strong per-user alignment while softly regularizing the model using preference signals from a user's graph neighbors.

For a mini-batch of preference tuples, ( u, I c , t, y + , y -) we define the Collaborative Direct Preference Optimization (C-DPO) loss as:

where N ( u ) denotes the K nearest neighbors of u in G , and λ governs the strength of collaboration. Weighted averaging ensures that the collaborative term does not overpower the individual objective. Each L DPO is instantiated via 1, but with the policy now conditioned on the user information . Because the reference policy π ref remains user-agnostic , the collaborative term pulls the user-conditioned policy toward neighbor preferences only where the data supports it , avoiding degenerate collapse to the global average. Equation 7 generalizes DPO to a multi-user setting. The first term preserves the per-user alignment benefits of vanilla DPO, whereas the second term implements a graph-regularized collaborative filter that shares statistical strength among like-minded users-crucial for data-sparse personalization.

4.1.3 Model Optimization Strategy

We adopt a two-stage training pipeline. First, we perform supervised fine-tuning (SFT) on paired base image and editing instruction data [5] to teach a pretrained language model to generate high-quality editing instructions conditioned on the image caption I c and text instruction t , but without any user information. Specifically, we train a LoRA adapter [15] on this task, enabling efficient adaptation of the base LLM for instruction generation. The resulting adapter is frozen and serves as the reference model π ref during subsequent preference-based training.

Afterwards, we fine-tune a separate copy of the same LoRA adapter using our collaborative DPO objective (Equation 7) over our user preference dataset. This adapter serves as the policy model π θ .

To personalize the model, we inject user-specific information as soft prompt tokens. For each user u , we compute a graph-based embedding h u using our GNN encoder. These embeddings are passed through a two-layer MLP and converted into a fixed set of soft tokens, which are prepended to the textual instruction t . This approach enables user conditioning without modifying the base language model architecture. Both the GNN and MLP components are trainable during C-DPO along with the LoRA, though we apply a lower learning rate to the GNN to preserve the structure learned during pretraining. During C-DPO training, the model learns not only from each user's preferences but also from the preferences of graph-connected neighbors, promoting better generalization across similar users while preserving individual alignment.

4.2 Image Editing with Personalized Editing Prompt

Once a personalized editing prompt is generated by our C-DPO model, we translate it into a concrete pixel-level transformation using a modular diffusion-based editing backend. We utilize FLUX [18], a large-scale rectified flow transformer developed for high-fidelity text-to-image generation, augmented with ControlNet [42] to ensure faithful, spatially constrained edits.

For a target user u , input image caption I c , and high-level textual cue t , our tuned C-DPO model outputs a personalized prompt :

where f θ denotes the tuned C-DPO policy model. The output p u is a natural-language instruction tailored to user u 's preferences, and is directly consumable by any T2I-style sampler. For example: 'change the sofa's color to hot pink.' . This setup enables faithful and personalized image edits that reflect the user's intent with high semantic precision.

5 Experiments

We evaluate our personalized-editing framework with extensive qualitative and quantitative experiments. Qualitative experiments showcase how edits are personalized based on different users, while quantitative experiments measure how well the edited images reflect each user's unique style and preferences across thousands of (image, user) pairs.

Experimental Setup We evaluate our method's ability to generate personalized edits for a given base image. To model user-specific edit instructions, we fine-tune QWEN2.5 [27] using LoRA [15]. For image editing, we employ FLUX.1-DEV 2 , enhanced with ControlNet 3 using a conditioning scale of 0.4 and Canny as the condition. We set the collaborative term strength, λ , to 0.15. While our full pipeline can be trained on a single NVIDIA L40 (48GB) GPU, we use multiple GPUs to accelerate training. During the initial supervised fine-tuning phase, we train for one epoch on the InstructPix2Pix training set [5] using 2 GPUs, completing in just under 3 hours. For collaborative tuning via C-DPO, we train for 3000 steps on our user editing preference dataset using 3 GPUs, with total training time of approximately 1 hour. We divide our dataset into training and test splits, with approximately 2,900 users in the training set and 100 users reserved for testing. Once trained, our system performs a personalized image edit for a given user in around one minute. Because our framework is editing-model agnostic, this process can be accelerated to about one second when paired with a faster backend such as TurboEdit [10]. Full training details and parameter configurations are provided in Appendix G.

Dataset To the best of our knowledge, no existing dataset captures the user-specific preferences necessary for personalized image editing. Therefore, we present a structured benchmark of 3,000 synthetic user profiles for studying personalized image editing in T2I diffusion models, spanning 144K samples on individual like/dislike preferences. To closely mimic the diversity of behavior, preferences and aesthetic tastes in the real world, we synthetically generate our data with distinct user demographic configurations that cover axes such as age, geography, and socioeconomic status. These configurations serve as a guide to the generation of detailed editing preferences, including themes, tones, overlays, likes, dislikes, and persona-aligned prompt examples. To simulate realistic editing interactions, each user is assigned four randomly sampled image captions from 80 COCO [21] categories. For each base image, we generate two pairs of preferred and rejected editing instructions across six edit types based on individual user preferences, yielding 48 annotated editing instructions per user. This rich data set serves as the foundation for training and evaluating our collaborative personalization framework. Our data set enables a rigorous evaluation of user alignment and preference modeling in diffusion-based editors. Our dataset is publicly available at http://personalized-editing.github.io .

5.1 Qualitative Experiments

We first showcase how identical objects are edited in distinct ways according to individual user preferences. Fig. 1, Fig. 3 (a) and Fig. 4 show several objects edited for different users with diverse profiles. Our system aligns each edit based on the likes and dislikes that the user has previously expressed. For instance, for a user ( labeled as 'Imaginative Child' ) who favors unicorns, rainbows, and vibrant, playful palettes (see Fig. 3 (a) and Fig. 4), our system infuses such preferences into a wide range of objects, from helmets and guitars to watchtowers. Our framework is also flexible to allow users to provide additional guidance while performing personalized edits. Fig. 3 (b) illustrates three distinct additional editing instructions such as ice, lava, and monster themes. As shown, our framework effectively combines user-specific preferences with the supplied guidance, producing coherent edits that align with both the explicit instruction and the underlying aesthetic of each user.

