A training-free framework for high-fidelity appearance transfer via diffusion transformers
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912985482526720 |
|---|---|
| author | Gu, Shengrong Wang, Ye Wu, Song Ma, Rui Wang, Qian Wang, Lanjun Yi, Zili |
| author_facet | Gu, Shengrong Wang, Ye Wu, Song Ma, Rui Wang, Qian Wang, Lanjun Yi, Zili |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_26767 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A training-free framework for high-fidelity appearance transfer via diffusion transformers Gu, Shengrong Wang, Ye Wu, Song Ma, Rui Wang, Qian Wang, Lanjun Yi, Zili Computer Vision and Pattern Recognition 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. |
| title | A training-free framework for high-fidelity appearance transfer via diffusion transformers |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.26767 |