Buckets:
| import sys | |
| import os | |
| from pathlib import Path | |
| from huggingface_hub import snapshot_download | |
| def load_badas_model(device="cuda"): | |
| print("Downloading BADAS model...") | |
| repo_path = snapshot_download(repo_id="nexar-ai/nexight", allow_patterns=["src/*", "weights/*"]) | |
| src_path = Path(repo_path) / "src" | |
| sys.path.insert(0, str(src_path)) | |
| from models.vjepa import VJEPAModel | |
| checkpoint_path = Path(repo_path) / "weights" / "badas_open.pth" | |
| model = VJEPAModel( | |
| model_name="facebook/vjepa2-vitl-fpc16-256-ssv2", | |
| checkpoint_path=str(checkpoint_path), | |
| frame_count=16, | |
| img_size=224, | |
| window_stride=1, | |
| target_fps=8.0, | |
| use_sliding_window=True, | |
| ) | |
| model.load() | |
| return model | |
Xet Storage Details
- Size:
- 768 Bytes
- Xet hash:
- b89e72dba128da4338cd28f1f528f5364053cfe88a2af717e6d046f6f592f90b
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.