Dvd
This repository hosts the pretrained DVD checkpoints for Discrete Voxel Diffusion for 3D Generation and Editing.
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 1,50–1,55 GB
- Versão
- vImg BSP ft
- Atualizado
- 10 de out. de 2026
- Fonte
- Hugging Face
Descrição da fonte
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 stage 2 to produce final mesh and 3D Gaussian assets.
- Paper: arXiv:2605.07971
- Spaces: DVD Image, 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
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:
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:
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
@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},
}
Fontes
1 fonte
Fontes
1 fonteVerificado 10 de out.
Download files
Artefatos de modelo
4 artefatos
Artefatos de modelo
4 artefatosdvd_img_BSP_ft.safetensors
safetensors · 1,55 GB · SHA-256 6e053b0fdab3…a307 · Hugging Face
Download filedvd_text_BSP_ft.safetensors
safetensors · 1,50 GB · SHA-256 32bf1fb84c39…74a4 · Hugging Face
Download fileTrechos de fonte
2 trechos
Trechos de fonte
2 trechos--- 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 batc...
Source context: 0 downloads · 1 likes · Pipeline image-to-3d · Library pytorch · Repo Zhengrui/dvd