5.2 Quantitative Experiments

Since no prior work directly tackles personalized image editing, we evaluate our proposed framework along two complementary dimensions: a) Prompt-level alignment, measuring how well the editing instruction aligns with each user's liked and disliked concepts, b) Image-level alignment, assessing how closely the resulting edited image reflects the user's preferences based on user's liked and disliked concepts.

Prompt-level alignment We compare our method on a diverse set of strong baselines that vary in personalization capability, modeling assumptions, and training objectives. This comparison allows us to isolate the contributions of user conditioning and collaborative learning.

First, we provide two generic vision-language baselines: Qwen [27], a vanilla large-scale language model that takes only the image description as input and generates a generic edit prompt without awareness of user identity; and LLaVA [22], another open-source vision-language model that simi-

Imaginative Child

Futuristic Techie

Input

+ice

+lava

+monster

+ice

+lava

+monster

a) Personalized edits for individual profiles

b) Personalized edits with user-provided instructions

larly lacks any personalization signal and serves as a second reference point for non-user-specific performance. Both models act as task-agnostic baselines with no preference alignment.

Next, we consider Qwen (SFT), a fine-tuned variant trained on 150,000 generic edit-instruction pairs from the InstructPix2Pix dataset [5]. While this model is editing-aware, it lacks user conditioning, allowing us to assess the effect of task-specific fine-tuning alone. We also compare with Vanilla DPO [30], trained on pairwise preference data sampled from our dataset but without access to user identifiers or relational information. This model tests whether learning from global preference signals alone provides any personalization benefit. To evaluate the impact of incorporating explicit user identity, we also compare with a user-aware variant, DPO-User, that uses a learnable embedding for each user ID but does not leverage any collaborative structure. This setup mimics the PPD objective [7] and represents a personalization mechanism based on private embeddings alone. Finally, our proposed method - Collaborative-DPO extends DPO-User by introducing graph-based collaboration, allowing user representations to evolve through interactions with stylistically similar neighbors. This model constitutes the full version of our framework.

To compare these methods fairly, we standardize the prompting format across all models. Each method receives an input of the form: 'Given this user's profile <profile>, suggest an edit for the following image: <image description>.' We construct three evaluation conditions reflecting different profile completeness: (i) Like+Dislike, where both liked and disliked examples are available; (ii) Likes-only; and (iii) Dislikes-only. For each condition, we measure CLIP-text similarity between the generated instruction and the user's ground-truth liked instructions. As shown in Table 2, our C-DPO

Edit 4

Original

Edit 1

Edit 2

Edit 3

model achieves the highest score across all configurations, indicating its ability to recover user intent from partial or complete preference signals.

Image-level alignment Next, we report DINO and CLIP scores computed between the original input image and its personalized edited version (see Table 3). The results indicate that our method better preserves the original identity compared to other approaches. We also compute HPS and CLIP-T scores between the personalized edited images and the textual description of the original image, further demonstrating that our method achieves higher alignment with the original content than competing methods. Please refer to Appendix A for additional quantitative experiments and comparisons with state-of-the-art editing methods.

Ablations Please refer to Appendix C for ablations on collaborative scale parameter λ , neighborhood size K , choice of editing method, and prompt-engineering baseline.

Personalized Image Generation Our method can also be repurposed for personalized image generation as opposed to editing . Please visit A.2 for more details.

5.3 User Study

We further validate our results through a user study to assess perceptual personalization quality. We ran a crowd-sourced evaluation on Prolific.com with 50 participants. We randomly sampled 10 synthetic user profiles from our test split to cover a range of stylistic tastes. For each profile we first showed a short text description of what aspects this user likes and dislikes based on their previous edit history. Participants then judged three target images that had been edited

by three competing methods (Vanilla DPO, DPO-User and Ours). Each edited image was displayed alongside its text prompt; the order of methods and the order of questions were fully randomized per task to prevent position bias.

Adapted from the user study of [38], workers were asked the following questions for every image: Q1: General Preference - 'Which edited image would this user prefer, given the prompt?' Q2: Visual Appeal - 'Ignoring the prompt, which image is more visually appealing?'. Table 1 summarizes responses from the 50 participants across the two evaluation questions. In every category our method secures the highest win rate by a large margin. These results indicate that, human judges consistently favor the edits produced by our method. We further conduct a user study with real-user preference data, replacing the synthetic preferences used in earlier experiments. In this study, participants provided their own liked and disliked concepts, and we evaluated how well the edited images aligned with these individual preferences. Please refer to Appendix E for detailed methodology and results.

6 Discussion

Broader Impact and Limitations By aligning text-to-image diffusion models to each individual's aesthetic, our framework can lower the barrier to high-quality visual content creation, reducing repetitive prompt-engineering cycles and enabling non-experts, including artists with motor impairments or limited technical skills to achieve their desired edits more efficiently. On the negative side, since our system tailors outputs to inferred tastes, it also risks reinforcing aesthetic 'filter bubbles,' narrowing users' exposure to diverse visual styles. In terms of technical limitations, when a new user lacks both personal edits and close neighbors in the graph, our model defaults to a generic editing. Furthermore, because our framework depends on off-the-shelf models such as Flux and ControlNet, any biases embedded in those backbones may propagate to the resulting edits. As for data, we choose to generate our data synthetically because of the scalability and budget constraints of crowd-sourcing, however, obtaining real-world user preference data presents an exciting future research direction by gathering more authentic and nuanced information.

