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I wrote an interactive article explaining the geometric intuition behind Rectified Flows. I visually explain why flow-models tend to learn curved trajectories, why this is bad for sampling latency, and a relatively simple technique for mitigating it. Check it out! Link 👇

159,395 views • 8 months ago •via X (Twitter)

20 Comments

Alec Helbling's profile picture
Alec Helbling8 months ago

Most of the visualizations have interactive elements that work by running an actual flow model on the front end using Tensorflow.js. It should even work on most mobile devices. Link to blog:

Alec Helbling's profile picture
Alec Helbling8 months ago

This project is a part of a broader effort to explain and visualize flow-based generative models with interactive visualization. Check out the code here:

Vaitkus Márton's profile picture
Vaitkus Márton8 months ago

Very nice, but IIRC in very high dimensions the paths straighten out quite a lot even with vanilla flow matching, so these 2D examplrs can be a bit misleading for realistic generative models... I even remember some papers that gave some theoretical explanation for this.

Ananya Patelik's profile picture
Ananya Patelik8 months ago

love this! curved flows always felt like data taking the scenic route. straightening them shaved 30% off my sampling time. visuals were super clear, thanks!

Sebastian Buzdugan's profile picture
Sebastian Buzdugan8 months ago

those curved trajectories always wreck sampling latency

Thirdpen's profile picture
Thirdpen8 months ago

sweet 😋

Carmelo schepis's profile picture
Carmelo schepis8 months ago

crazy

R's profile picture
R8 months ago

Super cool thanks for sharing!

Zorkimer's profile picture
Zorkimer8 months ago

I am basically reading this the same way the chimps investigate the obelisk at the start of 2001: A Space Odyssey but it's a beautiful document.

Jason Q.L. Williams's profile picture
Jason Q.L. Williams8 months ago

Beautiful, bro

Kelvin 🦖🤓's profile picture
Kelvin 🦖🤓8 months ago

🔥❤️

Coyote's profile picture
Coyote8 months ago

What clicked for me is that this isn’t just about faster sampling... it’s about constraint geometry!! Curvature is a symptom of mismatch between learned flow and data manifold. Rectification feels like enforcing the right geometry, not optimizing around the wrong one.

Fabián Souto's profile picture
Fabián Souto8 months ago

Pretty awesome work, Alec! Loved how you explained a complex topic in such a simple way. Really good!

Tarandeep Singh's profile picture
Tarandeep Singh8 months ago

Cool Stuff, lot of people are so busy trying to keep up with new models and techniques coming out everyday sometimes we forget to actually absorb the material, the true underlying principles underneath.

Saurabh Shukla's profile picture
Saurabh Shukla8 months ago

Very cool post, the visualizations are great.

Alec Helbling's profile picture
Alec Helbling8 months ago

Thank you!

WaterLyst's profile picture
WaterLyst7 months ago

Nice! That sounds super insightful and useful for anyone diving into flow models.#waterlystfeb2

Himanshu Kumar's profile picture
Himanshu Kumar8 months ago

@alec_helbling, your explanation of rectified flows will help many understand this complex topic better.

Rahul Raghav's profile picture
Rahul Raghav8 months ago

curious how this compares to normalizing flows in other domains

👨‍💻 James Augeri, PhD's profile picture
👨‍💻 James Augeri, PhD8 months ago

interesting / arXiv also?

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