Neuralcompression

* `HiFiCEncoder`
* `HiFiCDiscriminator`
* `HiFiCGenerator`

Variational Image Compression with a Scale Hyperprior


Variational Image Compression with a Scale Hyperprior
Johannes Ballé, David Minnen, Saurabh Singh, Sung Jin Hwang, Nick Johnston

* `HyperpriorAutoencoder`: base class for implementing hyperprior autoencoder architectures.
* `MeanScaleHyperpriorAutoencoder`
* `ScaleHyperpriorAutoencoder`

API Changes

* `neuralcompression.functional.hsv2rgb` is now `neuralcompression.functional.hsv_to_rgb`.
* neuralcompression.functional.learned_perceptual_image_patch_similarity is now `neuralcompression.functional.lpips`.

Acknowledgements

Thank you to the following people for their advice:

* Johannes Ballé (jonycgn) and [TensorFlow Compression](https://tensorflow.github.io/compression/)
* Jean Bégaint (jbegaint) and [Compress AI](https://interdigitalinc.github.io/CompressAI/)
* Fabien Racapé (fracape) and [Compress AI](https://interdigitalinc.github.io/CompressAI/)
* Justin Tan (Justin-Tan) and [high-fidelity-generative-compression](https://github.com/Justin-Tan/high-fidelity-generative-compression)

* `PriorAutoencoder`: base class for implementing prior autoencoder architectures.
* `FactorizedPriorAutoencoder`

High-Fidelity Generative Image Compression


High-Fidelity Generative Image Compression
Fabian Mentzer, George Toderici, Michael Tschannen, Eirikur Agustsson

- Fixes a typo for using pretrained model weights (PR 224)
- Fixes the `scipy` requirement to prevent errors in FID calculation (PR 226)

This release is paired with the [MS-ILLM open-source implementation](https://github.com/facebookresearch/NeuralCompression/tree/main/projects/illm) and includes utilities for generative compression, focusing on autoencoders with adversarial training.

List of updates:
- Removed models redundant with CompressAI and added HiFiC autoencoder model (PR 196)
- Metrics refactor to new `metrics` module, adding HiFiC FID/256 implementation, SwAV-based FID, and DISTS metric (PR 197)
- Datasets implementation for OpenImages, DIV2K, plus testing improvements (PR 198)
- Loss functions for non-saturating GAN, OASIS, MSE, and MSE-LPIPS (PR 199)
- Creation of model zoo and MS-ILLM model weights open-sourcing (PR 211)
- MS-ILLM training framework (PRs 200, 210, 216, 220)
- Repository build updates (PRs 208, 207, 214, 221)
- Bug fix for DVC decompress function (PR 202)
- Bug fix for optical flow color (PR 203)
- Apple Silicon support tail estimation (PR 205)

Contributors: N1ghtstalker2022, txya900619, NripeshN mmuckley

As documented in 188, we are going to work on deduplicating functionality in NeuralCompression from CompressAI. CompressAI is the standard package for neural compression research in PyTorch, and in general we should focus on maintaining only functionality that CompressAI doesn't already have. This release is intended to be a reference release so that any users wishing to continue using the current functionality of NeuralCompression can continue to do so by pinning the version to 0.2.2.