Instructions to use AlayaLab/KaiNinja with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Trellis
How to use AlayaLab/KaiNinja with Trellis:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
KaiNinja
Weights of KaiNinja, a part-aware image-to-3D generator built on TRELLIS.2.
| File | Stage | Model | Weights |
|---|---|---|---|
stage1_layout_flow.safetensors |
Stage 1, dual-volume sparse-structure flow | TriSparseStructureFlowModel |
step 75k |
stage2_slat_flow.safetensors |
Stage 2, dual-stream structured-latent flow | TriSLatSeparateFlowModel |
EMA (0.9999), step 100k |
The matching *.json files hold the model constructor arguments. The shape decoder is the original
TRELLIS.2 one and is not included here.
Inference settings
- Stage 1: Euler flow sampler, 50 steps, classifier-free guidance 3.0; the two decoded volumes go through overlap deduplication before Stage 2.
- Stage 2: Euler flow sampler, 50 steps, classifier-free guidance 3.0.
- Each volume is decoded with the TRELLIS.2 shape decoder; parts are the connected components within each volume.
License
MIT, as the base model.
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