Personalized Image Editing in Text-to-Image Diffusion Models via Collaborative Direct Preference Optimization
Authors: Connor Dunlop
Matthew Zheng
Kavana Venkatesh
Pinar Yanardag
Venue: NeurIPS 2025 poster ()
Keywords: image editing
t2i
personalization dpo
Primary area: applications
TL;DR: We introduce 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

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-lmage Diffusion Models via Collaborative Direct Preference Optimization
Connor Dunlop
Kavana Venkatesh" Virginia Tech, Blacksburg, VA {cdunlop, pinary)@vt.edu http://persomalzzaddddttiiii github.io
Matthew Zheng*
Pinar Yanardag
Input Image
black, blue, dislikes pink
User parple, User georetrric rainioow,, dialikes
User likes matare, dislkkss dislikes black
blue, nean, futuristic,, dislikes pink
brown, dislikes black
Figure 1: Our framework performs personalized image editing aligned with user's preference via a novel DPO objective that learns user preferences while leveraging collaborative signals from like-minded individuals.
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. 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 node in dynamic preference graph and learns embeddings via lightweight graph neural network, enabling information sharing across users with overlapping visual tastes. We enhance 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.
"Equal contribution.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
Text-to-image (T2I) diffusion models have achieved remarkable success in visual content generation. enabling high-quality image synthesis from textual descriptions [32 3T 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 S Despite progress, achieving a desired edit often demands substantial manual effort. Existing editing approaches do not account for the individual user's 's preferences. They treat image editing as one-size- mne-size-fits-a mite-fits-all problem: the model does not adapt to a particular user's style, and it assumes the same definition of 'good' edit for everyone.
In other domains like natural language processing, well recognized that different users have unique tastes and requirements, and models can be adapted accordingly [38] [0). 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-to- image 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 Al editors in real creative workflows.
In this work, we take the first step towards personalized image editing by proposing 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 users, we can generalize better from limited data. Consider home-decor hobbyist who frequently edits interior photos to give them a rustic living-room feel. They always add a stone fireplace and sitressse--eeatheer 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-son 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 DPO-based learning approach that aligns diffusion model's image edits with individual user preferences while simultaneously leveraging feedhack from a community of users. To enable this capability. we propose novel synthetic dataset of user editing preferences; 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 graph, which captures similarities in their editing behavior. This graph-based collaborative learning, together with the DPO objective, allows our model to learn wide spectrum of personalized editing styles within single unified model. In summary, our contributions are three-fold:
To the best of our knowledge, we introduce the first formulation of personalized text-to- image 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 paradigm in image editing.
We propose a novel training framework called, Collaborative Direct Preference Optimization, introducing a sraph-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 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://perrooalleeeeeeieiie
github.ii io

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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 33] and SDEdit [23] provide more fine-grate line-grinedd 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
to better align with human preferences. involves training reward model based on pairs of preferred and rejected data to guide the fine-tuning of the language model. A simpler alternative to RLHF Direct Preference Optimization (DPO) (30), which directly optimizes the model parameters on preference data rather than the need for separate reward model or reinforcement learning loop. The applications of DPO have extended beyond LLMs to vision domains. Adaptations such as Diffusion- DPO (88) 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 entire population
20
The expressive capabilities of T21 models have spurred research in user personalization. Recent methods have aimed to fit the preferences of individual user. ViPER 351 fine-tunes a VLM on a dataset of Al-generated user comments on images to extract information on user preferences. It then includes these preferences in the prompt and classifier-free" ffre--guddane 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 reinforcement learning agent to iteratively refine text prompt based on sequential user interactions hefore feeding to a T2I model. PPD ( uses a VLM to compare liked and disliked images to train additional -otss-attnnioon layers using Diffusion-DPO reward objective. Pigeon [40] trains a mask generator for user history and feeds to multimodal LLM trained with DPO objective 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 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 aser-personalized image editing. much more complex task that requires maintaining consistency across edits while also capturing the nuanced editing preferences of users.
Personalized Image Generation
3 Background
The alignment of large language and diffusion models is often framed problem of preference learning in which a model is encouraged to rank preferred outputs higher than rejected ones. Tradi- tional pipelines first train a separate reward model from pairwise numan-prefereee 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. (DPO), a lightweight alternative that removes the explicit reward model and RL stage. Let D = {(x,, w.t 3, ) be a dataset of prompts z; paired with a chosen response y and a rejected response y, Denote by TT(- wx the conditional distribution of the trainable policy and by Tee(( xx) 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 simple logistic loss
30 recently introduced Direct Preference Optimization
(0) = - Ea wy YDD log (B A A wt , - Aeela V. }1)
(1)
3

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1. Supervised Fine Tuning
2. Collaborative DPO
Instruct Pix2PIx
"Tmma ayan
LLM
Night Sunset
Mountains
'Change tunset turrese
A GNN
"A deak cheal
Simlar Users
Text instruction
"Supgest single ocheskee eddlingiing Instuction
"Euggest single, coheclie
Inage-edit instrctioon
MLP
LLM
C-OPO
3. Personalized Editing
"Maasa chall necer
User Embedding
Text Embedding
Figure 2: 1) We first fine-tune language model it generate precise editing instructions. 2) We then introduce a graph-aware DPO objective that leverages collaborative user data to learn individual editing preferences. After training, the system takes input image and user profile, produces tailored editing instructions, and outputs the corresponding personalized edit.
where a-) the sigmoid, | > a temperature controlling conservatism, and Ag and A are defined as
Ao(r, xt .V) = log (y x.) - log TO 1)); A y .V = log Teer xx) log Trer x.).
(2)
Text-to-image diffusion models have become the leading framework for high-fidelity image gen- eration 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 B11, Imagen (34), and Stable Diffission (32) have shown remarkable capabil- ities 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 SDEdir (33 introduce mechanisms for editing existing images through text-based instructions. However, these approaches remain general- purppse 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), novel training framework that extends DPO by incorporating user-specific embeddings and graph-based collaboration for personalized image editing. Section I.T 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 overview of our methodology.
4.1 Collaborative Direct Preference Optimization (C-DPO)
Direct Preference Optimization (DPO) has recently emerged 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 collabo- ration: all preference pairs are treated as if originating from single anonymous user, leading the model converge toward an average policy that ignores the diverse and often overlapping aesthetic preferences of real users. Second, shown in Eq. [ 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 collaboration, both of which are essential for effective user-specific image editing.

