Instructions to use Cloth-splatters/dexgarmentlab-folding-lifting-dynamics-gps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Cloth-splatters/dexgarmentlab-folding-lifting-dynamics-gps with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Cloth-splatters/dexgarmentlab-folding-lifting-dynamics-gps", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
dexgarmentlab-folding-lifting-dynamics-gps
GPSDynamicsModel — graph-based (GNN + Transformer) dynamics model for
variable-vertex cloth meshes. Given the 3 previous mesh frames and a 3D
gripper action, predicts the next 5 mesh frames via DDPM diffusion, using each
cloth's own rest state and topology (no global template).
- Task data: DexGarmentLab mixed-garment fold + lift-place demos (
dexgarmentlab_folding_lifting_meshes.h5, Cloth-splatters/dexgarmentlab-folding-lifting-meshes) - Formulation: DDPM diffusion
- Max vertices per mesh: 2048
- Best validation loss: 0.00012608407087100204 (checkpoint in
model/ischeckpoint-best) - Training run:
dexgarment_dyn_gps_2026-08-03_12-45-01_7138502(full config inconfig.yml)
- Downloads last month
- -
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support