[tonic_section]

Hey, thanks for bringing the brightness issue to my attention - turns out I wasn't normalizing the output correctly - I just pushed a fix and the output images don't have the brightness change now.

As for the random spots, that's an artifact of the entropy coding algorithm. In principle this is lossless but there is some distortion because I'm using a custom vectorized version of an rANS encoder and it's hard to encode overflow values in a vectorized fashion, I'm working on this though. If you can live with really slow decoding times (2-3mins) then you can disable vectorization to eliminate these small imperfections entirely.

As for the comparison to the official model, that's mainly because of compute constraints v. Google (this is just my weekend project). My model uses a smaller architecture and was trained for only 4e5 steps versus the 2e6 steps they reported in the paper - even then it took 4+ days on AWS! The model is also trained on the Openimages dataset, which is presumably much smaller and more noisy than the massive internal dataset Google used.

[fireattack]

Just curious, is the change on the model side? Since I didn't see much relevant in the notebook's rev history [1].

[1] https://colab.research.google.com/github/Justin-Tan/high-fid...

[fireattack]

Thank you!