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4.1.1 Modeling User-Edit Preferences in a Graph
To overcome the limitations of vanilla DPO, extend the editing policy to explicitly condition user identity and incorporate collaborative structure through a graph-based model user preferences. We define the personalized editing policy as:
Te (y) ut Los t),
where u E U denotes the user identifier, Le is the caption describing the base image, and / is the high-level editing instruction. Each user is represented by a learnable embedding go(u) Rd, which encodes the user's stylistic preferences and conditions the model to generate personalized outputs.
To enable collaboration across users with overlapping preferences, construct heterogeneous, bipartite, undirected urer-preee graph:
G G (,,
where U is the set of users initialized with 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 & consists of edges between users and their preferred 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 contextualized embedding hy for each user [12 Our GNN consists of a 2-layer GraphSAGE encoder and a decoder composed of linear layers. We pretrain the GNN auxiliary supervised edge classification task by predicting user-attribute links positive or negative, encouraging the encoder to learn semantically meaningful and stylistically distinct user embeddings.
core benefit of our approach is that generalizes to new users without requiring retraining, enabling both scalability and practicality. GraphSAGE inductively learns a set of aggregation functions that takes as input node's neighborhood. When an unseen user arrives inference, they are first added as node with 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 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 normalized similarity score between users u and based on the number of shared one-hop neighbors (i.e., common preference attributes):
PP()) n P())
Wio
(6)
max
where P(u) C 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 edge weights in our collaborative preference objective, allowing the model to softly attend to information from sty 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 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.
5

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For mini- batch of preference tuples, (u, Lc t, y², vー) we define the Collaborative Direct Prefer- ence Optimization (C-DPO) loss as:
LC.DPO LDPol 1o,1,y 1 yy
Σ
Wan LDPO(U, 1,
E Nv)(() Wuv rEN(u)
individual
collaborative
where (u) denotes the K nearest neighbors of u in G, and À governs the strength of collaboration. Weighted averaging ensures that the collaborative term does not overpower the individual objective. Each LPo is instantiated via [. but with the policy now conditioned on the user information. Because the reference policy Tel 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 generalizzss DPO to a multi-user setting. The first term preserves the per-user alignment benefits of vanilla DPO, whereas the second term implements reaph-regularized collaborative filter that shares statistical strength among like-minded users-crucin 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 pretrained language model to generate high-qualit editing instructions conditioned on the image caption Le and text instruction t. but without any user information. Specifically, train LoRA adapter [15] on this task. enabling efficient adaptation of the base L.M for instruction generation. The resulting adapter is frozen and serves as the reference model Fee 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 user preference dataset. This adapter serves the policy model me.
To personalize the model, we inject user-specific information soft prompt tokens. For each user u, we compute a graph-based embedding hg using our GNN encoder. These embeddings are passed through a two-layer MLP and converted into fixed set of soft tokens, which are prepended 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 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 personalized editing prompt is generated by our C-DPO model, we translate it into a concrete pixel-level transformation using modular diffusion-based editing backend. We utilize FLUX (18), large-scale rectified flow transformer developed for high-fidelity text-to-image generation, augmented with ControlNet 42 ensure faithful, spatially constrained edits.
For a target user u, input image caption Ic, and high-level textual cue t, our tuned C-DPO model outputs personalized prompt:
Ps. = Solu, Ics 1).
where fe denotes the tuned C-DPO policy model. The output P. is a natural-language instruction tailored to user u's preferences, and is directly consumable by any T21-style sampler. For exam- ple: "change the sofa's color to hot pink." " This setup enables faithful and personalized image edits that reflect the user's 's intent with high semantic precision.
5
Experiments
We evaluate our personalized-editin framework with extensive qualitative and quantitative experi- ments. 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.

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Experimental Setup We evaluate our method's ss ability to generate personalized edits for given base image. To model user-specific edit instructions, we fine-tune QWEN2.5 (7 using LoRA [[S]. For image editing, we employ FLUX. 1 enhanced with ControlNer using a conditioning scale of 0.4 and Canny as the condition. We set the collaborative term strength, X, to 0.15. While our full pipeline can be trained on a single NVIDIA L,40 (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 given user in around one minute. Because framework is editing-model agnostic, this process can be accelerated to about one second when paired with faster backend such TurboEdit [10]. Full training details and parameter configurations
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 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 [1] 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 enables a rigorous evaluation of user alignment and preference modeling in diffusion-basell editors. Our dataset is publicly available at http://personall wwwaalliieddddiiiieeeee .eig..gith........ 1o
5.1 Qualitative Experiments
We first showcase how identical objects are edited in distinct ways according individual user preferences. Fig. Fig. 3 (a) and Fig. 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 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 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 L.LaVA 22 another open-source languanla model that simi-
hstpap//hwuggggggaaooc.oo/ssstts httpa//ooooooootooooo.ooooo.ooo. Union
7

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Input
Futuristic Techie
Imaginative Child
a) Personalized edits for individual profiles
+iee
+lava
+monster
+iee
+lava
+monster
b) Personalized edits with user-provided instructions
Figure 3: (a) Qualitative Results for Individual Users on Different Objects. Our framework is able to incorporate personalized elements into the image editing process, such as adding ncon futuristic elements for Futuristic Techie profile. (b) Qualitative Results for User- essr--rrovdee Personalized Edits. Our framework allows users to provide additional guidance while performing personalized edits.
larly lacks any personalization signal and serves as a second reference point for non-user-specific performance. Both models act task-agnostic baselines with preference aligmment.
Next, we consider Qwen (SFT), fine-tuned variant trained 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 330 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 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-DRR allowing user representations to evolve through interactions with stylistically similar neighbors. This model constitutes the full version of our framework.
extends DPO-User by introducing graph-based collaboration,
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 edit for the following image: <image descriptions." We construct three evaluation conditions reflecting different profile completeness: (i) Like+DDsllike, where both liked and disliked examples are available; (ii) Likes-only; and (iii) Dislikes-only. For each condition, we measure CLIPPtext similarity between the generated instruction and the user's ground-truth liked instructions. As shown in Table 2 our C-DPO

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Original
Edit 1
Edit 2
Edit 8
Edit
Figure 4: Qualitative Results for a Single User on Diverse Objects. For a user who loves unicorns, rainbows, and vibrant, playful palettes, our system infuses that whimsical aesthetic into a wide range of objects from cars and guitars to watchtowers.
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 T)ble The results indicate that our method better preserves the original identity compared to other approaches. We also compute HPS and CLIP-T scores between 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 Appendix [Afor additional quantitative experiments and comparisons with state-of-the-arr editing methods.
Ablations Please refer to Appendix C for ablations on collaborative scale parameter X, neighborhood size K, choice of editing method, and prompt-engineering baseline.
Personalized Image Generation Our method can also be repurposed for personalized image genera- tion as opposed to editing. Please visit A.2 for more details.
5.3
User Study
We further validate our results through user study to assess perceptual personalization quality. We ran a crowd-sourced evaluation on Prolific.com with 50 participants. We ran- domly sampled 10 synthetic user profiles from our test split to cover range of stylistic tastes. For each profile we first showed short text description of what aspects this user likes and dislikes based on their previous edit history. Partic- ipants 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.
Table 1: User Study Results
Model
Q1
Q2
OPO-Vanilla
0.210
0.210
DPO-User
0.174
0.325
Ours
0.616
0.465

