المفاهيم الأساسية
Stale Diffusion is a method that solidifies and ossifies Stable Diffusion in a maximum-entropy state to generate sleep-inducing hyper-realistic 5D video.
الملخص
The authors propose Stale Diffusion, a method that builds upon the Stable Diffusion model to generate hyper-realistic 5D video content. The key highlights are:
Stale Diffusion starts from a maximum-entropy distribution (the uniform distribution) and implements a reverse diffusion process to recover samples from the original data distribution.
The authors claim that proving the limiting case of infinite iterations brings Stale Diffusion in line with the compute requirements of today's state-of-the-art methods.
The architecture uses a Transformer that automatically alternates between vehicular and anthropomorphic forms based on the needs of the generated video plot.
The training regime applies the cr-hinge loss to large collections of TikTok videos with a crying-joy emoji token appended to each input.
The authors showcase several example 5D movie-quality videos generated by their Stale Diffusion method, noting that the stills may or may not be identical to photos from IMDb.
The authors acknowledge limitations of their work, such as only applying to the 5 standard human senses and leaving out the "other one that allows you to see Bruce Willis."
Future work includes extending Stale Diffusion to more than 5 dimensions and experimenting with various techniques like mess-up regularization, arctangent learning rate schedules, and train-test contamination.
اقتباسات
"Leave the GAN. Take the cannoli."
"Uniforms are all you need"