Instructions to use DiffusionWave/sam3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DiffusionWave/sam3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="DiffusionWave/sam3")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DiffusionWave/sam3") model = AutoModel.from_pretrained("DiffusionWave/sam3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1ebb91a10cf694d5b65a18dda24bac6445f6e74ecae2809f9c19df49b58fed03
- Size of remote file:
- 3.44 GB
- SHA256:
- 6d06f0a5f84e435071fe6603e61d0b4cc7b40e0d39d487cfd4d67d8cc11cc14a
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