This is a diffusion network that uses both a LQ image
and a reference sample HQ image that is compressed into
a latent vector to perform upsampling
The hope is that we can steer the upsampling network
with sample images.
The intuition is this will help guide the network to make better informed decisions
about how it performs upsampling based on how it perceives the underlying content.
(I'm giving up on letting networks detect their own quality - I'm not convinced it is
actually feasible)
Adds support for GD models, courtesy of some maths from openai.
Also:
- Fixes requirement for eval{} even when it isn't being used
- Adds support for denormalizing an imagenet norm
- Allow image_folder_dataset to normalize inbound images
- ExtensibleTrainer can denormalize images on the output path
- Support .webp - an output from LSUN
- Support logistic GAN divergence loss
- Support stylegan2 TF weight extraction for discriminator
- New injector that produces latent noise (with separated paths)
- Modify FID evaluator to be operable with rosinality-style GANs
For massive upscales (ex: 8x), corruption does almost nothing when applied
at the HQ level. This patch adds support to perform corruption at a specified
intermediary scale. The dataset downscales to this level, performs the corruption,
then downscales the rest of the way to get the LQ image.
