HIVE-3D
This repository contains the two trained components used by HIVE-3D on top of
microsoft/TRELLIS-image-large:
denoiser: an IP-Adapter sparse-structure flow model.latent_projector: the latent conditioning projector used by the denoiser.
Both components are from training step 200000 and are distributed in the safetensors format. The HIVE-3D source code is required to construct the model classes and run the complete image-to-3D pipeline.
Files
denoiser/
├── config.json
└── model.safetensors
latent_projector/
├── config.json
└── model.safetensors
Loading
Download this repository through huggingface_hub, then pass the component
files to the HIVE-3D pipeline:
from pathlib import Path
from huggingface_hub import snapshot_download
from trellis.pipelines.test_pipeline import TestImageToSlatPipeline
checkpoint_root = Path(snapshot_download("mocun123/HIVE-3D"))
pipeline = TestImageToSlatPipeline.from_pretrained(
"microsoft/TRELLIS-image-large",
checkpoint_root / "denoiser/model.safetensors",
checkpoint_root / "latent_projector/model.safetensors",
checkpoint_root / "denoiser/config.json",
checkpoint_root / "latent_projector/config.json",
)
pipeline.cuda()
HIVE-3D follows the TRELLIS runtime requirements. A CUDA-capable NVIDIA GPU is required for the full pipeline; at least 16 GB of GPU memory is recommended.
Checksums
4c679dd55812709b2fa39547f714d63aeabe62f5b63fcf0191fee182ce4aa77a denoiser/model.safetensors
a8ce9615599020a7bf5c26538c7764431440da51439d32aeb5dc0815ca97369f latent_projector/model.safetensors
Base Model
HIVE-3D builds on the MIT-licensed
microsoft/TRELLIS-image-large
model. This repository does not redistribute the TRELLIS base-model weights;
they are downloaded from the official repository at runtime.
License Notice
A license for the HIVE-3D weights has not yet been declared in this model card. Users should review the HIVE-3D project license, the TRELLIS license, and the licenses of any optional third-party components before redistribution or commercial use.