Conclusion We presented the first framework that unifies personal and collaborative preference learning for editing with text-to-image diffusion models. Our novel objective function C-DPO (i) models each user's preference through learned graph embeddings, (ii) propagates information across a similarity graph of like-minded users, and (iii) couples the resulting personalized prompt with a T2I diffusion model to deliver high-fidelity, user-aligned edits. Our study demonstrates that collaborative signals can be harnessed without sacrificing individual customization, paving the way for practical editing assistants that adapt fluidly to diverse aesthetics. Future research will focus on extending the framework beyond still images such as video domain , where temporal coherence and multi-object consistency introduce new challenges, which remains an exciting direction for follow-up work.

References

Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al.: Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)

Alayrac, J.B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., Ring, R., Rutherford, E., Cabi, S., Han, T., Gong, Z., Samangooei, S., Monteiro, M., Menick, J., Borgeaud, S., Brock, A., Nematzadeh, A., Sharifzadeh, S., Binkowski, M., Barreira, R., Vinyals, O., Zisserman, A., Simonyan, K.: Flamingo: a visual language model for few-shot learning (2022), https://arxiv.org/abs/2204.14198

Avrahami, O., Fried, O., Lischinski, D.: Blended latent diffusion. ACM transactions on graphics (TOG) 42 (4), 1-11 (2023)

Brack, M., Friedrich, F., Kornmeier, K., Tsaban, L., Schramowski, P., Kersting, K., Passos, A.: Ledits++: Limitless image editing using text-to-image models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 8861-8870 (2024)

Brooks, T., Holynski, A., Efros, A.A.: Instructpix2pix: Learning to follow image editing instructions. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 18392-18402 (2023)

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33 , 1877-1901 (2020)

Dang, M., Singh, A., Zhou, L., Ermon, S., Song, J.: Personalized preference fine-tuning of diffusion models. arXiv preprint arXiv:2501.06655 (2025)

DeepMind, G.: Gemini 1: A family of highly capable multimodal models. Technical Report (2023), https://deepmind.google/technologies/gemini/

Deng, C., Zhu, D., Li, K., Gou, C., Li, F., Wang, Z., Zhong, S., Yu, W., Nie, X., Song, Z., Shi, G., Fan, H.: Emerging properties in unified multimodal pretraining (2025), https: //arxiv.org/abs/2505.14683

Deutch, G., Gal, R., Garibi, D., Patashnik, O., Cohen-Or, D.: Turboedit: Text-based image editing using few-step diffusion models. In: SIGGRAPH Asia 2024 Conference Papers. pp. 1-12 (2024)

Ge, Y., Zhao, S., Zeng, Z., Ge, Y., Li, C., Wang, X., Shan, Y.: Making llama see and draw with seed tokenizer (2023), https://arxiv.org/abs/2310.01218

Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. Advances in neural information processing systems 30 (2017)

Herlocker, J., Konstan, J.A., Riedl, J.: An empirical analysis of design choices in neighborhoodbased collaborative filtering algorithms. Information retrieval 5 (4), 287-310 (2002)

Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33 , 6840-6851 (2020)

Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021)

Junseong Kim, Seolhwa Lee, J.K.S.G.Y.K.M.C.J.y.S.C.C.: Linq-embed-mistral:elevating text retrieval with improved gpt data through task-specific control and quality refinement. Linq AI Research Blog (2024), https://getlinq.com/blog/linq-embed-mistral/

Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)

Labs, B.F.: Flux. https://github.com/black-forest-labs/flux (2024)

Li, X., Liu, Z., Guo, S., Liu, Z., Peng, H., Yu, P.S., Achan, K.: Pre-training recommender systems via reinforced attentive multi-relational graph neural network. In: 2021 IEEE international conference on big data (big data). pp. 457-468. IEEE (2021)

Li, X., Zhou, R., Lipton, Z.C., Leqi, L.: Personalized language modeling from personalized human feedback (2024), https://arxiv.org/abs/2402.05133

Lin, T.Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C.L., Dollár, P.: Microsoft coco: Common objects in context (2015), https: //arxiv.org/abs/1405.0312

Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual instruction tuning (2023), https://arxiv.org/ abs/2304.08485

Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J.Y., Ermon, S.: Sdedit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073 (2021)

Nabati, O., Tennenholtz, G., Hsu, C., Ryu, M., Ramachandran, D., Chow, Y., Li, X., Boutilier, C.: Personalized and sequential text-to-image generation. arXiv preprint arXiv:2412.10419 (2024)

Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., Assran, M., Ballas, N., Galuba, W., Howes, R., Huang, P.Y., Li, S.W., Misra, I., Rabbat, M., Sharma, V., Synnaeve, G., Xu, H., Jegou, H., Mairal, J., Labatut, P., Joulin, A., Bojanowski, P.: Dinov2: Learning robust visual features without supervision (2024), https://arxiv.org/abs/2304.07193

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in neural information processing systems 35 , 27730-27744 (2022)

Qwen, :, Yang, A., Yang, B., Zhang, B., Hui, B., Zheng, B., Yu, B., Li, C., Liu, D., Huang, F., Wei, H., Lin, H., Yang, J., Tu, J., Zhang, J., Yang, J., Yang, J., Zhou, J., Lin, J., Dang, K., Lu, K., Bao, K., Yang, K., Yu, L., Li, M., Xue, M., Zhang, P., Zhu, Q., Men, R., Lin, R., Li, T., Tang, T., Xia, T., Ren, X., Ren, X., Fan, Y., Su, Y ., Zhang, Y ., Wan, Y ., Liu, Y ., Cui, Z., Zhang, Z., Qiu, Z.: Qwen2.5 technical report (2025), https://arxiv.org/abs/2412.15115

Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision (2021), https://arxiv.org/abs/2103.00020

Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners. OpenAI Blog 1 (8), 9 (2019)

Rafailov, R., Sharma, A., Mitchell, E., Manning, C.D., Ermon, S., Finn, C.: Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems 36 , 53728-53741 (2023)

Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., Chen, M.: Hierarchical text-conditional image generation with clip latents (2022), https://arxiv.org/abs/2204.06125

Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10684-10695 (2022)

