import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("pci-lab/worldflow3d", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]WorldFlow3D — Front3D (indoor) models
Layout-conditioned 3D indoor-scene generation (3D-FRONT), as a coarse → color
refinement flow-matching cascade. Use with the
worldflow3d
package (pip install worldflow3d).
License. These models are trained on the 3D-FRONT dataset and are released for non-commercial / research use under the 3D-FRONT dataset terms. The
worldflow3dcode is Apache-2.0, but that license does not grant rights to these 3D-FRONT-derived weights. See the 3D-FRONT dataset terms.
The Waymo (outdoor) models are trained on the Waymo Open Dataset and live in
a separate repo, pci-lab/worldflow3d-waymo,
under the Waymo Dataset License (non-commercial).
Cascade stages
| Subfolder | Role |
|---|---|
front3d-coarse |
coarse layout-conditioned generation (direct-diffusion) |
front3d-color |
source-flow + color refinement (UNet in=4, color mesh sidecar) |
Usage
from worldflow3d import WorldFlow3DPipeline
pipe = WorldFlow3DPipeline.from_hub(
"pci-lab/worldflow3d", stage="front3d-coarse",
refinement_stages=["front3d-color"], device="cuda",
)
See the GitHub repo
for full docs and the generate_indoor CLI.
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