Flow Matching
topic on 1 show · 3 statements across 2 episodes
3 statements about Flow Matching, every show
Reddy: Mistral reduces flow-matching audio inference to 16 steps
“When you have a depth transformer, if you have K tokens, you need to do K autoregressive steps, right? Even though it's a small thing, it's like K steps, which is very latency heavy with flow matching. We were able to cut it down significantly, so we are able …”
Reddy: Continuous flow matching outperforms discrete audio tokens for speech generation
“So the thing we did differently is instead of having this autoregressive K step prediction, we have a flow matching model. Instead of modeling this as a discrete token set, we trained the codec to be both discrete and continuous to have this flexibility. So we…”
Inconsistent Diffusion Trajectories Drive the Need for Consistency Models
“The locations that you're learning are not basically on the same in the, in this latent space. They're not in the same trajectory
As each other, right? So they like and this causes a lot of inefficiency, and that's sort of the whole point to this, the, these …”