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 preprocessor_config.json from hf-internal-testing/tiny-random-Swin2SRForImageSuperResolution: direct link, hf CLI and curl.
- Browser
- Download file 185 Bytes
-
https://huggingface.co/hf-internal-testing/tiny-random-Swin2SRForImageSuperResolution/resolve/6eb6f4d3a6167dfd8d7bf5ed9f4227fa334a7c4c/preprocessor_config.json
- Command line
-
hf download hf://hf-internal-testing/tiny-random-Swin2SRForImageSuperResolution@6eb6f4d3a6167dfd8d7bf5ed9f4227fa334a7c4c/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/hf-internal-testing/tiny-random-Swin2SRForImageSuperResolution/resolve/6eb6f4d3a6167dfd8d7bf5ed9f4227fa334a7c4c/preprocessor_config.json
185 Bytes
| { | |
| "crop_size": 32, | |
| "do_pad": true, | |
| "do_rescale": true, | |
| "image_processor_type": "Swin2SRImageProcessor", | |
| "pad_size": 8, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": 32 | |
| } | |