Diffusion Transformers (DiTs) excel at generation, but their global self-attention makes controllable, reference-image-based editing a distinct challenge. Unlike U-Nets, naively injecting local appearance into a DiT can disrupt its holistic scene structure. We address this by proposing the first training-free framework specifically designed to tame DiTs for high-fidelity appearance transfer. Our core is a synergistic system that disentangles structure and appearance. We leverage high-fidelity inversion to establish a rich content prior for the source image, capturing its lighting and micro-textures. A novel attention-sharing mechanism then dynamically fuses purified appearance features from a reference, guided by geometric priors. Our unified approach operates at 1024px and outperforms specialized methods on tasks ranging from semantic attribute transfer to fine-grained material application. Extensive experiments confirm our state-of-the-art performance in both structural preservation and appearance fidelity.
@article{arxiv.2603.26767,
title = {A training-free framework for high-fidelity appearance transfer via diffusion transformers},
author = {Shengrong Gu and Ye Wang and Song Wu and Rui Ma and Qian Wang and Lanjun Wang and Zili Yi},
journal= {arXiv preprint arXiv:2603.26767},
year = {2026}
}