import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("layerdifforg/seethroughv0.0.2_layerdiff3d", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]See-through: LayerDiff 3D
This is the model file for See-through with the new tag definition. It generates the transparent body-part layers in the See-through pipeline, together with the depth model. Read our GitHub repository for usage and details.
A 4-bit NF4 version for GPUs with less memory is available at 24yearsold/seethroughv0.0.2_layerdiff3d_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:
- 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.
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}
}
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