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Table 2: CLIP scores between model outputs and user preferences under different prompting con- ditions where we provided both Like and Dislike preferences of the user (Like+Dislike), only like preferences (Likes) or only dislike preferences (Dislikes).
Model
Like+Dislike
Likes
Dislikes
Qwen-VL
0.274 + 0.055
0.276 1 0.052
0.229 + 0.051
LLaVA
0.244 = 0.065
0.254 = 0.061
0.227 + 0.051
0.276 + 0.049
0.276 + 0.042
0.185 0.065
SFT-Pix2Pix
0.227 + 0.078
0.238 + 0.065
0.196 + 0.065
DPO-Vanilla
0.272 = 0.059
0.274 0.057
0.172 + 0.057
DPO-User
0.307 = 0.071
0.286 + 0.071
0.269 + 0.076
Ours
0.354 1 0.068 0.306 + 0.069
0.294 1 0.071
3: DINO and CLIP scores computed between the original input image and its personalized edited version (DINO-Ref, CLIP-Ref), HPS and CLIP-T scores are computed between the personalized edited images and the textual description of the original image.
Model SFT
DINO-Ref
CLIP-Ref
HPS
CLIP-T
0.690 = 0.263
0,600 0.222
0.238 + 0.044
0,331 0.075
DPO-Vanilla
0.736 + 0.248
0.615 + 0.202
0.242 + 0.049
0.346 + 0.067
DPO-User
0.762 + 0.240
0.649 + 0.224
0.243 + 0.048
0.354 + 0.066
Ours
0.782 + 0.182
0.652 + 0.182
0.249 + 0.034
0.358 + 0.043
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 I 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 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 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 high-quality visual content creation, reducing repeti- tive pronpt-engineering cycles and enabling non-experts, including artists with motor impairments or limited technical skills 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 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 's preference through learned graph embeddings. (ii) propagates information across a similarity graph of like-minn like-minded users, and (iii) couples the resulting personalized prompt with 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 exciting direction for follow-up work.
10

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Table of Contents
Additional Quantitative Experiments
A.1 Experiments on Personalized Image Editing
21
A.2 Experiments Personalized Image Generation
21
A.3
Comparison with State-of-the-Ar Instruction- Based Editing Models
B
Detailed GNN Information
22
B.
GNN Construction and Training
22
B.2
Generalizing to Unseen Users
22
Additional Ablations
23
Ablation on Collaborative Scale Parameter
23
Ablation on Neighborhood Size
23
Ablation on Editing Method
23
Ablation Prompt-Engineering Baseline
23
Additional Qualitative Experiments
24
D.I
Additional Qualitative Examples
24
D.2 Visuals on Personalized Image Generation
24
D.3 Comparison with Stat-ooftthe-Ar Instruction mnstruction-Based Editing Models
24
Additional User Studies
25
Evaluation Prompt
26
Experimental Details
26
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Table 4: We report pairwise similarity scores between the user's liked images and the generated personalized images amaage-iaaee 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).
Model
DINO-Pref
CLIP-I-Pref
CLIP-T-Pree
CLIP-T-Neighbor
SFT
0.054 + 0.102
0.315
0.098
0.334 + 0.049
0.286 0.025
DPO-Vanilla
0.056 + 0.106
0.335
+ 0.097
0.359 + 0.035
0.304 + 0.026
DPO-User
0.056 + 0.105
0.325
0.096
0.359 + 0.034
0.297 0.026
Ours
0.059 + 0.106
0.338
0.089
0.364 + 0.038
0.312 + 0.020
Table 5: Quantitative comparison for personalized image generation with VIPER.
Model
CLIP-I
DINO
HPS
ViPer
0.286 + 0.015
0.035 = 0.008
0.205 = 0.050
Ours
0.326 + 0.020
0.037 = 0.016
0.253 = 0.046
A
Additional Quantitative Experiments
We conduct additional quantitative experiments to evaluate our personalized image editing framework. Specifically, we compare user sser-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.I].
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
(main paper) presents a quantitative comparison between our method and other state-of-the ohethe- art approaches using editing prompts generated by each method, 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 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-Prefh We also include a ELIP-T-Neighhor 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 with the state-of-the-art personalized image generation method VIPER (35). As shown in Table and Fig. ? our method achieves higher scores when comparing generated images the user's s liked images. Additionally, it delivers superior visual quality and relevance, better aligning with user preferences (see Fig. 9).
9
A.3 Comparison with State-of-the-AN Instruction-Based Editing Models
To better contextualize our approach within the broader landscape of instruction- -based and unified image-editing frameworks, conducted additional quantitative comparisons against recent state-of-
21

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Table 6: Quantitative comparison with recent state-of-the-art instruction- based editing frameworks.
Our C-DPO method achieves superior personalization and overall alignment
Method
CLIP-I-Pref
DINO-Pref
HPS
BAGEL
0.3397 0.0144
0.0363 + 0.0066
0.2475 + 0.0388
SEED-Llama
0.3109 + 0.0159
0.0349 + 0.0121
0.2500 + 0.0287
Nexus-Gen
0.3327 0.0138
0.0373 = 0.0113
0.2598 = 0.0307
Ours
0.3515 + 0.0190
0.0365 = 0.0164
0.2607 = 0.0328
For fairness, we provided each model with the textual user-preference prompts used in setup, thereby allowing them to leverage equivalent conditioning information without requiring architectural modification.
the-art baselines: BAGEL [9. SEED-LLAMA 11 and Nexus-Gen 41
As summarized in the Table our approach clearly outperformed these methods across standard im- age 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 lightweight Graph Neural Network (GNN) to compute contextualized user embeddings by aggregating neighborhood information a heterogencous bipartite graph of users and attributes. Each attribute node represents an editing instruction topic (e.g., rustic themes, neon colors, mini- malistic textures) and is initialized with a dense text embedding derived from a pretrained language model. Each user node is initialized with a learnalee embedding vector. The resulting graph encodes user serratttribute relationships via edges that indicate whether a user likes or dislikes a given attribute.
Our GNN architecture consists of 2-layer GraphSAGE convolutional encoder using mean aggre- gation 12), followed by an edge decoder comprising two linear layers. Details on specific layer dimensions can be found in Table 12 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% the edges, reserving 20% each for validation and testing, following standard protocols outlined in foundational works 11 I7].
During pretraining, both liked and disliked attribtee pairs are leveraged in a supervised edge classification task (c.g.. edges of the form (userl, likes, "neon green") "ren""))e or (user2, dislikes, "rainbows")) )). Once pretraining complete, the classification head 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
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, GraphS AGE learns set of ag- gregation 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 new users arrives, (1) we create 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
22

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Table 7: Ablation on C-DPO Neighborhood Size (K)
K
CLIP Liked Similarity
2
0.353 + 0.065
3
0.354 + 0.068
5
0.358 + 0.065
12
0.344 = 0.069
functions over this 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 the collaborative scaling factor A, evaluating values of 0.01, 0.15, and 0.50 (see Fig. 7 A very low value (A = 0.01) limits the influence of neighbor preferences, effectively reducing the method user-based DPO, neighbors contribute insufficient information to guide personalization. In contrast, a high value (A = 0.50) overly emphasizes neighbor preferences, which can override the user's own preferences and degrade performance. Our results indicate that 0.15 provides the best balance, allowing meaningful influence from similar users without overwhelming the target user's preferences.
C.2 Ablation Neighborhood Size
We further investigate the effect 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 smaller to moderate values yield stronger alignment between generated edits and user preferences, as measured by CLIP Like Similarity. Increasing K beyond certain threshold leads 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 exclusive synthetic data and generally occurs in collaborative and graph-based methods, even with real-works datasets [13 43 19] When too many neighbors are aggregated, weaker less relevant signals tend to dilute the informative collaborative signals from closely related neighbors-4 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 applicable Stable Diffusion-based editing methods well. To highlight the impact of the underlying editing method, we compared personalized editing results across several Stable Diffusion- based approaches: SDEdit (23, Ledits++ 44. InstructPix2Pix (5). TurboEdit [10] well Flux-
based methods including RF Inversion 33 and Flux ControlNe(" As shown in Fig. g Flux
ControlNet consistently delivers superior fidelity and visual quality compared 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 strong prompt-engineering baseline that directly injects textual user descriptions, including aggregated neighbor information, into the model prompt, without using
23