Rout, L., Chen, Y., Ruiz, N., Caramanis, C., Shakkottai, S., Chu, W.S.: Semantic image inversion and editing using rectified stochastic differential equations. arXiv preprint arXiv:2410.10792 (2024)

Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E.L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., et al.: Photorealistic text-to-image diffusion models with deep language understanding. Advances in neural information processing systems 35 , 36479-36494 (2022)

Salehi, S., Shafiei, M., Yeo, T., Bachmann, R., Zamir, A.: Viper: Visual personalization of generative models via individual preference learning. In: European Conference on Computer Vision. pp. 391-406. Springer (2024)

Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models (2022), https://arxiv. org/abs/2010.02502

Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, S., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)

Wallace, B., Dang, M., Rafailov, R., Zhou, L., Lou, A., Purushwalkam, S., Ermon, S., Xiong, C., Joty, S., Naik, N.: Diffusion model alignment using direct preference optimization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8228-8238 (2024)

Wu, X., Hao, Y., Sun, K., Chen, Y., Zhu, F., Zhao, R., Li, H.: Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis (2023), https://arxiv.org/abs/2306.09341

Xu, Y., Wang, W., Zhang, Y., Tang, B., Yan, P., Feng, F., He, X.: Personalized image generation with large multimodal models. In: Proceedings of the ACM on Web Conference 2025. pp. 264-274 (2025)

Zhang, H., Duan, Z., Wang, X., Zhao, Y., Lu, W., Di, Z., Xu, Y., Chen, Y., Zhang, Y.: Nexus-gen: Unified image understanding, generation, and editing via prefilled autoregression in shared embedding space (2025), https://arxiv.org/abs/2504.21356

Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 3836-3847 (2023)

Zhou, X., Lin, D., Liu, Y., Miao, C.: Layer-refined graph convolutional networks for recommendation. In: 2023 IEEE 39th international conference on data engineering (ICDE). pp. 1247-1259. IEEE (2023)

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Table of Contents

A Additional Quantitative Experiments

We conduct additional quantitative experiments to evaluate our personalized image editing framework. Specifically, we compare user-preferred images with the personalized outputs generated by our method using DINO [25], CLIP-I, CLIP-T [28], and HPS [39] metrics (see Sec. A.1).

Furthermore, we demonstrate the effectiveness of our approach for personalized image generation by benchmarking against state-of-the-art methods in this domain (see Sec. A.2).

A.1 Experiments on Personalized Image Editing

Table 2 (main paper) presents a quantitative comparison between our method and other state-of-theart approaches using editing prompts generated by each method, as these baselines only support prompt-level generation rather than direct image outputs. To further evaluate image-level quality, we conduct additional experiments comparing SFT, Vanilla DPO, and User-based DPO against our method. For each method, we generated editing prompts and used Flux ControlNet to produce corresponding images. Results of the quantitative comparisons are shown in Table 4 and Table 3.

First, in Table 4, we report pairwise similarity scores between the user's liked images and the generated personalized images (image-image similarity using DINO-Pref and CLIP-I-Pref), as well as between the personalized edited images and the user's liked prompts (image-text similarity using CLIP-T-Pref). We also include a CLIP-T-Neighbor measure, which evaluates how well the personalized images align with attributes favored by similar users. For each user, we identify their ten most similar users based on the user-preference graph, aggregate the those users liked attributes, and compute the CLIP similarity between these aggregated preferences and the images generated for the target user. As can be seen from the results, our method achieves better results in generating personalized editing images.

A.2 Experiments on Personalized Image Generation

To further validate the effectiveness of our method beyond personalized image editing, we compare it with the state-of-the-art personalized image generation method VIPER [35]. As shown in Table 9 and Fig. 9, our method achieves higher scores when comparing generated images to the user's liked images. Additionally, it delivers superior visual quality and relevance, better aligning with user preferences (see Fig. 9).

A.3 Comparison with State-of-the-Art Instruction-Based Editing Models

To better contextualize our approach within the broader landscape of instruction-based and unified image-editing frameworks, we conducted additional quantitative comparisons against recent state-of-

the-art baselines: BAGEL [9], SEED-LLAMA [11], and Nexus-Gen [41]. For fairness, we provided each model with the same textual user-preference prompts used in our setup, thereby allowing them to leverage equivalent conditioning information without requiring architectural modification.

As summarized in the Table 9, our approach clearly outperformed these methods across standard image editing evaluation metrics, highlighting the distinct advantage of our collaborative personalization framework.

B Detailed GNN Information

Here, we go into more detail regarding graph architecture, GNN construction and training, and generalization to unseen users at inference.

B.1 GNN Construction and Training

We employ a lightweight Graph Neural Network (GNN) to compute contextualized user embeddings by aggregating neighborhood information in a heterogeneous bipartite graph of users and attributes. Each attribute node represents an editing instruction topic (e.g., rustic themes, neon colors, minimalistic textures) and is initialized with a dense text embedding derived from a pretrained language model. Each user node is initialized with a learnable embedding vector. The resulting graph encodes user-attribute relationships via edges that indicate whether a user likes or dislikes a given attribute.

Our GNN architecture consists of a 2-layer GraphSAGE convolutional encoder using mean aggregation [12], followed by an edge decoder comprising two linear layers. Details on specific layer dimensions can be found in Table 12 The model is pretrained on an auxiliary edge-prediction task designed to classify whether a user-attribute link is positive or negative. This task encourages the GNN to learn semantically meaningful user representations grounded in the structure of the user-attribute graph. We train the model on 60% of the edges, reserving 20% each for validation and testing, following standard protocols outlined in foundational works [12, 17].

During pretraining, both liked and disliked user-attribute pairs are leveraged in a supervised edge classification task (e.g., edges of the form (user1, likes, 'neon green') or (user2, dislikes, 'rainbows')). Once pretraining is complete, the classification head is discarded and only the encoder's user embeddings are retained. These embeddings are subsequently used in the full Collaborative Direct Preference Optimization (C-DPO) stage, where user embeddings are combined with target image captions (e.g., 'White ceramic bowl') to construct user-specific positive and negative instruction pairs (e.g., 'Add a holographic pattern to the bowl' as positive, 'Overlay a floral pattern on the bowl' as negative).

