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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2510.16888 |
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| _version_ | 1866914135356211200 |
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| author | Li, Zongjian Liu, Zheyuan Zhang, Qihui Lin, Bin Wu, Feize Yuan, Shenghai Yan, Zhiyuan Ye, Yang Yu, Wangbo Niu, Yuwei Wang, Shaodong Cheng, Xinhua Yuan, Li |
| author_facet | Li, Zongjian Liu, Zheyuan Zhang, Qihui Lin, Bin Wu, Feize Yuan, Shenghai Yan, Zhiyuan Ye, Yang Yu, Wangbo Niu, Yuwei Wang, Shaodong Cheng, Xinhua Yuan, Li |
| contents | Instruction-based image editing has achieved remarkable progress; however, models solely trained via supervised fine-tuning often overfit to annotated patterns, hindering their ability to explore and generalize beyond training distributions. To this end, we introduce Edit-R1, a novel post-training framework for instruction-based image editing based on policy optimization. Specifically, we utilize Diffusion Negative-aware Finetuning (DiffusionNFT), a likelihood-free policy optimization method consistent with the flow matching forward process, thereby enabling the use of higher-order samplers and more efficient training. Another key challenge here is the absence of a universal reward model, resulting from the diverse nature of editing instructions and tasks. To bridge this gap, we employ a Multimodal Large Language Model (MLLM) as a unified, training-free reward model, leveraging its output logits to provide fine-grained feedback. Furthermore, we carefully design a low-variance group filtering mechanism to reduce MLLM scoring noise and stabilize optimization. \texttt{UniWorld-V2}, trained with this framework, achieves \textbf{state-of-the-art} results on the ImgEdit and GEdit-Bench benchmarks, scoring 4.49 and 7.83, respectively. Crucially, our framework is model-agnostic, delivering substantial performance gains when applied to diverse base models like Qwen-Image-Edit and FLUX-Kontext, demonstrating its wide applicability. Code and models are publicly available to support further research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_16888 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Uniworld-V2: Reinforce Image Editing with Diffusion Negative-aware Finetuning and MLLM Implicit Feedback Li, Zongjian Liu, Zheyuan Zhang, Qihui Lin, Bin Wu, Feize Yuan, Shenghai Yan, Zhiyuan Ye, Yang Yu, Wangbo Niu, Yuwei Wang, Shaodong Cheng, Xinhua Yuan, Li Computer Vision and Pattern Recognition Instruction-based image editing has achieved remarkable progress; however, models solely trained via supervised fine-tuning often overfit to annotated patterns, hindering their ability to explore and generalize beyond training distributions. To this end, we introduce Edit-R1, a novel post-training framework for instruction-based image editing based on policy optimization. Specifically, we utilize Diffusion Negative-aware Finetuning (DiffusionNFT), a likelihood-free policy optimization method consistent with the flow matching forward process, thereby enabling the use of higher-order samplers and more efficient training. Another key challenge here is the absence of a universal reward model, resulting from the diverse nature of editing instructions and tasks. To bridge this gap, we employ a Multimodal Large Language Model (MLLM) as a unified, training-free reward model, leveraging its output logits to provide fine-grained feedback. Furthermore, we carefully design a low-variance group filtering mechanism to reduce MLLM scoring noise and stabilize optimization. \texttt{UniWorld-V2}, trained with this framework, achieves \textbf{state-of-the-art} results on the ImgEdit and GEdit-Bench benchmarks, scoring 4.49 and 7.83, respectively. Crucially, our framework is model-agnostic, delivering substantial performance gains when applied to diverse base models like Qwen-Image-Edit and FLUX-Kontext, demonstrating its wide applicability. Code and models are publicly available to support further research. |
| title | Uniworld-V2: Reinforce Image Editing with Diffusion Negative-aware Finetuning and MLLM Implicit Feedback |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.16888 |