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 # Warning: Pipeline type "image-to-image" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("image-to-image", model="hf-internal-testing/tiny-random-Swin2SRForImageSuperResolution")# pip install -U transformers accelerate # 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
Download pytorch_model.bin from hf-internal-testing/tiny-random-Swin2SRForImageSuperResolution: direct link, hf CLI and curl.
- Browser
- Download file 793 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-Swin2SRForImageSuperResolution/resolve/refs%2Fpr%2F3/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-Swin2SRForImageSuperResolution@refs/pr/3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-Swin2SRForImageSuperResolution/resolve/refs%2Fpr%2F3/pytorch_model.bin
793 kB
- Xet hash:
- 1c15b01144ff234c6e6a6fe14a0a48dac061d86cd3b85808933d8f9831bbdf2b
- Size of remote file:
- 793 kB
- SHA256:
- e84ac55d7a7d461d8154b09f5071da0c928ea54e96c7613b791f83159067d602
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