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Peter Holderrieth

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@peholderrieth

CS PhD student at @MIT • Prev: Stats/Neuro @OxfordUni• Math at @UniBonn • Former: @NVIDIA, @AIatMeta
peterholderrieth.com Joined July 2022
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Peter Holderrieth Verified account @peholderrieth
🚀MIT Flow Matching and Diffusion Lecture 2026 Released (diffusion.csail.mit.edu)!

We just released our new MIT 2026 course on flow matching and diffusion models! We teach the full stack of modern AI image, video, protein generators - theory and practice. We include:
📺 Videos: Step-by-step derivations.
📝 Notes: Mathematically self-contained lecture notes
💻 Coding: Hands-on exercises for every component

We fully improved last years’ iteration and added new topics: latent spaces, diffusion transformers, building language models with discrete diffusion models.

Everything is available here: diffusion.csail.mit.edu

A huge thanks to Tommi Jaakkola for his support in making this class possible and Ashay Athalye (MIT SOUL) for the incredible production! Was fun to do this with @RShprints!

#MachineLearning #GenerativeAI #MIT #DiffusionModels #AI
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Peter Holderrieth Verified account @peholderrieth
Very elegant distillation method for flow models! This works just out-of-the-box and is both simple and scalable!
Neta Shaul @shaulneta Jul 29
1/ New paper: Parallel Decoding Distillation (PDD)!🚀

To accelerate diffusion/flow models, trajectory distillation usually merges consecutive ODE steps into 1 larger step. PDD changes this by predicting MULTIPLE ODE steps in 1 network eval.🧵

w/ @julberner @ArashVahdat Chao Liu
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Peter Holderrieth Verified account @peholderrieth
Check out our DeCAF model! We and many others promoted for a while that inference-time scaling with flow maps is the way to go. It is super cool to see this working on protein-ligand co-folding models. Excited to see what these ideas will unlock for proteins in the future!
Joey Bose Verified account @bose_joey Jun 10
Protein–ligand cofolding models are getting incredibly powerful… but do they have to be so slow? 🧬🐢💊

Our new preprint introduces a new flow-map framework called DeCAF for fast few-step cofolding — up to 5× faster while preserving sample quality on the SOTA Pearl model and 20x faster than Boltz 1x. ⚡🧵

📜 Blog: genesis.ml/news/genesis-m…
🔗arXiv: arxiv.org/abs/2606.08375
Code (coming soon): github.com/genesistherape…
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Peter Holderrieth Verified account @peholderrieth
Wonderful tutorial by @ArashVahdat, @julberner, and @wn8_nie about making diffusion go fast, e.g. enabling cool applications like interactive world models!
Julius Berner Verified account @julberner Jun 9
From theory to interactive world models ⚡️

The slides for our #CVPR2026 tutorial on accelerated diffusion models are online now!

Topics covered:
1️⃣ General acceleration paradigms (@ArashVahdat)
2️⃣ Step-Distillation (@julberner)
3️⃣ Interactive World Models (@wn8_nie)

Check out the slides on our website cvpr26-tutorial-fastgen.github.io or in the thread below!🧵
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