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Table 8: Comparison between our C-DPO framework and prompt-engineering baseline that encodes user and neighbor information textually. Our method achieves markedly higher user-alignment scores, validating the effectiveness of GNN-based personalization.
Method
Like+Dislike
Likes
Dislikes
Prompt Engineering
0.270 + 0.051 0.354 + 0.068
0.270 + 0.045
0.200 = 0.057
Ours
0.306 + 0.069
0.294 = 0.071
Table Quantitative comparison with recent state-of-the-art instruction- based editing frameworks. Our C-DPO method achieves superior personalization and overall alignment
Method
CLIP-I-Pref
DINO-Pref
HPS
BAGEL
0.3397 + 0.0144
0.0363 + 0.0066
0.2475 + 0.0388
SEED-Llama
0.3109 + 0.0159
0.0349 = 0.0121
0.2500 + 0.0287
Nexus-Gen
0.3327 = 0.0138
0.0373 = 0.0113
0.2598 = 0.0307
Ours
0.3515 + 0.0190
0.0365 = 0.0164
0.2607 + 0.0328
GNN-derived embeddings. This baseline represents simplified but potentially competitive approach where user and neighborhood preferences are expressed through structured textual conditioning rather than learned representations.
As shown in Table S our proposed method substantially outperforms this baseline across all metrics. These results indicate that our proposed framework provides meaningful benefit over simpler, baset
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 depicts user who prefers playful elements, vivid color palettes and childish elements such as unicorns and rainbows. When this user is presented with a bike ceramic bowl, our model automatically adds rainbow-hued paint, pastel gradients, and subtle unicorn-themes playful touches that align with a child-friendly aesthetic, while preserving the underlying object structure in disentangled manner. On the other hand, when our system is presented with user who prefers earthy tones and nature, our framework adds natural elements such 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 compar- ing with the state-of-the -ettthe-rtt approach, VIPER [35]. Fig. 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 broader range of user- preferences while maintaining focus coherent objects. In contrast, VIPER often emphasizes textures or patterns without anchoring them to meaningful objects.
D.3 Comparison with Statc-of-the-Art Instruction-Batt 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 g SEED-LLAME [1I, and Nexus-Gen [1]. For fairness, we provided each model with the same textual prefeeereeeeece prompts used in our setup, thereby allowing them to leverage equivalent conditioning information without requiring architectural modification.
24

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User Profile
Original
Edit
Edir
Edic
Edit
The suer down to trigh, uitmuaal tamanica ti sparlins calsbonn usioon mottl they foor sot ploiigg viunt and inggratta tones mitimallat styles seyzhing pearly realatic or oull.
earthy
and cains organic fee. They lasf and evertoys matarialy, while wolding calors synthetc nuture, and digial
Thai inaptred westadic.
the prlfes the conse laaur and chopres,
ther acstlttc loos hih- contust detal, sekeh, and
pungr, cartees and fhod owelas bear deek
iapo cee wnst stered odeer sihemen they fher drcenthe corpostions cove comption, whie rejeting bast fitess daas commenta
Figure 5: Qualitative Results for Different Users on the Same Objects. Our framework tailors image edits to each user's preferences. In the first row, for example, user who loves unicorns, rainbows, and playful color schemes sees their inputs transformed to match that personalized aesthetic preferences.
As summarized in the Table our approach clearly outperformed these methods across standard im- age editing evaluation metrics, highlighting the distinct advantage of our collaborative personalization framework.
E
Additional User Studies
We conducted a two-stage user survey with participants using real-human data. Specifically, we first asked participants to indicate their likes and dislikes attributes from 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. Partici- pants were then sent a follow-up survey featuring personalized edits of five objects: an apple, mug, a bag, 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 scale of to (1=Does not matching my personal taste at all, 5=This is matching my personal taste lot) you rate the following image?
Q2: To what extent does the edit preserve the original image while reflecting personalized preferences?
No= at all, 5=Very Much)
25

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Table 10: Additional User Study results.
Object Name
QI: Personalization
Q2: Disentanglement
Apple
4.375 + 0.74
4.75 + 0.46
Mug
4.625 1.06
4.50 = 0.75
Teddy bear
4.75 + 0.46
4.625 + 0.51
Bag
4.50 1.06
3.85 = 0.83
Bowl
4.75 = 0.46
5.00 = 0.00
Total
4.60 = 0.15
4.55 = 0.38
Before Personalization
After Porsonalization
Figure 6: Example edit for member of our real user study. This user preferred pink, purple, and neon colors, while disliking white, gray, and beige. Their favored patterns included rainbow, fluffy, and glossy styles, while they disliked floral and earthy designs. This image rated Personalization 5/5, Disentanglement = 4/5, showing that the user found the edit matching their taste.
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 compare baselines. Specifically, users presented with an object alongside three personalized edits (from Vanilla DPO, User-DPO, and our C-DPO method) where images are shown an anonymous fashion. Users are asked pick the edit they most prefer of options.
Table 11: Additional User Study comparison of preference ratios across models.
Object
C-DPO (Ours)
User-DPP -PO
User- Vanilla
A Bag
0.45
0.22
0.33
Bowl
1.00
0.00
0.00
Mug
0.45
0.33
0.22
Teddy Bear
0.78
0.22
0.00
An Apple
1.00
0.00
0.00
F
Evaluation Prompt
To ensure a fair comparison across baselines, we adopt unified structured prompt for all evaluations (see Fig. 10
G Experimental Details
Various details of our experimental setup are given in Table I2
26

---

Ours
0.350
Like Similarity
0.340
0.3.0
0.01
0.15
0.50
Collaborative Scale (1)
Figure 7: Ablation on Collaborative Scale (A)
Input Image
Instruct Pix2Pix
SD Edit
RF Inversion
Ledits++
ControlNt
Turbe Edit
Futuristic Techie
P
Profile
Glamour Profile
Figure 8: Ablation Stable Diffusion and Flux based editing methods.
"A cup"
"An umbrella"
"A feddy bear"
"A chair
OURS
VIPER
Imaginative Child Profile
Glamour Stylist Profile
Figure 9: Personalized image generation for two profiles: Imaginative Child Profile who likes rainbow colors and glitter, and Glamour Stylist Profile who likes shiny and metallic elements.
27

