Instructions to use fusing/ddpm-cifar10-ema with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fusing/ddpm-cifar10-ema with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("fusing/ddpm-cifar10-ema", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a2cb50fa9669325f5046342d4dc4cbd0f00c8536b17e4523fded3a32508cdba6
- Size of remote file:
- 143 MB
- SHA256:
- 8cac436859f61de928c198ca4dad08d08a9f266faa72b15dc5ae222cabe47b6e
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