| --- |
| license: other |
| library_name: diffusers |
| pipeline_tag: image-to-image |
| base_model: stabilityai/sd-turbo |
| tags: |
| - image-restoration |
| - novel-view-synthesis |
| - 3d-gaussian-splatting |
| - dualdiff3d |
| --- |
| |
| # DualDiff |
|
|
| Official DualDiff repair checkpoint for |
| [DualDiff3D](https://github.com/Akaneqwq/DualDiff3D). |
|
|
| DualDiff repairs a degraded novel-view rendering using its structure together |
| with the appearance of a nearby reference image. |
|
|
| ## Usage |
|
|
| The DualDiff3D code downloads this checkpoint automatically: |
|
|
| ```bash |
| python -m DualDiff.hub |
| ``` |
|
|
| Run image-pair inference: |
|
|
| ```bash |
| python DualDiff/infer.py \ |
| --input_image /path/to/degraded_render.png \ |
| --ref_image /path/to/reference.png \ |
| --output_dir outputs/repair |
| ``` |
|
|
| The checkpoint contains the complete structure and appearance U-Net state |
| dictionaries plus the trained VAE LoRA and skip-connection parameters. It does |
| not contain optimizer state. The SD-Turbo VAE, text encoder, tokenizer, |
| scheduler, and base configuration are loaded separately by the code. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{wang2026dualdiff3d, |
| title = {DualDiff3D: Dual Structure-Appearance Diffusion |
| Priors for Reliability-Enhanced 3D Gaussian Splatting}, |
| author = {Qian Wang and Yu Wang and Weiqi Li and Xinhua Cheng |
| and Xiandong Meng and Ronggang Wang and Jian Zhang}, |
| booktitle = {European Conference on Computer Vision (ECCV)}, |
| year = {2026} |
| } |
| ``` |
|
|
| ## License |
|
|
| The DualDiff code is released under Apache-2.0. This checkpoint is derived from |
| [SD-Turbo](https://huggingface.co/stabilityai/sd-turbo); use of the checkpoint |
| is also subject to the applicable SD-Turbo model license. |
|
|
