Instructions to use layerdifforg/seethroughv0.0.2_layerdiff3d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use layerdifforg/seethroughv0.0.2_layerdiff3d with Diffusers:
pip install -U diffusers transformers accelerate
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] - Notebooks
- Google Colab
- Kaggle
|
Download README.md from layerdifforg/seethroughv0.0.2_layerdiff3d: direct link, hf CLI and curl.
- Browser
- Download file 2.66 kB
-
https://huggingface.co/layerdifforg/seethroughv0.0.2_layerdiff3d/resolve/main/README.md
- Command line
-
hf download hf://layerdifforg/seethroughv0.0.2_layerdiff3d/README.md
-
curl -L -o README.md https://huggingface.co/layerdifforg/seethroughv0.0.2_layerdiff3d/resolve/main/README.md
2.66 kB
| license: openrail++ | |
| base_model: cagliostrolab/animagine-xl-4.0 | |
| base_model_relation: finetune | |
| library_name: diffusers | |
| # See-through: LayerDiff 3D | |
| This is the model file for [See-through](https://github.com/shitagaki-lab/see-through) with the new tag definition. It | |
| generates the transparent body-part layers in the See-through pipeline, together | |
| with the [depth model](https://huggingface.co/layerdifforg/seethroughv0.0.1_marigold). 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/seethroughv0.0.2_layerdiff3d_nf4](https://huggingface.co/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](https://huggingface.co/cagliostrolab/animagine-xl-4.0) and [Stable Diffusion XL 1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0): CreativeML Open RAIL++-M License | |
| - [LayerDiffuse](https://huggingface.co/lllyasviel/LayerDiffuse_Diffusers): CreativeML Open RAIL-M License | |
| - [SDXL-VAE-FP16-Fix](https://huggingface.co/madebyollin/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](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} | |
| } | |
| ``` | |