Instructions to use hf-internal-testing/tiny-random-Swin2SRModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Swin2SRModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-internal-testing/tiny-random-Swin2SRModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-Swin2SRModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-Swin2SRModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from hf-internal-testing/tiny-random-Swin2SRModel: direct link, hf CLI and curl.
- Browser
- Download file 185 Bytes
-
https://huggingface.co/hf-internal-testing/tiny-random-Swin2SRModel/resolve/c67f6ecff9ef8675c3869c987277b0a1e040f4be/preprocessor_config.json
- Command line
-
hf download hf://hf-internal-testing/tiny-random-Swin2SRModel@c67f6ecff9ef8675c3869c987277b0a1e040f4be/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/hf-internal-testing/tiny-random-Swin2SRModel/resolve/c67f6ecff9ef8675c3869c987277b0a1e040f4be/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 | |
| } | |