---

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 single, cohesive image-editing instruction.
Figure 10: The input prompt template used during our evaluations.
Which edited image would user prefer, given their preferences?
User Preferences:
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 styles
Option
Option
Option
Figure 11: User Study Q1
28

---

Which Image Is more visually appealing?
Option
Option
Option
Figure 12: User Study Q2
29

---

LoRA Details
Base LLM
Qween/Qwenn.5-7BBIIninnu 2
LoRA Rank
16
LoRA a
32
Dropout Optimizer LR Scheduler
0,05
Adam W
Cosine
Data Type
b
Target Modules
All Linear
User Based GNN
Embedding Size
4096
Keyword Encoder
Linq-AI- Ling-Al-Research/Lin BittssaarchLiin--mbbeeeee [6
Pretrain Epochs
150
Pretrain Learning Rate
3e-5
Pretrain Optimizer
Adam
GraphSAGE Hidden Channels
1024
Linear Hidden Channels
1024
Initial Supervised Finetuning
Data
Instruct Pix2Pix 5
GPUs
2 NVIDIA L40 (48GB) GPUs ~3 Hours
Train Time
Epochs
1
Learning Rate
2c-6
Batch Size
16
Collaborative DPO
Data GPUs
Our Synthetic Data
3 NVIDIA L40 (48GB) GPUs
Start Checkpoint
SET Tuned LoRA on Instruct Pix2Pix
Train Time
~ Hour
Number of Similar Users
3
Collaborative Term Scale (A)
0.15
Number of Soft Prompt Tokens
8
Train Steps
3000
Learning Rate
2e-7
GNN Parameters Learning Rate Batch Size
2e-8
12
Flux+ControlNet
Flux Model
black-forest-lab//LLUXX. I-dev [18]
ControlNet
Control Mode
Canny
Number of Inference Steps
60
Guidance Scale
35
ControlNet Conditioning Scale
0.3-0.4
Table 12: Full Experimental Details for Replicating Our Method
30


