Instructions to use layerdifforg/seethroughv0.0.1_marigold with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use layerdifforg/seethroughv0.0.1_marigold 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/seethroughv0.0.1_marigold", 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
File size: 1,909 Bytes
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Copyright 2026 The See-through Authors
This model is part of See-through (https://github.com/shitagaki-lab/see-through).
The See-through code is licensed under the Apache License, Version 2.0.
The licence terms for these model weights are in LICENSE.
==============================================================================
Third-party models
==============================================================================
This model is derived from, or includes components of, the following:
* Marigold Depth v1.1 by PRS, ETH Zürich. Copyright (c) 2023-2025 PRS, ETH Zürich.
https://huggingface.co/prs-eth/marigold-depth-v1-1
Open RAIL++-M License
The UNet was fine-tuned from this model. The VAE, text encoder and tokenizer
are taken from it.
* Stable Diffusion 2 by Stability AI. Copyright (c) 2022 Stability AI and contributors.
CreativeML Open RAIL++-M License
Base model of Marigold. The use-based restrictions in its Attachment A apply
to this model.
==============================================================================
Changes made by The See-through Authors
==============================================================================
* The UNet was fine-tuned from Marigold Depth v1.1 for pseudo-depth estimation
of anime characters, and converted into the multi-frame UNet
(UNetFrameConditionModel) used by the See-through pipeline.
* All components were saved in the diffusers format.
==============================================================================
Citation
==============================================================================
Jian Lin, Chengze Li, Haoyun Qin, Kwun Wang Chan, Yanghua Jin, Hanyuan Liu,
Stephen Chun Wang Choy, and Xueting Liu. 2026. See-through: Single-image Layer
Decomposition for Anime Characters. In SIGGRAPH Conference Papers '26. ACM.
https://doi.org/10.1145/3799902.3811209
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