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jepa-wms

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🤖 JEPA-WMs Pretrained Models

Github

ArXiv

Meta AI Research, FAIR

This 🤗 HuggingFace repository hosts pretrained JEPA-WM world models.
👉 See the main repository for training code and datasets.

This repository contains pretrained world model checkpoints from the paper "What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?"

Available Models

JEPA-WM Models

Model	Environment	Resolution	Encoder	Pred. Depth
`jepa_wm_droid`	DROID & RoboCasa	256×256	DINOv3 ViT-L/16	12
`jepa_wm_metaworld`	Metaworld	224×224	DINOv2 ViT-S/14	6
`jepa_wm_pusht`	Push-T	224×224	DINOv2 ViT-S/14	6
`jepa_wm_pointmaze`	PointMaze	224×224	DINOv2 ViT-S/14	6
`jepa_wm_wall`	Wall	224×224	DINOv2 ViT-S/14	6

DINO-WM Baseline Models

Model	Environment	Resolution	Encoder	Pred. Depth
`dino_wm_droid`	DROID & RoboCasa	224×224	DINOv2 ViT-S/14	6
`dino_wm_metaworld`	Metaworld	224×224	DINOv2 ViT-S/14	6
`dino_wm_pusht`	Push-T	224×224	DINOv2 ViT-S/14	6
`dino_wm_pointmaze`	PointMaze	224×224	DINOv2 ViT-S/14	6
`dino_wm_wall`	Wall	224×224	DINOv2 ViT-S/14	6

V-JEPA-2-AC Baseline Models

Model	Environment	Resolution	Encoder	Pred. Depth
`vjepa2_ac_droid`	DROID & RoboCasa	256×256	V-JEPA-2 ViT-G/16	24
`vjepa2_ac_oss`	DROID & RoboCasa	256×256	V-JEPA-2 ViT-G/16	24

VM2M Decoder Heads

Model	Encoder	Resolution
`dinov2_vits_224`	DINOv2 ViT-S/14	224×224
`dinov2_vits_224_INet`	DINOv2 ViT-S/14	224×224
`dinov3_vitl_256_INet`	DINOv3 ViT-L/16	256×256
`vjepa2_vitg_256_INet`	V-JEPA-2 ViT-G/16	256×256

Usage

Via PyTorch Hub (Recommended)

python

import torch

# Load JEPA-WM models
model, preprocessor = torch.hub.load('facebookresearch/jepa-wms', 'jepa_wm_droid')
model, preprocessor = torch.hub.load('facebookresearch/jepa-wms', 'jepa_wm_metaworld')

# Load DINO-WM baselines
model, preprocessor = torch.hub.load('facebookresearch/jepa-wms', 'dino_wm_metaworld')

# Load V-JEPA-2-AC baseline
model, preprocessor = torch.hub.load('facebookresearch/jepa-wms', 'vjepa2_ac_droid')

Via Hugging Face Hub