Reviews:
[{"review_id": "bv34LkD6cp", "reviewer": "Reviewer_bCfR", "summary": "This 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.", "questions": "- 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?", "limitations": "The authors briefly address the limitation related to handling new users, which is also raised in the weaknesses part.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "The authors have addressed the reviewers' concerns regarding the specifics of the GNN structure and the handling of unseen users during inference. They further strengthened their argument by adding new experiments to verify whether the proposed C-DPO effectively captures collaboration effects, as well as conducting a user study with real participants. Although the proposed method is based on DPO and employs a two-phase training process, I find its originality and novelty in proposing a preference-learning approach that accounts for diverse, non-uniform preferences across users, and in effectively designing a method to leverage collaboration among users with similar preferences.", "rebuttal": "**W1: The architecture and training strategy of the GNN.**\n\nWe thank the reviewer for highlighting this important point, and we agree that some details were briefly discussed in Section 4.1.1. due to space limitations. We would like to clarify our architecture and training strategy. Specifically, we utilize a lightweight GNN to compute contextualized user embeddings by aggregating neighbor information in our heterogeneous graph of users and attributes. Attribute node features are dense text embeddings of editing instruction topics (such as watercolor, cats, etc). The model consists of a GNN encoder of 2 GraphSAGE convolutions using mean aggregation and an edge decoder of 2 linear layers. The GNN is pretrained on an auxiliary task of predicting user–attribute links (labeled positive or negative) to ensure semantically meaningful user representations. We train on 60% of edges in our graph, withholding 20% for validation and testing each. We will elaborate this further in our final manuscript.\n\n**W2: How preference annotations are formatted and how they are integrated with GNN-based C-DPO training.**\n\nDuring GNN pretraining, we leverage both liked and disliked user-attribute pairs in an auxiliary edge prediction task. For instance, edges may take the form of (“user1”, “likes”, “neon green”) or (“user2”, “dislikes”, “rainbows”). Once pretraining is complete, we discard the classification head and retain the resulting user embeddings. In the full C-DPO training stage, we use these user embeddings in combination with the caption of the target image (e.g., “White ceramic bowl”). Positive and negative pairs are constructed using editing instructions that the user liked or disliked (e.g., “Add a holographic pattern to the bowl” as a positive, and “Overlay a floral pattern on the bowl” as a negative). \n\n**W3 & Q7: When conditioning on a test-time user w who is not seen during training, how is the corresponding embedding h_w obtained?**\n\nIndeed, our approach leverages an inductive embedding framework (GraphSAGE) rather than a transductive one, explicitly designed to generalize efficiently to new users without retraining. Traditional transductive methods are inherently limited to fixed graphs, unable to generalize embeddings to unseen nodes or evolving structures.  Rather than generating isolated embeddings for each user, GraphSAGE learns a set of aggregation functions which intake feature information from a node’s neighborhood and aggregates. Applying these aggregation functions efficiently generates embeddings for previously unseen users based solely on their attribute connections, provided the attribute schema matches the training data.\n\nAt inference time, embedding a new user does not require retraining. Instead, a new user provides a small set of liked/disliked attributes; we then add a corresponding user node with a fixed-width, zero-initialized feature vector, link it to relevant attribute nodes, and apply our learned aggregation functions in the pretrained GNN encoder. The resulting embedding is computed inductively from the attributes and neighborhood structure alone. While periodic fine-tuning on the expanded graph may further enhance embedding quality, it is optional, not essential for inference, thus preserving the practicality and scalability of our method.  \n\n**W4: (..) more in-depth quantitative analysis is needed to verify whether correlated attributes, implicitly preferred by similar users, are actually reflected in generated images.**\n\nIn response to the reviewer’s suggestion, we conducted additional quantitative experiments to assess whether correlated attributes implicitly preferred by similar users are reflected in the generated outputs. For each user, we identified their top 10 most similar users based on the user-preference graph and collected the attributes those users liked. We then measured the CLIP between these collected preferences and the images generated by our method (CLIP-Neighbor), using the same evaluation set as in Tables 1 and 2 of the Appendix:\n\n| Method      | Clip-Neighbor ↑   |\n| ----------- | ----------------- |\n| SFT         | 0.286 ± 0.025     |\n| DPO-Vanilla | 0.304 ± 0.026     |\n| DPO-User    | 0.297 ± 0.026     |\n| **Ours**    | **0.312 ± 0.020** |\n\nWe will include this experiment to camera ready. \n\n**Q1: How is the GNN constructed? (..) How is the computational cost?**\n\nThe users and their respective liked/disliked edit instructions are represented in a heterogeneous bipartite graph, with users as one node type and attributes as another. We condense edit instructions to keyword attributes to capture broader topics. The graph is not fully connected as users are only connected to attributes they have history with. Users are only indirectly connected via shared keywords; there are no direct user–user edges. The GNN architecture (see W1 above) is pretrained on this graph and only takes ~2 minutes and is frozen for C-DPO training, which remains efficient, completing in about one hour (see Appendix Table 8 for details).\n\nFor similarity metric: we rank the neighbors by the # of shared preference attributes i.e. one-hot neighbors in the graph. Thus, the nearest neighbors are the ones who like the most attributes in common with a user. \n\n**Q2: In Eq. 7, it seems that the positive/negative pairs (y+, y-) are defined based on user u's preference. (...)**\n\nThe positive/negative pair (y+,y−) is defined by the anchor user u. The collaborative term does not relabel pairs using a neighbor’s preferences; instead, it re‑evaluates the same pair under the policy conditioned on a neighbor v. This encourages the policy to prefer u’s chosen edits in contexts where like‑minded neighbors would also agree, providing a graph‑weighted regularizer on top of the primary per‑user DPO term.\n\nPotential mismatch is controlled in two ways. (i) We choose only the K nearest neighbors, so the auxiliary signal comes from users with aligned tastes. (ii) The collaborative contribution is weighted by w_uv and scaled by λ ensuring it cannot overpower the individual term. This also ensures that each neighboring user gets a signal that is proportional to how similar those users are to the anchor user. Practically, this design augments u’s sparse supervision with corroborating signals from nearby users.\n\n**Q3-1: How is the dataset constructed?**\n\nPlease see our answer at reviewer F3Yh-Q3.\n\n**Q3-2:  Instruction generation in a user-agnostic manner vs user u's preferences. How is p_u generated?**\n\nInstruction Generation (user-agnostic): Initially, we fine-tune an LLM using LoRA adapters in a user-agnostic manner. This model generates general-purpose editing instructions conditioned solely on image captions and high-level text prompts, without user-specific embeddings.\n\nPersonalization via Preferences (user-specific): Personalized instructions p_u are generated by injecting user-specific embeddings derived from our trained GNN. Specifically, each user's embedding (obtained via GraphSAGE from their profile p_u) is converted into soft tokens, prepended to the textual input prompt, and passed through the fine-tuned LoRA-adapted LLM. This strategy yields personalized instructions that reflect the user's editing preferences.\n\nThe overall format of the dataset includes pairs of base-image captions and edit instructions, annotated with explicit user identifiers and associated binary preference labels (liked or disliked). Each user profile p_u contains structured sets of preferred attributes (positive) and explicitly avoided attributes (negative), spanning several dimensions such as stylistic themes (e.g., rustic, luxury), color palettes, lighting preferences, visual textures (e.g., distressed, polished), object-level attributes (e.g., wooden beams, marble finishes), and decorative overlays (e.g., floral, cosmic effects).\n\n\n**Q4: In comparison experiments, were the prompts used for baseline methods augmented with both positive and negative attributes from P(u)?**\n\nIn our comparisons, we indeed augmented the input prompts for all baseline T2I editing methods with both positive and negative attributes explicitly derived from the user's profile P(u). Specifically, we standardized prompt inputs across all tested methods by explicitly providing both liked (positive) and disliked (negative) preferences from each user’s profile. This ensured that all methods received identical, detailed conditioning information, enabling a fair and direct comparison of their personalization capabilities.  \n\n**Q5: In col. 1 (Q1) of Table 1, DPO-User performs worse than DPO-Vanilla, which seems inconsistent with the authors' claims. (...)**\n\nWhile it is surprising that DPO-User performed slightly worse than DPO-Vanilla on Q1 in our user study, we confirm that our user study was conducted rigorously and carefully controlled. Annotators were recruited via a reputable platform (Prolific) with screening to ensure participants responded reliably. When considering quantitative metrics (CLIP similarity scores reported in Table 2), the DPO-User model outperformed DPO-Vanilla, confirming that user-specific embeddings do, in fact, better capture personalized intent.  \n\n**Q6: In Table 2, is the reported score based on CLIP similarity between the instruction and the generated image?**\n\nIn Table 2, the reported scores represent CLIP similarity between the generated textual instructions and the ground-truth textual user preferences. We agree that image-based metrics are also critical for evaluating our method's effectiveness. Therefore, in Tables 1, 2, and 3 of the supplementary material, we provide extensive image-based metrics. Specifically, in Table 1 (supplementary) \"CLIP-T-Pref\" explicitly reports CLIP similarity computed between the edited images and the user's textual preferences, directly assessing how faithfully the images align with user intent."}, {"review_id": "cUtmAirzsx", "reviewer": "Reviewer_F3Yh", "summary": "This 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.", "questions": "Some 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?