Instructions to use hf-internal-testing/tiny-random-Swin2SRForImageSuperResolution with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Swin2SRForImageSuperResolution with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-to-image", model="hf-internal-testing/tiny-random-Swin2SRForImageSuperResolution")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageToImage processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-Swin2SRForImageSuperResolution") model = AutoModelForImageToImage.from_pretrained("hf-internal-testing/tiny-random-Swin2SRForImageSuperResolution") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 320a6ee7953b38547c7e05d4b139180a4c486cafce3bad8c6d80c434dbe9176a
- Size of remote file:
- 767 kB
- SHA256:
- 3c31bab178f59fa4583f5642f6b485042943298feb7b4e6e7598ea58757b02bf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.