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ARXIV:2604.19406 · IMAGE EDITING · SUBMITTED 22 APR · 20:32 UTC · FRESHNESS STALE
ARXIV:2604.19406IMAGE EDITINGSUBMITTED 22 APR · 20:32 UTCFRESHNESS STALEFan Li · Chonghuinan Wang · Lina Lei · Yuping Qiu · Jiaqi Xu · Jiaxiu Jiang · +6 at arXiv
A post-training framework and dataset for aligning diffusion-based image editing models with human preferences.
Opportunity summary
Pain A post-training framework and dataset for aligning diffusion-based image editing models with human preferences.
Evidence 244 refs | 4 sources | 83% coverage
Blocker Evidence verified
A post-training framework and dataset for aligning diffusion-based image editing models with human preferences. Meanwhile, although reinforcement learning (RL) methods such as Diffusion-DPO and Flow-GRPO have further improved generation quality, efficiently applying Reinforcement Learning…
Common image editing tasks typically adopt powerful generative diffusion models as the leading paradigm for real-world content editing. Meanwhile, although reinforcement learning (RL) methods such as Diffusion-DPO and Flow-GRPO have further improved generation quality,…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Extensive experiments demonstrate that our approach significantly enhances models such as Qwen-Image-Edit-2509, aligning their outputs more closely with human preference. Code availability is flagged…
Image Editing moved forward this cycle; last verified April 2026. Public score 8.0/10. Production flags indicate code availability.
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A post-training framework and dataset for aligning diffusion-based image editing models with human preferences.
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10.48550/arXiv.2604.19406A post-training framework and dataset for aligning diffusion-based image editing models with human preferences.
Abstract
Common image editing tasks typically adopt powerful generative diffusion models as the leading paradigm for real-world content editing. Meanwhile, although reinforcement learning (RL) methods such as Diffusion-DPO and Flow-GRPO have further improved generation quality, efficiently applying Reinforcement Learning from Human Feedback (RLHF) to diffusion-based editing remains largely unexplored, due to a lack of scalable human-preference datasets and frameworks tailored to diverse editing needs. To fill this gap, we propose HP-Edit, a post-training framework for Human Preference-aligned Editing, and introduce RealPref-50K, a real-world dataset across eight common tasks and balancing common object editing. Specifically, HP-Edit leverages a small amount of human-preference scoring data and a pretrained visual large language model (VLM) to develop HP-Scorer--an automatic, human preference-aligned evaluator. We then use HP-Scorer both to efficiently build a scalable preference dataset and to serve as the reward function for post-training the editing model. We also introduce RealPref-Bench, a benchmark for evaluating real-world editing performance. Extensive experiments demonstrate that our approach significantly enhances models such as Qwen-Image-Edit-2509, aligning their outputs more closely with human preference.
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Proof status
verified244 refs; 4 sources; 83% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 8.0
PROBLEM
A post-training framework and dataset for aligning diffusion-based image editing models with human preferences. Meanwhile, although reinforcement learning (RL) methods such as Diffusion-DPO and Flow-GRPO have further improved generation quality, efficiently applying Reinforcemen...
METHOD
Common image editing tasks typically adopt powerful generative diffusion models as the leading paradigm for real-world content editing. Meanwhile, although reinforcement learning (RL) methods such as Diffusion-DPO and Flow-GRPO have further improved generation quality, efficient...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Extensive experiments demonstrate that our approach significantly enhances models such as Qwen-Image-Edit-2509, aligning their outputs more closely with human preference. Code availability is flagged in t...
WHY NOW
Image Editing moved forward this cycle; last verified April 2026. Public score 8.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 26, "author": "Fan Li; Chonghuinan Wang; Lina Lei; Yuping Qiu; Jiaqi Xu; Jiaxiu Jiang; Xinran Qin; Zhikai Chen; Fenglong Song; Zhixin Wang; Renjing Pei; Wangmeng Zuo"
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A post-training framework and dataset for aligning diffusion-based image editing models with human preferences.
Segment
Image Editing
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Commercial read
8.0/10 public viability
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244 refs / 4 sources / 83% coverage
stale
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partial
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Research evidence exists; buyer urgency still needs source proof.
Evidence
244 references, 4 sources, 83% evidence coverage.
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Fan Li
Huawei Noah’s Ark Lab
Chonghuinan Wang
Huawei Noah’s Ark Lab
Lina Lei
Huawei Noah’s Ark Lab
Yuping Qiu
Huawei Noah’s Ark Lab
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This research introduces a significant advance in image editing by creating a framework that aligns editing tasks more closely with human preferences, addressing an important gap in the application of Reinforcement Learning from Human Feedback (RLHF) to editing models.
To productize this, build an API or plugin that integrates with existing image editing software to offer preference-aligned edits as a service, leveraging the RealPref-50K dataset for personalized editing recommendations.
HP-Edit could replace existing plugin models and automatic editing suggestions that do not use human feedback to align outcomes with user preferences, improving satisfaction and engagement.
The market is substantial, with growing demand for AI-enhanced creativity tools among both amateur and professional photographers. Businesses and individuals would pay for superior and customizable editing capabilities.
An AI-powered image editing tool for consumers that allows users to apply edits in a way that is more aligned with personal aesthetic preferences, ideal for social media content creators and professional photographers.
HP-Edit utilizes small amounts of human preference data and a visual language model (VLM) to develop HP-Scorer, which functions as both a dataset builder and reward function for training editing models. This approach scales preference data collection and aligns model outputs with human preferences, leading to improved performance on image editing tasks.
HP-Edit was tested using the RealPref-50K dataset across eight editing tasks. It enhanced existing models like Qwen-Image-Edit-2509, showing improved alignment with human preferences over traditional models.
HP-Edit relies heavily on the quality and diversity of the underlying preference data; biased data could lead to less representative results. Additionally, the success of deployment would depend on integration with existing editing software.
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