Instructions to use jadechoghari/vfusion3d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jadechoghari/vfusion3d with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jadechoghari/vfusion3d", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from jadechoghari/vfusion3d: direct link, hf CLI and curl.
- Browser
- Download file 385 Bytes
-
https://huggingface.co/jadechoghari/vfusion3d/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://jadechoghari/vfusion3d/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/jadechoghari/vfusion3d/resolve/main/preprocessor_config.json
385 Bytes
| { | |
| "_name_or_path": "jadechoghari/vfusion3d", | |
| "source_size": 512, | |
| "use_rembg": true, | |
| "rembg_model": "isnet-general-use", | |
| "border_ratio": 0.20, | |
| "image_mean": [0.5, 0.5, 0.5], | |
| "image_std": [0.5, 0.5, 0.5], | |
| "_class_name": "LRMImageProcessor", | |
| "processor_class": "processor.LRMImageProcessor", | |
| "auto_map": { | |
| "AutoImageProcessor": "processor.LRMImageProcessor" | |
| } | |
| } | |