Researchers have developed new algorithms for sampling and diffusion models. Flow-matching, a technique similar to diffusion, simplifies the process by predicting the velocity of trajectories in noised images. It involves linear interpolations between data and noise pairs. The approach aims to enhance denoising diffusion.
Flow Matching in action https://t.co/fNOUf7VilT https://t.co/x9zF6rb9rb
Diffusion Model With Optimal Covariance Matching. https://t.co/rBzx143S7b
Flow Matching is SOOOO simple GG denoising diffusion? https://t.co/fdArTRk9Z1
Flow-matching implementation: https://t.co/sP5DXLr4jI Flow-matching is very similar to diffusion, but simplifies things. Noised images are linear interpolations between (data, noise) pairs, and the network predicts *velocity* of this trajectory. https://t.co/KHsxhPJvV6
New algorithms for sampling and diffusion models. https://t.co/tpXRJPzS72