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**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", "was_revised": true, "final_justification": "I increase the score to a borderline accept since authors sufficiently explain the questions and are willing to incorporate improvements.", "rebuttal": "**Q1: 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). 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\nWe appreciate the reviewer's request for a clearer justification of K. In our framework, K controls only the size of the collaborative neighborhood used in the C-DPO loss, specifically, how many of the most similar users contribute weighted preference signals. It does not limit the message-passing radius inside the GNN itself. The GNN still aggregates over all incident edges when computing the user embeddings. \n\nWe conducted preliminary experiments during development to empirically select an appropriate K. We tested multiple values observing that smaller to moderate values strike a good balance between computational efficiency, embedding quality, and effective collaborative personalization. To help the reader further understand the selection of K, we conduct an additional ablation which we will include in our revised manuscript. Increasing K beyond a certain threshold results in diminishing returns or even degraded performance, likely due to noise from less relevant neighbors.\n\n| K   | CLIP Like Similarity ↑ |\n| --- | ---------------------- |\n| 2   | 0.353 ± 0.065          |\n| 3   | 0.354 ± 0.068          |\n| 5   | 0.358 ± 0.065          |\n| 12  | 0.344 ± 0.069          |\n\n\n**Q2: 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\nWe thank the reviewer for highlighting this important point, and we agree that some details were briefly discussed in Section 4.1.1. due to space limitations. We would like to clarify our architecture and training strategy. Specifically, we utilize a lightweight GNN to compute contextualized user embeddings by aggregating neighbor information in our heterogeneous graph of users and attributes. Attribute node features are dense text embeddings of editing instruction topics (such as watercolor, cats, etc). The model consists of a GNN encoder of 2 GraphSAGE convolutions using mean aggregation and an edge decoder of 2 linear layers. The GNN is pretrained on an auxiliary task of predicting user–attribute links (labeled positive or negative) to ensure semantically meaningful user representations. We train on 60% of edges in our graph, withholding 20% for validation and testing each. We will elaborate this further in our final manuscript.\n\n**Q3: 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?/The method used to generate the synthetic data should also be detailed. For e.g. how is preference data curated?**\n\nWe thank the reviewer for raising this important point. Our synthetic dataset comprises 144K editing preferences across 3,000 user profiles, each designed to simulate diverse and realistic aesthetic tastes. To curate this data, we first define user profiles with distinct demographic attributes (e.g., age, region, taste clusters) which guide their style preferences. Each user is assigned four base image captions (sampled from 80 COCO categories), and for each, we generate two pairs of preferred and rejected editing instructions across six edit types (e.g., color, material, style, overlay), totaling 48 edits per user. These instructions are produced using GPT-4o, explicitly conditioned on the user’s profile. Preference labels are assigned via templated prompts ensuring stylistic alignment with the user's traits. To promote collaborative learning, we construct a user graph based on shared preference attributes and pretrain a GNN to embed these relationships. This process ensures that our preference data is semantically rich, structurally diverse, and aligned with realistic editing behavior, enabling effective training and evaluation of our collaborative personalization framework.\n\n\n**Q4: 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?/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\nWe thank the reviewer for highlighting this important comparison. Indeed, we conducted experiments comparing our GNN-based collaborative method (C-DPO) with a strong baseline employing sophisticated prompt engineering. Specifically, this baseline directly injected detailed textual descriptions of user preferences—including aggregated neighbor information—into the prompt itself, without using the GNN-based embedding.\n\nThe results clearly demonstrated that our method significantly outperformed the sophisticated prompt-engineering baseline as shown below:\n\n| Method               | Likes+Dislikes ↑     | Likes ↑              | Dislikes ↑           |\n| -------------------- | ------------------- | ------------------- | ------------------- |\n| Prompt Engineering   | 0.270 ± 0.051        | 0.270 ± 0.045        | 0.200 ± 0.057        |\n| **Ours**             | **0.354 ± 0.068**   | **0.306 ± 0.069**   | **0.294 ± 0.071**   |\n\nThis indicates that our proposed framework provides a meaningful benefit over simpler, prompt-based personalization methods, effectively justifying the additional complexity introduced by the GNN. We will explicitly include these results and discussions in the revised manuscript to clarify this important point.\n\n**Q5: 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?/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.**\n\nWe thank the reviewer for highlighting the importance of evaluating our framework on real-world data. Collecting real-world data at sufficient scale for training is challenging due to typical academic budget constraints. Indeed, validating our framework on a dataset of real user preferences (albeit smaller in scale) would significantly enhance the strength of our claims about real-world applicability. While the current experiments utilize a synthetic dataset primarily due to budget and scalability constraints, we acknowledge this limitation and we will strongly consider collecting a real-world user-preference dataset and performing additional validation experiments to include in the camera-ready version, further demonstrating our method's practical utility and generalization capabilities. \n \n**Q6: It is not fully clear if incorporating new users into the graph would require retraining the GNN.**\n\nOur approach leverages an inductive embedding framework (GraphSAGE) rather than a transductive one, explicitly designed to generalize efficiently to new users without retraining. Traditional transductive methods are inherently limited to fixed graphs, unable to generalize embeddings to unseen nodes or evolving structures. Rather than generating isolated embeddings for each user, GraphSAGE learns a set of aggregation functions which intake feature information from a node’s neighborhood and aggregates. Applying these aggregation functions efficiently generates embeddings for previously unseen users based solely on their attribute connections, provided the attribute schema matches the training data.\n\nAt inference time, embedding a new user does not require retraining. Instead, a new user provides a small set of liked/disliked attributes; we then add a corresponding user node with a fixed-width, zero-initialized feature vector, link it to relevant attribute nodes, and apply our learned aggregation functions in the pretrained GNN encoder. The resulting embedding is computed inductively from the attributes and neighborhood structure alone. While periodic fine-tuning on the expanded graph may further enhance embedding quality, it is optional, not essential for inference, thus preserving the practicality and scalability of our method.\n\n**Q7: The reference section is very messy and needs to be revised.**\n\nWe thank the reviewer for pointing this out. We will thoroughly revise and reorganize the reference section in the final manuscript."}, {"review_id": "iNtVW7QP0b", "reviewer": "Reviewer_bMep", "summary": "The 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.", "questions": "- 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", "limitations": "Yes", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "I believe C-DPO brings in a new perspective when it comes to collaborative image generation. While the method is not completely matured and flawless, this definitely starts a theme in the field and deserves a place in neurips presentation. The newly designed user study conducted by the authors shows promise in their work.", "rebuttal": "**Q1: 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\nWe thank the reviewer for highlighting this important point, and we agree that some details were briefly discussed in Section 4.1.1. due to space limitations. Specifically, we utilize a lightweight GNN to compute contextualized user embeddings by aggregating neighbor information in our heterogeneous graph of users and attributes. Attribute node features are dense text embeddings of editing instruction topics (such as watercolor, cats, etc). The model consists of a GNN encoder of 2 GraphSAGE convolutions using mean aggregation and an edge decoder of 2 linear layers. The GNN is pretrained on an auxiliary task of predicting user–attribute links (labeled positive or negative) to ensure semantically meaningful user representations. We train on 60% of edges in our graph, withholding 20% for validation and testing each. Our implementation closely follows standard approaches described in foundational works such as Hamilton et al. (NeurIPS 2017) and Kipf & Welling (ICLR 2016) as referenced in our paper. We will enhance clarity by adding more details as space permits, and explicitly directing readers to these key references in the manuscript.\n\nAt inference time, embedding a new user does not require retraining. Instead, a new user provides a small set of liked/disliked attributes; we then add a corresponding user node with a fixed-width, zero-initialized feature vector, link it to relevant attribute nodes, and apply our learned aggregation functions in the pretrained GNN encoder. The resulting embedding is computed inductively from the attributes and neighborhood structure alone. While periodic fine-tuning on the expanded graph may further enhance embedding quality, it is optional, not essential for inference, thus preserving the practicality and scalability of our method.\n\n**Q2: 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\nWe thank the reviewer for requesting clarification on the importance of the collaborative DPO term. Indeed, while the GNN pretraining provides an initial embedding structure that encodes user similarities, the collaborative DPO term (the second term in Eq. 7) is critical for effectively leveraging these similarities during the training process. Specifically, this term acts as a regularization mechanism during training, encouraging the model to leverage shared patterns among like-minded users.