B.2 Generalizing to Unseen Users

A central advantage of our approach is that it supports generalization to new users without retraining. Our approach follows an inductive embedding framework (GraphSAGE) rather than a transductive one. Unlike transductive methods, which are limited to fixed graphs, GraphSAGE learns a set of aggregation functions that generalize to unseen nodes by aggregating features from their neighborhoods. This allows our model to efficiently infer embeddings for new users.

At inference time, when a new users arrives, (1) we create a new user node with a zero-initialized feature vector of fixed width, (2) we connect this node to relevant attribute nodes representing the user's liked and disliked attributes. (3) We apply the pretrained GNN encoder's learned aggregation

functions over this new node's neighborhood to compute the user's embedding inductively from the connected attributes and their neighborhood structure.

This process requires no retraining, making it scalable and practical for continual deployment. Although periodic fine-tuning on the expanded graph may further refine embedding quality, it remains optional and is not necessary for inference.

C Additional Ablations

C.1 Ablation on Collaborative Scale Parameter

We conduct an ablation study on the collaborative scaling factor λ , evaluating values of 0 . 01 , 0 . 15 , and 0 . 50 (see Fig. 7). A very low value ( λ = 0 . 01 ) limits the influence of neighbor preferences, effectively reducing the method to a user-based DPO, as neighbors contribute insufficient information to guide personalization. In contrast, a high value ( λ = 0 . 50 ) overly emphasizes neighbor preferences, which can override the user's own preferences and degrade performance. Our results indicate that λ = 0 . 15 provides the best balance, allowing meaningful influence from similar users without overwhelming the target user's preferences.

C.2 Ablation on Neighborhood Size

We further investigate the effect of the collaborative neighborhood size K , which determines how many of the most similar users contribute weighted preference signals in the C-DPO loss 7. As shown in Table 7, smaller to moderate values yield stronger alignment between generated edits and user preferences, as measured by CLIP Like Similarity. Increasing K beyond a certain threshold leads to diminishing returns or even degraded performance, likely due to noise from less relevant neighbors. Although the synthetic nature of our dataset may influence this, the phenomenon is not exclusive to synthetic data and generally occurs in collaborative and graph-based methods, even with real-world datasets [13, 43, 19]. When too many neighbors are aggregated, weaker or less relevant signals tend to dilute the informative collaborative signals from closely related neighbors-a well-documented issue across various real-world collaborative filtering and recommendation tasks.

C.3 Ablation on Editing Method

In our experiments, we used Flux ControlNet as the base editing method. However, our method is applicable to Stable Diffusion-based editing methods as well. To highlight the impact of the underlying editing method, we compared personalized editing results across several Stable Diffusionbased approaches: SDEdit [23], Ledits++ [4], InstructPix2Pix [5], TurboEdit [10] as well as Fluxbased methods including RF Inversion [33] and Flux ControlNet 4 . As shown in Fig. 8, Flux ControlNet consistently delivers superior fidelity and visual quality compared to the other Flux-based methods. Similarly, our personalized editing with Ledits++ performs better than other SD based methods.

C.4 Ablation on Prompt-Engineering Baseline

To further justify the added complexity of our GNN-based collaborative personalization framework, we compare C-DPO against a strong prompt-engineering baseline that directly injects textual user descriptions, including aggregated neighbor information, into the model prompt, without using

GNN-derived embeddings. This baseline represents a simplified but potentially competitive approach where user and neighborhood preferences are expressed through structured textual conditioning rather than learned representations.

As shown in Table 8, our proposed method substantially outperforms this baseline across all metrics. These results indicate that our proposed framework provides a meaningful benefit over simpler, prompt-based personalization methods, effectively justifying the additional complexity introduced by the GNN.

D Additional Qualitative Experiments

D.1 Additional Qualitative Examples

We first showcase how identical objects are edited in distinct ways according to individual user preferences. Fig. 5 shows several objects edited for different users with diverse profiles. Our system adapts each image to the likes and dislikes that the user has previously expressed (see 'User Profile' which describes user's preferences). For instance, the first row depicts a user who prefers playful elements, vivid color palettes and childish elements such as unicorns and rainbows. When this user is presented with a bike or a ceramic bowl, our model automatically adds rainbow-hued paint, pastel gradients, and subtle unicorn-themed playful touches that align with a child-friendly aesthetic, while preserving the underlying object structure in a disentangled manner. On the other hand, when our system is presented with a user who prefers earthy tones and nature, our framework adds natural elements such as leaves, wooden materials and plants.

D.2 Visuals on Personalized Image Generation

We further demonstrate the applicability of our method for personalized image generation by comparing it with the state-of-the-art approach, VIPER [35]. Fig. 9 presents results for two distinct user profiles: the Imaginative Child, who prefers rainbow colors and glitter, and the Glamour Stylist, who favors shiny and metallic elements. As shown, our method captures a broader range of user-specific preferences while maintaining focus on coherent objects. In contrast, VIPER often emphasizes textures or patterns without anchoring them to meaningful objects.

D.3 Comparison with State-of-the-Art Instruction-Based Editing Models

To better contextualize our approach within the broader landscape of instruction-based and unified image-editing frameworks, we conducted additional quantitative comparisons against recent state-ofthe-art baselines: BAGEL [9], SEED-LLAMA [11], and Nexus-Gen [41]. For fairness, we provided each model with the same textual user-preference prompts used in our setup, thereby allowing them to leverage equivalent conditioning information without requiring architectural modification.

User Profile

The  user

is

drawn  to

bright,

whimsical

colors

,

fantastical

elements

,  and

playful  overlays

like

sparkles,

rainbows,

or

unicorn motifs.

They  favor  soft,

glowing  visuals  and  imaginative

edits,

while

rejecting

muted

tones,

minimalist

styles,

and

anything

overly realistic or dull.

