README.md

metadata

license: cc-by-nc-4.0
library_name: diffusers
tags:
  - diffusers
  - safetensors
  - vfxdb
  - openvdb
  - 3d
  - volumetric

VfxDB paper checkpoints

Inference-only EMA checkpoints for the VfxDB static-unconditional, static-conditional, and temporal-conditional 3D diffusion models.

The tensor values were converted losslessly from the paper step_1000000.pt checkpoints. The public diffusion_pytorch_model.safetensors files contain the paper EMA weights directly; they do not contain optimizer state, online weights, or pickle payloads.

Checkpoints

Subfolder	Task	Input channels	Classes	Weight size
static-unconditional-32	uncond_static	1	0	154.61 MiB
static-conditional-32	cond_static	1	11	154.62 MiB
temporal-conditional-32	cond_temporal	2	11	154.63 MiB

All checkpoints use a 32³ dense volume, a 200-step linear DDPM schedule, epsilon prediction, an occupancy output head, and the log1p value space with scale 0.02.

Required inference behavior

Exact paper-aligned inference requires:

• scheduler_name: ddpm
• scheduler_legacy_align: true
• sampling_steps: 200
• only_cfg_eps: true
• the per-checkpoint CFG and temporal settings in each inference_config.yaml

Using the native Diffusers DDPM scheduler without legacy alignment does not reproduce the historical sampler exactly.

Load the model weights

Use the VfxDB model implementation and Hub-aware inference entrypoint from the official repository, pinned here to commit d8cc604594f6875677a32117bc051c539e8427a7:

git clone https://github.com/VfxDB-Official/VfxDB.git
cd VfxDB
git checkout d8cc604594f6875677a32117bc051c539e8427a7
python -m pip install -r requirements-core.txt

from models.vfx_model import UNet3DModel

model = UNet3DModel.from_pretrained(
    "ryogishiki/VfxDB-models",
    subfolder="checkpoints/paper-v1/static-conditional-32",
)
model.eval()

The model is a custom VfxDB Diffusers ModelMixin, not an official built-in Diffusers architecture. Construct the VfxDBDensePipeline with the official repository code and enable the legacy-aligned scheduler.

The official CLI can load each Hub subfolder directly with the paper-aligned preset configs:

python infer_one_stage_hf.py --config configs/infer_paper_static_unconditional_32.yaml
python infer_one_stage_hf.py --config configs/infer_paper_static_conditional_32.yaml
python infer_one_stage_hf.py --config configs/infer_paper_temporal_conditional_32.yaml

The presets pin this repository's initial weight revision and do not require a pickle checkpoint or trainer_state.pt.

Provenance

• Model milestone: 896aadcf799f6392100025ffbcabbe2d83357201 (milestone_v1_0)
• Paper backup: 0e65b55b3093cbda42a6824fd0a2e76e0b743dad
• Paper backup 2: ffa577bbb70d71ebe312e7078d9e82a490f9ba87
• Weight conversion and initial validation code: b74cf7db151d83a47e6598f6418a32a0ac68e7c6
• Hub-ready public inference release: d8cc604594f6875677a32117bc051c539e8427a7

Exact source and artifact hashes are recorded in checkpoints/paper-v1/manifest.json and each checkpoint's provenance.json.

License

CC BY-NC 4.0, matching the VfxDB dataset release.