Adobe Research and KAIST introduce FlowTool: treating retouching tool parameters as conditional generation, cutting L1 by up to 28.8% and L2 by up to 47.4% on reference metrics with at least 50x lower latency
Researchers at Adobe Research and KAIST introduce FlowTool, which uses conditional rectified flow to directly model the distribution of high-quality retouching tool parameters conditioned on the input image and user instruction, combining a vision-language model backbone with a Diffusion Transformer parameter generator and a tool-presence head instead of autoregressive MLLM reasoning and token-by-token numeric generation; across MMArt-Bench, FlowTool-Eval, ArtEdit-Bench, and MIT-Adobe5K it achieves stronger reference-based performance than specialized MLLM editing agents and proprietary MLLMs, remains competitive with proprietary models under reference-free evaluation, and reduces inference latency by at least 50x while requiring nearly 2x less memory.