This  user  prefers  images  with

earthy  tones

,

natural  textures

,

and  a  calm,  organic  feel.  They

favor

leaf  and  floral  overlays

,

soft lighting

, and

wood or stone

materials

,  while  avoiding

neon

colors

,

synthetic  textures

,  and

digital  or  cartoon  effects

.

Their

edits evoke a grounded, nature-

inspired aesthetic.

The

user

prefers

images

that

convey

luxury

and

elegance

,

with

vibrant  tones

,

glamorous

lighting

,  and

editorial  balance.

Their

aesthetic

favors

high-

contrast

detail

,

bokeh

,

and

refined

textures

like

polished

metal or marble, while rejecting

grunge

,

cartoon

,

and

floral

overlays

in

favor

of

a

sleek,

fashion-forward look.

The  user

prefers  images  with

cosmic textures

,

warped forms

,

and

vivid,

altered

color

schemes

.  They  favor

dreamlike

overlays

,

double exposures

, and

abstract

compositions

that

evoke surrealism, while rejecting

basic  filters

,

clean  commercial

edits

,  and

literal  or  minimalist

styles

.

Edit 4

Original

Edit 1

Edit 2

Edit 3

As summarized in the Table 9, our approach clearly outperformed these methods across standard image editing evaluation metrics, highlighting the distinct advantage of our collaborative personalization framework.

E Additional User Studies

We conducted a two-stage user survey with 10 participants using real-human data. Specifically, we first asked participants to indicate their likes and dislikes attributes from a predefined list of 45 concepts as suggested. This list covers a wide range of colors (e.g. standard colors, neon colors), styles (e.g. watercolor, cyberpunk), textures (e.g. glossy, wood) and other concepts (such as rainbow, butterfly).

Based on these preferences, we generated personalized images using our proposed method. Participants were then sent a follow-up survey featuring personalized edits of five objects: an apple, a mug, a bag, a teddy bear, and a bowl. In this survey, participants viewed both the original and personalized versions of each object and answered the following questions:

Q1: On a scale of 1 to 5 (1=Does not matching my personal taste at all, 5=This is matching my personal taste a lot) can you rate the following image?

Q2: To what extent does the edit preserve the original image while reflecting personalized preferences? (1=Not at all, 5=Very Much)

BeforePersonalization

AfterPersonalization

Our results (Table 10) demonstrate that real users found the images personalized by our framework highly personalized and disentangled.

On the same real-user preference data, we conducted an additional user study with personalized edits for each image to compare baselines. Specifically, users are presented with an object alongside three personalized edits (from Vanilla DPO, User-DPO, and our C-DPO method) where images are shown in an anonymous fashion. Users are asked to pick the edit they most prefer out of 3 options.

F Evaluation Prompt

To ensure a fair comparison across baselines, we adopt a unified structured prompt for all evaluations (see Fig. 10).

G Experimental Details

Various details of our experimental setup are given in Table 12.

Ours

0.350

Like Similarity

0.340

3

0.33

0.01

0.15

0.50

Collaborative Scale ()

Ledits++

Flux+ControlNet

Instruct Pix2Pix

SD Edit

RF Inversion

Profile

Futuristic Techie

Profile

Turbo Edit

'A cup'

'An umbrella'

'A teddy bear'

'A chair

OURS

VIPER

Imaginative Child Profile

Glamour Stylist Profile

System Prompt

You are an expert at providing user personalized image-editing instructions.

User Prompt

Likes: [e.g. colorful and playful styles, unicorn motifs, rainbows] Dislikes: [e.g. minimalistic styles, muted tones, overly realistic imagery]

Original Image Caption: [e.g. "A white ceramic bowl"]

Suggest a single, cohesive image-editing instruction.

Whichedited imagewould this user prefer, given their preferences?

UserPreferences:

Likes:

Futuristic|Sci-fi|Neon colors|Glass&metal textures|Holographic&circuit patterns | LED/glow effects| Cyberpunk/digital styles

Dislikes:

Vintage| Sepia/pastels | Wood/fabric textures| Floral/polka patterns | Traditional art styles

