24yearsold/seethroughv0.0.2_layerdiff3d_nf4

See-through: LayerDiff 3D (NF4)

A 4-bit NF4 quantisation of layerdifforg/seethroughv0.0.2_layerdiff3d for GPUs with less memory. The UNet and both text encoders are quantised with bitsandbytes (NF4, bfloat16 compute dtype). The VAE and the transparent VAE are unquantised and identical to the full model.

It is used by inference/scripts/inference_psd_quantized.py (the default --quant_mode nf4), which needs requirements-inference-bnb.txt. Read our GitHub repository for usage and details.

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:

• Animagine XL 4.0 and Stable Diffusion XL 1.0: CreativeML Open RAIL++-M License
• LayerDiffuse: CreativeML Open RAIL-M License
• SDXL-VAE-FP16-Fix (the VAE): MIT License

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 for the full terms and NOTICE for attributions and the changes we made.

This quantised copy carries the same licence terms as the full model.

Citation

If you find this work useful, please cite:

@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}
}