HIVE: Harnessing Human Feedback for Instructional Visual Editing
HIVE leverages human feedback to enhance instruction-based visual editing via reward modeling and offline RL, significantly improving alignment.
Key Findings
Methodology
HIVE consists of three steps: first, instruction supervised fine-tuning on 1.1M dataset; second, collecting human preference rankings to train a reward model using BLIP-based multi-modal encoders; third, applying offline RL techniques (e.g., PPO variants) to incorporate reward signals into diffusion model fine-tuning. The reward model employs preference ranking data to optimize a scalar preference score. Two scalable fine-tuning methods—Weighted Reward Loss and Conditional Reward Loss—are introduced, with the latter showing superior performance. The framework effectively aligns generated images with user instructions, as validated by extensive quantitative and qualitative experiments.
Key Results
- On synthetic datasets, HIVE improves CLIP similarity scores by approximately 15% over InstructPix2Pix, with over 75% of user preference votes favoring HIVE-generated images. In real image user studies, HIVE outperforms baseline models by around 25% in preference votes. Ablation studies confirm that reward modeling and cycle consistency augmentation significantly boost performance. These results demonstrate the effectiveness of human feedback in guiding visual editing models.
- HIVE achieves superior accuracy in complex multi-object and multi-step instructions, maintaining image details and reducing over-editing. The preference-based training results in more natural, user-aligned outputs, with higher consistency and satisfaction scores across diverse scenarios. The experiments validate that integrating human preferences via reward models and offline RL substantially enhances the quality and robustness of instruction-guided image editing.
Significance
This work advances the field of content personalization by integrating human preferences into diffusion-based image editing. It addresses longstanding challenges of misalignment and over-editing, providing a scalable, efficient framework that bridges human intuition with generative models. The approach opens new avenues for personalized media creation, virtual assistants, and adaptive content generation, with potential industry-wide impacts on automation and user-centric AI systems.
Technical Contribution
The paper introduces a novel framework combining preference ranking-based reward modeling with scalable offline RL strategies for diffusion models. It innovates by designing efficient fine-tuning approaches—Weighted Reward Loss and Conditional Reward Loss—that balance computational cost and performance. The integration of cycle consistency augmentation enriches training data, improving robustness. The reward model architecture leverages pre-trained vision-language encoders, enabling precise alignment with human preferences. These contributions collectively push the frontier of personalized, preference-aware generative AI.
Novelty
This is the first comprehensive system to incorporate human preference feedback directly into diffusion model fine-tuning for instruction-based visual editing. Unlike prior works that rely solely on supervised data or heuristics, HIVE employs a learned reward function trained on preference rankings, combined with offline RL, to guide image editing. The innovative use of cycle consistency augmentation further enhances data diversity, making this approach a significant leap beyond existing supervised or rule-based methods, setting a new standard for personalized content generation.
Limitations
- The approach depends heavily on large-scale, high-quality preference datasets, which are costly to acquire and may introduce bias, affecting fairness and generalization.
- The models still struggle with highly complex or ambiguous instructions, especially in multi-object or multi-step scenarios, limiting applicability in some real-world cases.
- Computational costs for training and fine-tuning remain high, posing challenges for deployment in resource-constrained environments. Future work needs to optimize efficiency and address bias issues.
Future Work
Future research will focus on automating preference data collection, reducing annotation costs, and improving bias mitigation strategies. Extending the framework to multi-modal preferences and multi-turn interactions could enable more sophisticated, personalized AI assistants. Additionally, efforts to improve model efficiency and interpretability will facilitate broader deployment, making personalized AI content creation accessible across various platforms and devices.
AI Executive Summary
HIVE introduces a pioneering framework that significantly enhances instruction-based visual editing by integrating human feedback into the training process. Traditional models often struggle with understanding complex instructions and maintaining image fidelity, leading to results that deviate from user expectations. To address this, HIVE employs a three-stage process: first, it performs instruction supervised fine-tuning on a large-scale dataset of 1.1 million image-instruction pairs, establishing a solid foundation. Next, it collects human preference rankings—3,600 pairs—using annotators to evaluate multiple output variations, training a reward model based on a pre-trained BLIP encoder. This reward model captures nuanced user preferences, serving as a guide for the final step: offline reinforcement learning. By applying techniques like PPO variants, the diffusion model is fine-tuned to maximize alignment with human preferences, resulting in more accurate and natural edits.
Experimental results demonstrate that HIVE outperforms previous state-of-the-art methods such as InstructPix2Pix, with a 15% improvement in CLIP similarity scores and a preference vote margin exceeding 25%. User studies confirm higher satisfaction and better preservation of image details, especially in complex editing tasks. The framework’s ability to reduce over-editing and better interpret instructions marks a significant step forward in personalized content generation. The research also introduces cycle consistency augmentation, which enriches training data and enhances robustness.
Overall, HIVE’s innovative combination of reward modeling, offline RL, and data augmentation paves the way for more intelligent, user-aligned AI systems. Its scalable design and promising results suggest broad applicability in personalized media, virtual assistants, and automated content creation. Future directions include automating preference data collection, addressing bias, and extending multi-modal capabilities, aiming to make personalized AI-driven editing accessible and fair for diverse user groups.
Deep Dive
Abstract
Incorporating human feedback has been shown to be crucial to align text generated by large language models to human preferences. We hypothesize that state-of-the-art instructional image editing models, where outputs are generated based on an input image and an editing instruction, could similarly benefit from human feedback, as their outputs may not adhere to the correct instructions and preferences of users. In this paper, we present a novel framework to harness human feedback for instructional visual editing (HIVE). Specifically, we collect human feedback on the edited images and learn a reward function to capture the underlying user preferences. We then introduce scalable diffusion model fine-tuning methods that can incorporate human preferences based on the estimated reward. Besides, to mitigate the bias brought by the limitation of data, we contribute a new 1M training dataset, a 3.6K reward dataset for rewards learning, and a 1K evaluation dataset to boost the performance of instructional image editing. We conduct extensive empirical experiments quantitatively and qualitatively, showing that HIVE is favored over previous state-of-the-art instructional image editing approaches by a large margin.