Towards In-Context Tone Style Transfer with A Large-Scale Triplet Dataset

Tone style transfer for photo retouching aims to adapt thestylistic tone of the reference image to a given content image. However,the lack of high-quality large-scale triplet datasets with stylized groundtruth forces existing methods to rely on self-supervised or proxy objec-tives, which limits model capability. To mitigate this gap, we design adata construction pipeline to build TST100K, a large-scale dataset of100,000 content-reference-stylized triplets. At the core of this pipeline,we train a tone style scorer to ensure strict stylistic consistency for eachtriplet. In addition, existing methods typically extract content and ref-erence features independently and then fuse them in a decoder, whichmay cause semantic loss and lead to inappropriate color transfer and de-graded visual aesthetics. Instead, we propose ICTone, a diffusion-basedframework that performs tone transfer in an in-context manner by jointlyconditioning on both images, leveraging the semantic priors of generativemodels for semantic-aware transfer. Reward feedback learning using thetone style scorer is further incorporated to improve stylistic fidelity andvisual quality. Experiments demonstrate the effectiveness of TST100K,and ICTone achieves state-of-the-art performance on both quantitativemetrics and human evaluations. The project page is available online.