README.md

---
license: openrail++
base_model: prs-eth/marigold-depth-v1-1
base_model_relation: finetune
library_name: diffusers
---

# See-through: Marigold Depth

The pseudo-depth model of [See-through](https://github.com/shitagaki-lab/see-through), fine-tuned from Marigold Depth
v1.1 for anime characters. It is used together with the
[LayerDiff 3D model](https://huggingface.co/layerdifforg/seethroughv0.0.2_layerdiff3d) to order the decomposed layers. Read our
[GitHub repository](https://github.com/shitagaki-lab/see-through) for usage and details.

A 4-bit NF4 version for GPUs with less memory is available at
[24yearsold/seethroughv0.0.1_marigold_nf4](https://huggingface.co/24yearsold/seethroughv0.0.1_marigold_nf4).

## Licence

The See-through code is licensed under Apache-2.0. These weights are released
under Apache-2.0 for our own contributions, and they also inherit the licences
of the models they are derived from:

- [Marigold Depth v1.1](https://huggingface.co/prs-eth/marigold-depth-v1-1) (PRS, ETH Zürich): Open RAIL++-M License
- Stable Diffusion 2 (Stability AI), the base model of Marigold: CreativeML Open RAIL++-M License. The use restrictions in its Attachment A are the ones that apply to these weights, since the Open RAIL++-M text published for Marigold leaves its own list as a placeholder.

Commercial use is permitted. The use-based restrictions in paragraph 5 and
Attachment A of the Open RAIL licences apply to every use of these weights. If
you distribute the weights or a derivative of them, or host them as a service,
you must include those restrictions as an enforceable provision in the terms
that govern that use, and tell your users about them.

The `license` field above reads `openrail++` because that licence sets the
conditions of use; our Apache-2.0 grant applies on top of it. See
[LICENSE](LICENSE) for the full terms and [NOTICE](NOTICE) for attributions and
the changes we made.

## Citation

If you find this work useful, please cite:

```bibtex
@inproceedings{lin2026seethrough,
  author={Lin, Jian and Li, Chengze and Qin, Haoyun and Chan, Kwun Wang and Jin, Yanghua and Liu, Hanyuan and Choy, Stephen Chun Wang and Liu, Xueting},
  title={See-through: Single-image Layer Decomposition for Anime Characters},
  booktitle={Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers},
  series={SIGGRAPH Conference Papers '26},
  publisher={Association for Computing Machinery},
  address={New York, NY, USA},
  year={2026},
  pages={1--11},
  doi={10.1145/3799902.3811209},
  url={https://doi.org/10.1145/3799902.3811209}
}
```