Alchemy3D is the primary checkpoint of an open-sourced foundation model for editing existing 3D assets while preserving identity and structure. Given a source mesh (e.g. .glb) and a target reference image, it performs versatile edits including Addition, Removal, Replacement, Local/Global Appearance, and Animation.
The model is trained on Alchemy3D-1M (1.25M unique assets, 1.38M edit pairs, 7 edit types) and builds on TRELLIS.2 structured latents.
Model Variants
Weights are mirrored on Hugging Face and ModelScope. If Hugging Face is unreachable, load the ModelScope IDs below — the official code falls back automatically.
| Model | Description | Hugging Face | ModelScope |
|---|---|---|---|
| Alchemy3D | Primary image-conditioned editing model | libadi/Alchemy3D | libd55/Alchemy3D |
| Alchemy3D-Turbo | Step-distilled variant for faster inference with competitive quality | libadi/Alchemy3D-Turbo | libd55/Alchemy3D-Turbo |
| Alchemy3D-Flux | Replaces DINOv3 with a Flux2 encoder for stronger PBR / appearance editing | libadi/Alchemy3D-Flux | libd55/Alchemy3D-Flux |
| Alchemy3D-Instruct | Instruction-driven editing from natural-language text instead of a target image | libadi/Alchemy3D-Instruct | libd55/Alchemy3D-Instruct |
| Alchemy3D-Segment | Downstream 3D part segmentation from 1–8 multi-view 2D segmentation maps | libadi/Alchemy3D-Segment | libd55/Alchemy3D-Segment |
Quick Start
Install and run from the official repository.
from alchemy3d.pipelines import Pipeline
import o_voxel
pipe = Pipeline.from_pretrained("libadi/Alchemy3D")
pipe.cuda()
output = pipe.run(
source="./assets/examples/edits/01/source.glb",
image="./assets/examples/edits/01/target_image.png",
comparison_video="example.mp4",
)[0]
glb = o_voxel.postprocess.to_glb(
vertices=output.vertices,
faces=output.faces,
attr_volume=output.attrs,
coords=output.coords,
attr_layout=output.layout,
voxel_size=output.voxel_size,
aabb=[[-0.5, -0.5, -0.5], [0.5, 0.5, 0.5]],
decimation_target=1000000,
texture_size=4096,
remesh=True,
remesh_band=1,
remesh_project=0,
verbose=False,
)
glb.export("example.glb")
Hardware: NVIDIA GPU recommended; ~24GB VRAM is a practical minimum. See the GitHub README for full environment setup (
setup.sh).
Citation
If you use Alchemy3D, please cite:
@misc{li2026scalingversatile3dassets,
title={Scaling Versatile 3D Assets Editing with a Million-Scale Dataset},
author={Badi Li and Tianxin Huang and Yu Zhou and Wei-Shi Zheng and Yi Ma and Shenghua Gao},
year={2026},
eprint={2609.34271},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2609.34271},
}
License
Apache License 2.0. See the GitHub repository for dependency licenses (O-Voxel / TRELLIS.2, nvdiffrast, nvdiffrec, etc.).