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
Add free_dimension_overrides in config.json
#62
by ibelem - opened
Based on https://github.com/huggingface/transformers.js/blob/main/src/models.js#L239 , WebNN requires free_dimension_overrides to be set in config.json as a field within "transformers.js_config".
Based on https://github.com/huggingface/transformers.js/pull/1276 and further discussions, will prioritize some of the more popular models, and make automated PRs for models which ordinarily only support static shapes. Close this PR.
ibelem changed pull request status to closed