Instructions to use Zhengrui/dvd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Trellis
How to use Zhengrui/dvd 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
| license: mit | |
| library_name: pytorch | |
| tags: | |
| - 3d-generation | |
| - voxel-generation | |
| - 3d-editing | |
| - diffusion | |
| - image-to-3d | |
| - text-to-3d | |
| - trellis | |
| pipeline_tag: image-to-3d | |
| # DVD: Discrete Voxel Diffusion for 3D Generation and Editing | |
| This repository hosts the pretrained DVD checkpoints for **Discrete Voxel Diffusion for 3D Generation and Editing**. | |
| DVD is a discrete diffusion pipeline that generates or edits a pure `64^3` voxel grid. The voxel output can be used directly, exported as a cubified voxel mesh, or passed as the sparse-structure anchor for [TRELLIS](https://github.com/microsoft/TRELLIS) stage 2 to produce final mesh and 3D Gaussian assets. | |
| - Paper: [arXiv:2605.07971](https://arxiv.org/abs/2605.07971) | |
| - Spaces: [DVD Image](https://huggingface.co/spaces/Zhengrui/dvd-image), [DVD Text](https://huggingface.co/spaces/Zhengrui/dvd-text) | |
| ## Checkpoints | |
| | Variant | Files | Use case | | |
| | --- | --- | --- | | |
| | Image generation | `dvd_img.json`, `dvd_img.safetensors` | image-conditioned voxel generation | | |
| | Image editing | `dvd_img_BSP_ft.json`, `dvd_img_BSP_ft.safetensors` | image-conditioned voxel editing | | |
| | Text generation | `dvd_text.json`, `dvd_text.safetensors` | text-conditioned voxel generation | | |
| | Text editing | `dvd_text_BSP_ft.json`, `dvd_text_BSP_ft.safetensors` | text-conditioned voxel editing | | |
| ## Minimal Usage | |
| ```python | |
| from dvd import DVDImageToVoxelPipeline, DVDTextToVoxelPipeline | |
| repo_id = "Zhengrui/dvd" | |
| image_dvd = DVDImageToVoxelPipeline.from_pretrained(repo_id, variant="base", device="cuda") | |
| image_edit_dvd = DVDImageToVoxelPipeline.from_pretrained(repo_id, variant="bsp", device="cuda") | |
| text_dvd = DVDTextToVoxelPipeline.from_pretrained(repo_id, variant="base", device="cuda") | |
| text_edit_dvd = DVDTextToVoxelPipeline.from_pretrained(repo_id, variant="bsp", device="cuda") | |
| ``` | |
| Example image-conditioned generation: | |
| ```python | |
| from PIL import Image | |
| from dvd import DVDImageToVoxelPipeline, export_cubified_voxels | |
| image = Image.open("assets/example_image/T.png") | |
| dvd = DVDImageToVoxelPipeline.from_pretrained("Zhengrui/dvd", variant="base", device="cuda") | |
| voxels = dvd.sample_voxels(image, seed=42, steps=256) | |
| export_cubified_voxels(voxels, "dvd_voxels.glb") | |
| ``` | |
| ## Voxel Convention | |
| DVD stores voxel outputs as: | |
| ```text | |
| samples: [B, D, H, W] | |
| coords: [N, 4] as [batch, x, y, z] | |
| ``` | |
| Saved `.npy` coordinate files usually omit the batch dimension and can be loaded back with `as_voxel_output(coords, resolution=64)`. | |
| ## Notes | |
| These files are the DVD voxel-stage checkpoints. TRELLIS stage-2 checkpoints are loaded separately from the official TRELLIS model repositories, such as `microsoft/TRELLIS-image-large` and `microsoft/TRELLIS-text-large`. | |
| ## Citation | |
| ```bibtex | |
| @misc{xiang2026dvddiscretevoxeldiffusion, | |
| title={DVD: Discrete Voxel Diffusion for 3D Generation and Editing}, | |
| author={Zhengrui Xiang and Jiaqi Wu and Fupeng Sun and Heliang Zheng and Yingzhen Li}, | |
| year={2026}, | |
| eprint={2605.07971}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV}, | |
| url={https://arxiv.org/abs/2605.07971}, | |
| } | |
| ``` | |