\n\nWe performed an ablation study confirming the importance of the collaborative term as shown in Fig. 2 in the Appendix. Setting the collaborative scale very low (e.g. λ=0.01) reduced our method's performance close to the user-only DPO baseline. This indicates that minimal collaborative signals lead to suboptimal personalization, highlighting the necessity of a balanced collaborative term.\n\n**Q3: 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\nWe thank the reviewer for this insightful question. Indeed, our framework robustly handles scenarios in which users explicitly request edits outside their typical style preferences. Due to the flexible design of our personalized embeddings and the underlying LLM-based approach, the model naturally integrates explicit instructions provided at inference, even if they diverge from previously learned preferences.\n\nWe explicitly demonstrate this adaptability in the supplementary material (Fig. 4 and Table 3), where we repurposed our personalization framework—originally trained solely for image editing—to perform personalized image generation tasks without additional training. Our method successfully generalized to this new task and significantly outperformed state-of-the-art personalized generation approaches, such as VIPER. These results underscore the versatility of our framework and its effectiveness at integrating personalized embeddings with explicit user-provided instructions during inference.\n\nDue to NeurIPS rebuttal guidelines, we are currently unable to share visuals in our rebuttal, but we will include additional examples showcasing our method's ability to adapt to such cases with the camera-ready version of the paper.\n\n**Q4: 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\nWe thank the reviewer for this interesting and relevant suggestion. Indeed, a RAG or long-context LLM baseline, where past instructions or user preferences are directly retrieved based on textual similarity and used as prompts, represents a plausible alternative approach.\n\nHowever, such a method primarily relies on explicit textual similarity or keyword matching, potentially overlooking nuanced, implicit preferences that emerge from complex collaborative signals across users. In contrast, our GNN-based embedding explicitly models subtle user similarities via graph-structured relationships, capturing higher-order, implicit user preferences that may not be directly retrievable via textual matching alone.\n\nWe also consider GraphRAG where we could potentially combine the advantages of both explicit graph-based collaboration and retrieval-based personalization. Exploring this promising hybrid approach remains an exciting direction that we plan to pursue in future work.\n\n**Q5: 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\nWe thank the reviewer for this insightful suggestion. We agree that directly collecting user preferences from participants could indeed provide a more realistic and nuanced assessment of our framework's effectiveness.\nWe initially employed synthetic personas primarily due to constraints related to scalability and budget constraints. However, as suggested, performing a real-user evaluation—where each participant explicitly provides their own detailed preferences and is subsequently embedded into the graph—would significantly enhance our understanding of the framework's practical effectiveness and nuance-capturing capabilities. We recognize this as an important opportunity to further strengthen our evaluation.  \n\n**Q6: 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\nWe thank the reviewer for this valuable suggestion. Crowdsourcing real-world user preferences indeed would yield more authentic and nuanced preference data, but it involves practical considerations such as scalability and budget constraints. Despite these challenges, collecting such data represents an exciting and beneficial direction for future research. We will briefly discuss this opportunity in the revised manuscript."}, {"review_id": "amW9kDVjJv", "reviewer": "Reviewer_HyiN", "summary": "The 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.", "questions": "1. 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.", "limitations": "Please check the weaknesses and questions.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "The core equation is Eq.7 in the paper, which is too simple as also widely used in federated learning several years ago. This paper may get better rating if the main context is enriched with more qualitative comparisons with image editing methods. Given its current shape, I cannot convince myself to raise the score. It's not very matching the NeurIPS bar.", "rebuttal": "**Q1: 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\nWe thank the reviewer for highlighting this practical consideration. Indeed, the reported inference time (\"just over one minute\") primarily reflects our choice of editing backend (Flux+ControlNet). Our personalization framework itself is editing-model agnostic, allowing easy integration with faster editing backends. For instance, we tested our pipeline with TurboEdit, which significantly reduced inference time to under 2 seconds per edit. We will include detailed results with TurboEdit in the final manuscript to further demonstrate our framework's flexibility and applicability in real-time scenarios.\n\n**Q2: 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. /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\nWe thank the reviewer for highlighting this point regarding technical novelty.  Indeed, while DPO has been previously studied in the context of T2I generation, our Collaborative Direct Preference Optimization (C-DPO) framework represents a novel and significant extension of the original DPO formulation. Our C-DPO introduces a graph-structured regularization term into the original 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.  Given its flexibility and general applicability, we expect that our novel collaborative regularization formulation can also be adapted beyond T2I generation scenarios. For instance, it has potential utility in broader personalization problems involving LLMs, such as personalized text generation, dialogue systems, or recommendation tasks, opening promising avenues for future research. \n\nMoreover, to the best of our knowledge, while previous research has explored personalized image “generation”, ours is the first work to define and tackle the task of personalized `image editing`. \n\nWe believe these contributions represent substantial and novel technical advances beyond the original DPO formulation and existing personalized T2I generation methods. We will explicitly highlight and clarify these points in the revised manuscript.\n\n**Q3. 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.**\n\nWe thank the reviewer for this valuable suggestion. We fully agree that including more qualitative examples can significantly enhance readers' understanding of our method's capabilities and strengths. While the main manuscript was limited due to space constraints, we have provided additional qualitative comparisons extensively in the supplementary material (see Figures 1, 3, and 4). In our revised version, we will further expand these qualitative results in the supplementary to comprehensively illustrate our approach's performance and diversity across various user preferences and editing tasks.\n\n\n**Q4: 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\nWe thank the reviewer for this important point. In addition to the user studies, we have indeed evaluated our method using several standard quantitative image-editing metrics, including DINO (image-image similarity), CLIP-I (image-image similarity), CLIP-T (image-text similarity), and HPS scores. These metrics objectively quantify the alignment between edited images and user preferences, as well as the preservation of original image identity. These quantitative results are reported in Tables 1 and 2 of the supplementary material, demonstrating our method's significant improvements over baselines. In the revised manuscript, we will explicitly emphasize these common quantitative metrics alongside user studies, ensuring a balanced and comprehensive evaluation of our framework.\n\n**Q5: Whether it's allowed to put any anonymous links to some image collections during rebuttal/Better to compare with current state-of-the-art instruction-based image editing methods or unified models with user preference prompts such as Bagel, MmaDA, Blip-3o, Show-o, Janus-series, Nexus-Gen, UniVG, Step1X, GoT, VAR-GPT, SEED-LLAMA, AnyEdit, etc.**\n\nWe thank the reviewer for raising this point. Due to NeurIPS rebuttal guidelines, unfortunately we are not allowed to share any visuals during this phase. However,  as requested, to better position our approach relative to state-of-the-art instruction-based and unified image editing models, we conducted *quantitative* comparisons specifically with Bagel, SEED-LLAMA, and Nexus-Gen—strong recent baselines in this area. For these comparisons, we explicitly provided user preferences as textual prompt inputs to these models. As summarized in the table below, our approach clearly outperformed these methods across standard image editing evaluation metrics, highlighting the distinct advantage of our collaborative personalization framework:\n\n| Method      | CLIP-I-PREF ↑      | DINO ↑             | HPS ↑               |\n| ----------- | ------------------ | ------------------ | ------------------- |\n| BAGEL       | 0.3397 ± 0.0144    | 0.0363 ± 0.0066    | 0.2475 ± 0.0388     |\n| SEED-Llama  | 0.3109 ± 0.0159    | 0.0349 ± 0.0121    | 0.2500 ± 0.0287     |\n| Nexus-Gen   | 0.3327 ± 0.0138    | **0.0373 ± 0.0113** | 0.2598 ± 0.0307     |\n| **Ours**    | **0.3515 ± 0.0190** | 0.0365 ± 0.0164    | **0.2607 ± 0.0328** |\n\nWe will explicitly incorporate these results into the revised manuscript to clearly demonstrate the superiority of our personalized editing approach over existing unified and instruction-based editing frameworks."}]

Author rebuttal:

Meta-review: The paper introduces the first framework for personalized image editing in diffusion models. During the rebuttal, the authors addressed the reviewers’ initial questions thoroughly, by including new baseline comparisons and user studies. Three out of four reviewers were satisfied with the responses and recommended acceptance. 

One reviewer, however, remained unconvinced, arguing that the techniques were similar to federated learning. The AC considers this criticism unpersuasive and agrees with the authors that reusing techniques from other fields does not diminish the novelty if they are effectively applied to a new problem. The authors also made strong contributions by integrating GNNs and DPO into diffusion models.

Overall, the paper proposes a timely and interesting problem, presents a clever solution for personalized diffusion models, and opens up a promising research direction in personalized generative AI with potential long-lasting impact. The AC recommends acceptance (poster). 

The authors are encouraged to incorporate the new experiments and discussions from the rebuttal into the final version.
Decision: Accept (poster)