| title | FlowTool: Controlling Tool Parameter in Image Retouching via Flow Matching |
|---|---|
| authors | Thanh-Long V. Le, Steven Walton, Seunghyun Yoon, Branislav Kveton, Trung Bui, Eunho Yang, Viet Lai |
| date | 2026-09-28 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.35673 |
| upvotes | 31 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | Tool-based image editing (image retouching) is commonly formulated with autoregressive multimodal large language models (MLLMs) that sequentially generate reasoning, tool selections, and parameter values. In this work, we present a novel approach to tool-based image editing by framing the task as a flow matching problem. We introduce FlowTool, a framework that directly models the distribution of high-quality tool parameters conditioned on the input image and user instruction using conditional rectified flow. FlowTool combines a vision-language model backbone for multimodal understanding with a Diffusion Transformer parameter generator that transforms Gaussian noise into an editing plan. We train FlowTool with a two-stage supervised flow-matching curriculum, followed by reward-based post-training. Across MMArt-Bench, FlowTool-Eval, ArtEdit-Bench, and MIT-Adobe5K, FlowTool achieves significantly stronger reference-based performance than specialized MLLM editing agents and proprietary MLLMs, while remaining competitive with proprietary models under reference-free evaluation. Moreover, FlowTool significantly improves inference efficiency, reducing latency by at least 50times while requiring nearly 2times less memory than the compared baselines. These results demonstrate that tool-based image editing can be effectively modeled as conditional generation over structured continuous editing parameters, without autoregressive reasoning. |
cite
@misc{le2026flowtool,
title={FlowTool: Controlling Tool Parameter in Image Retouching via Flow Matching},
author={Thanh-Long V. Le and Steven Walton and Seunghyun Yoon and Branislav Kveton and Trung Bui and Eunho Yang and Viet Lai},
year={2026},
eprint={2609.35673},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.35673}
}