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
File size: 1,037 Bytes
afea9c4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | ---
license: mit
pipeline_tag: image-to-3d
tags:
- 3d-generation
- part-aware
- trellis
---
# 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.
|