Instructions to use bytedance-research/UNO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bytedance-research/UNO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-to-image", model="bytedance-research/UNO")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bytedance-research/UNO", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cdf7b6a6aed1006b8f400d2e15cd371b20cd238372ee311d51f9a0a52edabde7
- Size of remote file:
- 1.91 GB
- SHA256:
- c166d6ca1084c5c37e74dec383a6504f5add5269a3b6c9d275e810dd77113552
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