Option 2

Option 1

Option 3

Which image is more visually appealing? *

Option3

Option 1

Option2

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces **Collaborative Direct Preference Optimization (C-DPO)**, a novel framework designed to address the fundamental limitation of generic text-to-image (T2I) diffusion models by enabling **personalized image editing**. C-DPO uniquely aligns image edits with **individual user preferences** by modeling users in a dynamic preference graph and **leveraging collaborative signals** from like-minded individuals with overlapping tastes. It integrates user-specific embeddings, learned via a lightweight graph neural network, into a **novel DPO objective** that jointly optimizes for both individual user alignment and neighborhood coherence. A key contribution is the **first formulation of personalized T2I editing**, supported by the creation of the **first large-scale synthetic benchmark** comprising 144K richly structured editing preferences.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n*1. Novel Problem Formulation and Originality of Method:* The paper introduces the first framework for personalized image editing in text-to-image (T2I) diffusion models, moving beyond the inherent \"generic\" and \"one-size-fits-all\" nature of existing models. This redefines the problem setting for T2I editing. It proposes Collaborative Direct Preference Optimization (C-DPO), a novel method that uniquely combines individual user preference alignment with collaborative signals from like-minded individuals, a significant innovation in applying DPO to personalized vision tasks.\n\n*2. Creation of a Unique, Large-Scale Benchmark Dataset:* A significant contribution is the curation of the first large-scale synthetic personalized image editing benchmark, comprising 144K richly structured editing preferences from 3,000 synthetic user profiles. This dataset is meticulously designed to mimic real-world diversity, providing a valuable resource for future research in this nascent field.\n\n*3. Robust Collaborative Learning Framework:* C-DPO effectively addresses the challenge of limited individual user data by modeling users within a dynamic preference graph and leveraging a lightweight Graph Neural Network (GNN) to share information among users with overlapping visual tastes.\n\n**Weaknesses**\n\n*1. Computational Overhead for Inference:* While the training pipeline is efficient for research (e.g., 2-3 GPUs for 1-3 hours), the inference time of \"just over one minute\" to generate a personalized image edit for a given user might be a practical limitation for applications requiring real-time interaction or high throughput.\n\n*2. Technique Novelty:* Although the C-DPO looks interesting while solving this novel task, the technique novelty is pretty low since the DPO method has been widely studied in T2I generation and augmentation research direction. I would like to see what's the main technical novelty in this paper except the huge dataset contribution.\n\n*3. Lack of qualitative comparisons:* For qualitative comparisons, I would encourage the authors to include more in the supplementary material. I know there are some limits for the main paper. Putting more qualitative examples in the supplementary helps to know the quality of your method. Furthermore, it's better to compare with current state-of-the-art instruction-based image editing methods or unified models with user preference prompts. That will help to know whether your personalized image editing is really outperforming the exisiting models. For example, Bagel, MmaDA, Blip-3o, Show-o, Janus-series, Nexus-Gen, UniVG, Step1X, GoT, VAR-GPT, SEED-LLAMA, AnyEdit, etc. You can select several of them to compare and make the user preference as text prompt inputs to show that your method is advantageous than them. More importantly, not only include human preferences, please consider common image editing evaluation metrics. The user study is an optional way to support the common evaluations, not the main evaluation metrics to make a fair comparison.\n\nQUESTIONS:\n1. Novelty (same as weakness 2): The technique novelty referring to the C-DPO is low, which I would like the authors to claim more.\n\n2. Qualitative Comparisons (as weakness 3): I don't know whether it's allowed to put any anonymous links to some image collections during rebuttal. However, quantitative comparison with some unified or image-editing models may help to claim your advantages over them on the personalized image editing task.\n\nLIMITATIONS:\nPlease check the weaknesses and questions.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces an interesting framework for \"personalized\" image editing in text-to-image diffusion models. This is particularly interesting because of the current editing methods being very generic. The proposed Collaborative Direct Preference Optimization (C-DPO), aligns image edits with a user's specific tastes while also leveraging collaborative signals from like-minded users. The collaborative framework models users as nodes in a preference graph and uses a Graph Neural Network (GNN) to learn user embeddings. To support this framework, the authors curated a large-scale synthetic dataset containing 144,000 structured editing preferences from 3,000 synthetic users. The main collaborative learning happens in training a LLM to generate instructions.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n- The main strength of the work is introducing the collaborative framework. This introduces the opportunity for users to explore new concepts that \"likeminded\" users also like. Almost like a social network for image editing.\n\n\nWeakness:\n\n- I am not an expert in GNNs, how would a new user that is not present in the training be handled? What does the input to the GNN look like? These details can be added to the manuscript to improve the understanding of the proposed method \n\n- In line 190: \"whereas the second term implements a graph-regularized collaborative filter that shares statistical strength among like-minded users—crucial for data-sparse personalization.\" What do the authors mean by this? Is the collaborative DPO term really essential for the training? I thought the collaboration comes from the GNN pretraining. What happens if you remove this term from your C-DPO (the second term which you term collaborative)? \n\n- The current setup in inference, generally embeds the user into a token and asks a generic question like \"suggest a single cohesive edit\". What happens if a user also asks for a particular edit? Would the LLM respond well? What if a user who is mostly interested in neon art asks for a water painting art? I am curious how the LLM would handle it.\n\n- A naive baseline that comes to my mind is using a LLM with long context/RAG and ask for \"bring instruction that are relevant to this user\". Again, I am not someone who works with RAGs. So I would appreciate the authors thoughts on this \n\n- For the user study, I think there is a gap to make it significantly more interesting and impactful. Is it possible to do user study by actually treating the users as new \"candidates\" in the graph and really collect their preferences and evaluate them directly? Rather than showing them a persona? I think showing the persona is a little unsatisfactory because the persona contains some keywords and users might end up looking for those words. But the work captures more nuances than simple keywords. And I think this is not tested. \n\n\nThis is a very cool initiative! However, I am not satisfied with the evaluation for user study. This isn't essentially a weakness. But I think it more as an oppurtunity for improving the paper and adding the \"wow\" factor.\n\nQUESTIONS:\n- How difficult would it be to crowdsource and collect preference data? I think such a data would be more natural and realistic. Adding this discussion to the paper would be beneficial to the future works\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a novel framework for personalized image editing, called Collaborative Direct Preference Optimization (C-DPO), which leverages user preference graphs to condition text-to-image diffusion models. The core idea is to employ a graph neural network to learn contextualized embeddings for each user, incorperating signals from other users with similar tastes. This approach allows for highly customized image edits that align with individual aesthetic preferences. The method is validated through extensive experiments, including quantatative benchmarks and user studies, demonstrating superior performance against existing baselines in achieving user-specific personalizaion.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**:\n* The proposed C-DPO framework is a novel and well-motivated extension of DPO for the task of personalization. The use of a graph-based representation for user preferences is an elegant way to model collaborative signals and scale personalization.