Instructions to use caidas/swin2SR-lightweight-x2-64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use caidas/swin2SR-lightweight-x2-64 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-to-image", model="caidas/swin2SR-lightweight-x2-64")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageToImage processor = AutoImageProcessor.from_pretrained("caidas/swin2SR-lightweight-x2-64") model = AutoModelForImageToImage.from_pretrained("caidas/swin2SR-lightweight-x2-64") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
tags:
- vision
- image-to-image
inference: false
Swin2SR model (image super-resolution)
Swin2SR model that upscales images x2. It was introduced in the paper Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration by Conde et al. and first released in this repository.
Intended use cases
This model is intended for lightweight image super resolution.
Usage
Refer to the documentation.