Instructions to use wrice/swin2sr-ffhq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wrice/swin2sr-ffhq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-to-image", model="wrice/swin2sr-ffhq")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageToImage processor = AutoImageProcessor.from_pretrained("wrice/swin2sr-ffhq") model = AutoModelForImageToImage.from_pretrained("wrice/swin2sr-ffhq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 46b3a5511f2e73bab52007474dcbde1fb7201da03866c41d7da0e8763c27a254
- Size of remote file:
- 48.3 MB
- SHA256:
- 5e36fc30db741428e040ec58af07062722d44c2339439bee04340baa7583ec4a
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