\n* The evaluation is comprehensive, combining automatic metrics like CLIP-text similarity with qualitative examples and, importantly, a user study. This multi-faceted evaluation provides strong evidence for the effectiveness of the proposed method in generating preferred edits.\n* The creation of a large-scale synthetic dataset of user profiles and preferences is a significant contribution. This resource could be valuable for the community and encourage further research in personalized generative modeling.\n\n**Weaknesses**:\n* The paper could benefit from a more thorough justification of its hyperparameters and architectural choices. For instance, the selection of the number of neighbors (K) for the GNN seems arbitrary. An ablation study exploring the impact of K and clarifying why not all nodes are considered (when looking up the nearest neighbors). \n* The experiments are conducted exclusively on synthetic user data. While the dataset is large, its validity as a proxy for real-world preferences is not established. The paper would be significantly strengthened by including experiments on a dataset of real user preferences to show the transferability of the approach. The method used to generate the synthetic data should also be detailed. For e.g. how is preference data curated?\n* A simple (but potentially strong) baseline is missing from the evaluation. It is unclear how C-DPO compares against a simpler approach where user preferences are injected into the model via a more sophisticated prompt engineering (e.g., using the user profile description and/or aggregate neighbour information directly in the prompt). This comparison is helpful to justify the added complexity of the GNN-based framework.\n* It is not fully clear if incorporating new users into the graph would require retraining the GNN.\n* The reference section is very messy and needs to be revised. \n  - Reference [2], [3] and [4] are the same paper\n  - Reference [18], [19] are the same paper\n  - Reference [28], [29] are the same paper\n  - Reference [30], [31] are the same paper\n\nQUESTIONS:\nSome of these points are mentioned in the weakness section too.\n\nq1. I do not expect authors to run any experiment in this phase. But, is there a study on the number of neighbors (K) used in the GNN and justify this design choice over a simpler approach that considers all nodes?\n\nq2. Can you please expand on the GNN's training methodology, specifically detailing the input data format, the network architecture, and the optimization process to improve reproducibility?\n\nq3. Could you elaborate on the generation process for the synthetic dataset? Specifically, what model was used, and what steps were taken to ensure the preference data is of high quality?\n\nq4. Have you considered comparing your method against a strong baseline that uses prompt engineering with the \"user description\" (including the neighbours) to steer the model, in order to justify the added complexity of the GNN framework?\n\nq5. To strengthen the claims of real-world applicability, have you considered evaluating your framework on a dataset of real user preferences, even if smaller in scale?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposed a novel framework for text-to-image (T2I) generation that incorporates user-specific preferences via Direct Preference Optimization (DPO). To utilize preference patterns shared among similar users, the authors introduce Collaborative DPO (C-DPO), which encodes user embeddings using a graph neural network (GNN). They further construct a large-scale synthetic dataset of 144K text-instructed image editing samples, each annotated with like/dislike preferences across 3,000 synthetic user profiles. Each profile is associated with a distinct set of preferred attributes. The proposed method enables the model to learn preference-aligned image generation, even when conflicting attribute preferences exist across users. Furthermore, it allows the model to reflect attributes that are not explicitly specified in a user's profile by leveraging information from neighboring users with similar preferences.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n- S1. A key consideration in preference-based model tuning is that the training data should ideally be collected from users with consistent preferences. Prior work often addressed this by statistically validating that participants shared similar tastes. In contrast, this paper acknowledges the diversity of individual preferences and extends the problem to personalized T2I editing tailored to each user's preference profile.\n- S2. Leveraging GNNs to integrate information from users with similar preferences into the user embedding process is a well-motivated design choice that supports the goal of collaborative preference modeling.\n\n\n**Weaknesses**\n- W1. The architecture and training strategy of the GNN used for encoding user information are not clearly described. If omitted for brevity, providing a reference to relevant prior work would help readers understand the method.\n- W2. Key details are missing regarding the structure and usage of the dataset, especially how preference annotations are formatted and how they are integrated with GNN-based C-DPO training. Providing concrete examples would help readers understand the overall training pipeline.\n- W3. At inference time, the method for obtaining embeddings for users not seen during training is not explained. If the current approach requires re-training the GNN whenever a new user is introduced, it would significantly limit the practicality and generalizability of the method.\n- W4. The evaluation of the collaborative effect is primarily based on user studies. While user feedback is valuable, more in-depth quantitative analysis is needed to verify whether correlated attributes, implicitly preferred by similar users, are actually reflected in generated images. For instance, in the case of a \"rustic-style\" preference mentioned by the authors, it would be helpful to evaluate quantitatively whether attributes strongly associated with rusticity are indeed realized even they are not explicitly specified.\n\nQUESTIONS:\n- Q1. How is the GNN constructed? Are all users in the dataset connected in a fully connected graph? If so, computing C-DPO over all users during each update would be computationally expensive. How is the computational cost?\n  - According to Eq. 7, the C-DPO update appears to be computed only over the k-nearest neighbors. What similarity metric is used to determine these neighbors?\n  - Are the nearest neighbors determined based on Euclidean distance between user embeddings?\n  - How is the embedding computed for the user who are not included in the training data?\n\n- Q2. In Eq. 7, it seems that the positive/negative pairs (y+, y-) are defined based on user *u*'s preference. but the l207-209 suggests they are re-evaluated using the preference of neighboring user *v*. Which interpretation is correct?\n  - If the first interpretation is correct, wouldn't conditioning on a pair defined by user *u*'s preferences while training with user *v* hinder learning due to a mismatch between the preference source and the conditioning user?\n\n- Q3. How is the dataset constructed? l193-196 mention that the LLM used for instruction generation was LoRA-tuned in a ***user-agonostic*** manner. However, in l217-218, $p_u$ is described as an instruction reflecting **user *u*'s preferences**. How is $p_u$ generated in this case?\n  - What is the overall format of the dataset, and what types of attributes are included in $\\mathcal{P}$?\n  - Since the user profiles are synthesized, what criteria were used to generate them? Does the lack of grounding in real user dataset affect the realism of the dataset?\n\n- Q4. In comparison experiments, were the prompts used for baseline T2I editing methods augmented with both positive and negative attributes from the users profile $P(u)$? Wouldn't this be necessary to ensure a fair comparison?\n\n- Q5. In col. 1 (Q1) of Table 1, DPO-User performs worse than DPO-Vanilla, which seems inconsistent with the authors' claims. Is there any evidence that the user study was conducted rigorously? Can the authors confirm that the annotators responded faithfully?\n\n- Q6. In Table 2, is the reported score based on CLIP similarity between the instruction and the generated image?\n\n- Q7. When conditioning on a test-time user *w* who are not seen during training, how is the corresponding embedding $h_w$ obtained? If retraining the GNN is required for each new user, does this not limit the method's generalizability?\n\nLIMITATIONS:\nThe authors briefly address the limitation related to handling new users, which is also raised in the weaknesses part.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}