Uniworld-V2: Reinforce Image Editing with Diffusion Negative-aware
  Finetuning and MLLM Implicit Feedback
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. UniWorld-V2, trained with this framework, achieves
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 at
https://github.com/PKU-YuanGroup/UniWorld-V2.
68f7036a24c4489363111985
Zongjian Li
68f7036a24c4489363111986
Zheyuan Liu
68f7036a24c4489363111987
Qihui Zhang
claimed_verified
2025-10-21T14:03:12.558Z
67a99d1fef1439e285c4cbec
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/VrwUmrY2wsg4sVSIMc--K.png
Qihui Zhang
user
77Hui
68f7036a24c4489363111988
Bin Lin
claimed_verified
2025-10-21T14:03:19.442Z
6367a8175bb06007ea099b8f
https://cdn-avatars.huggingface.co/v1/production/uploads/6367a8175bb06007ea099b8f/IjG7HyWyWRlVt_XwRbxRW.jpeg
linbin
user
LanguageBind
68f7036a24c4489363111989
Shenghai Yuan
claimed_verified
2025-10-21T14:03:16.291Z
63468720dd6d90d82ccf3450
https://cdn-avatars.huggingface.co/v1/production/uploads/63468720dd6d90d82ccf3450/tVBFlmZNz8FRMkOrDaDID.jpeg
YSH
user
BestWishYsh
68f7036a24c448936311198a
Zhiyuan Yan
68f7036a24c448936311198b
Yang Ye
68f7036a24c448936311198c
Wangbo Yu
68f7036a24c448936311198d
Yuwei Niu
68f7036a24c448936311198e
Li Yuan
Uniworld-V2: Reinforce Image Editing with Diffusion Negative-aware
  Finetuning and MLLM Implicit Feedback
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. UniWorld-V2, trained with this framework, achieves
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 at
https://github.com/PKU-YuanGroup/UniWorld-V2.
Edit-R1, a post-training framework using Diffusion Negative-aware Finetuning and a Multimodal Large Language Model, achieves state-of-the-art results in instruction-based image editing by addressing overfitting and lack of a universal reward model.
Diffusion Negative-aware Finetuning DiffusionNFT flow matching higher-order samplers Multimodal Large Language Model MLLM low-variance group filtering UniWorld-V2 ImgEdit GEdit-Bench