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
