Instructions to use ddalcu/Hunyuan3D-2.1-MLX-Serve-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ddalcu/Hunyuan3D-2.1-MLX-Serve-8bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Hunyuan3D-2.1-MLX-Serve-8bit ddalcu/Hunyuan3D-2.1-MLX-Serve-8bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Hunyuan3D 2.1 for MLX Core / mlx-serve (8-bit, combined)
Photo → textured, rigged 3D model — fully on-device on Apple Silicon. This is the complete 3D generation stack for mlx-serve / MLX Core, converted to MLX-native safetensors and quantized to 8-bit (group size 64), packaged as one repo so the app downloads one thing:
| Stage | Where | Contents | Source |
|---|---|---|---|
| Shape (image → mesh) | repo root | dit.safetensors (3.3B MoE flow-match DiT), conditioner.safetensors (DINOv2-Large), vae.safetensors (ShapeVAE decoder + geo-decoder) |
tencent/Hunyuan3D-2.1 |
| Texture (full PBR paint) | paint/ |
unet.safetensors (2.5D multiview UNet), unet_dual.safetensors (reference stream), vae.safetensors (SD 2.x AutoencoderKL), dino.safetensors (DINOv2-giant) |
tencent/Hunyuan3D-2.1 (hunyuan3d-paintpbr-v2-1) + facebook/dinov2-giant |
| Auto-rig (skeleton + skin) | unirig/ |
skeleton.safetensors (UniRig stage-1 AR skeleton: michelangelo perceiver + OPT-350m decoder) |
VAST-AI/UniRig |
Total ≈ 8.1 GB. Dense tensors are fp16; eligible linears are 8-bit affine-quantized.
Use
The MLX Core app's 3D pane downloads this
automatically. With a standalone mlx-serve:
hf download ddalcu/Hunyuan3D-2.1-MLX-Serve-8bit --local-dir ~/.mlx-serve/models/ddalcu/Hunyuan3D-2.1-MLX-Serve-8bit
mlx-serve --serve --model-dir ~/.mlx-serve/models
curl http://localhost:8080/v1/3d/generations -H 'Content-Type: application/json' -d '{
"model": "ddalcu/Hunyuan3D-2.1-MLX-Serve-8bit",
"image": "<base64 PNG/JPEG>",
"texture": true,
"rig": true
}' # → {"format":"glb","data":"<base64 GLB>"} — a textured, rigged glTF binary
texture and rig are optional (shape-only is fastest); the server resolves the
paint/ and unirig/ stages from this repo's subdirectories automatically.
Conversion & parity
Converted with the scripts in the mlx-serve repo (tests/convert_hunyuan3d_weights.py,
tests/convert_hunyuan3d_paint_weights.py, tests/convert_unirig_weights.py). The
native Zig engines were validated against the PyTorch references with cosine-similarity
oracles: shape e2e SDF grid 0.9998, paint full-UNet denoise step 1.0000, UniRig greedy
skeleton decode token-exact. Structural transforms (per-head QKV de-interleaves, MoE
expert stacking, NCHW→OHWI conv layout) are baked out at convert time.
Licenses
- Shape + paint weights: Tencent Hunyuan3D-2.1 Community License (also
paint/LICENSE). - DINOv2 encoders (
conditioner.safetensors,paint/dino.safetensors): Apache-2.0 (Meta AI). - UniRig skeleton weights: MIT (VAST-AI-Research); see
unirig/NOTICEfor the code/weights licensing split.
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Quantized
Model tree for ddalcu/Hunyuan3D-2.1-MLX-Serve-8bit
Base model
VAST-AI/UniRig