Instructions to use BAAI/Emu3-VisionTokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BAAI/Emu3-VisionTokenizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BAAI/Emu3-VisionTokenizer", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BAAI/Emu3-VisionTokenizer", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "auto_map": { | |
| "AutoImageProcessor": "image_processing_emu3visionvq.Emu3VisionVQImageProcessor" | |
| }, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "Emu3VisionVQImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "max_pixels": 1048576, | |
| "min_pixels": 262144, | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "max_pixels": 1048576, | |
| "min_pixels": 262144 | |
| }, | |
| "spatial_factor": 8 | |
| } | |