Insight
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 …”
Assertion Supported
Major Image Models Have Not Yet Adopted Consistency Models
“None of this technology that we're discussing today is in any of really in any of the big models that we know and love with maybe of maybe with the exception of flux.”
Opinion
Isolating Tangent Function Instability is OpenAI's Core Contribution in sCM
“And this is, I, in my opinion, the meat of the paper. So you have this, part of the, you have this tangent function that I had called out.”
Insight
GANs Outperform Diffusion on Benchmarks but Are Abandoned for Mode-Seeking
“The reason why people don't use them is because they're hard to train and they're, they have, they're very, they have mode seeking behavior, meaning it's hard to get any diversity and hard to control them. But for these benchmarks, they do the best.”
Assertion Supported
Consistency Models Generate High-Quality Images in a Single Pass
“With the regular diffusion models that we all know and love, they have to iterate multiple times generally to generate a good image. Whereas The, these consistency models are designed so they can generate a good image with only one pass through the network.”
Assertion Supported
Cosine Noise Schedules Eliminate Wasted Backward Steps in Diffusion Models
“They found in this paper that it's actually inefficient, that you end up
with a lot of wasted a way wasted backwards process backwards diffusion steps that you don't need and you can cut out a whole bunch of it just by using a cosine schedule.”
Insight
Consistency Models Map Any Trajectory Point Directly to Original Data
“What a consistency model does is it says that everything should be on the same trajectory, right? So I'm gonna, if I estimate it, I can I'm gonna learn how to map from any point on this trajectory to the to this point in the data.”
Assertion Supported
Stabilization Techniques Help Continuous Consistency Models Outperform Discrete Models
“And so like when you stack all of these things together, then you're able to train much more effectively and continuous time does much better. Then these discrete, this n is the number of discrete steps that your model is taking, and, you know, maybe one inter…”
Assertion Contradicted
Distilling sCM Requires Roughly Twice the Compute of Teacher Training
“One thing that they said in the paper, it's not here, but that that it, they, it takes about two X to compute to train
the
This consistency model from as a as a distillation of whatever they distilled from. So approximately twice the compute.”
Assertion Supported
Consistency Models Still Underperform Diffusion Teacher Models on Standard Benchmarks
“The distillation doesn't do quite as well as the training. And, but neither of them do as well as the diffusion teacher. Including the one that was trained from scratch.”
Assertion Supported
Probability Flow ODE Traces Maximum Likelihood Path Deterministically in Diffusion
“And then this probability flow ODE is sort of a deterministic version that looks at what is the maximum likelihood path If I started at that trajectory, right?”
Insight
Large Step Sizes in Discrete ODE Solvers Cause Trajectory Errors
“So if this Delta T here is very big, you see it goes, like, far from XT to X minus Delta T, then the error that It can have is very big and that can put it on a different trajectory. So you get the wrong trajectory.”
