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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f2b588234d8b04727c031a9ceaa52900e25c659ddf32ec62de2934b55962215
- Size of remote file:
- 789 kB
- SHA256:
- c451c54f714986c54bb6983a23155c25576fcd4ee154a164f709d58b06c4bdd2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.