Research scientist @AIatMeta (FAIR), prev @WeizmannScience. Interested in generative models and deep learning of irregular/geometric data.
Israel
Joined August 2014
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A new (and comprehensive) Flow Matching guide and codebase released! Join us tomorrow at 9:30AM @NeurIPSConf for the FM tutorial to hear more...
arxiv.org/abs/2412.06264 github.com/facebookresearch/…
Yaron Lipman retweeted
Adaptive tokenization is becoming an essential tool for making visual generation more efficient. In our new work, Level-of-Token (LoT) Diffusion, we allocate tokens based on where details are.
Diffusion models spend the same compute on a blank wall as on a face. But you often know in advance where the detail will be.
Introducing Level-of-Token (LoT) Diffusion: we turn that knowledge into a multiresolution token layout, with fine tokens where detail is needed and coarse tokens elsewhere.
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Yaron Lipman retweeted
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
Yaron Lipman retweeted
Flow Matching obtains its training supervising velocity by conditioning on data samples. But what if you don’t have a dataset, only an unnormalized density? Flow Sampling gets the supervising drift by conditioning on the source instead of the target.
Catch @aaronjhavens at #ICML
Excited to be at ICML to present our Spotlight paper: Flow Sampling!
We propose a simple fixed-point objective for learning diffusion samplers, built on the flow matching marginal construction. More details to come soon!
Paper: arxiv.org/abs/2605.03984
Yaron Lipman retweeted
The alpha version of my new book "Optimal Transport
for Machine Learners" is out, with in particular an online version with interactive figures
gpeyre.com/ot4ml/
Yaron Lipman retweeted
Another day another demo 🤓
Today we release the code and a demo for our recent Spectral Progressive Diffusion paper🎉
Play around with it anytime! Just as what we have been doing also, we hope that it encourages the integration of our plug-and-play framework into latest and greatest image and video generation models!!
We also included an agent skill wrapper in the repo, to making things easier.
🛜Project website: howardxiao.ca/speed/
📄Paper: arxiv.org/abs/2605.18736
💻Github (with ComfyUI): github.com/howardhx/speed
🤗Demo (HuggingFace): huggingface.co/spaces/howard…
Yaron Lipman retweeted
Arriving in Rio 🇧🇷for #ICLR! I will present GLASS Flows + Diamond Maps in the coming days - starting today with GLASS Flows Oral at 11:30 AM! I am looking forward to meeting new people! Feel free to reach out!
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We release Diamond Maps💎 unlocking accurate and efficient guidance for diffusion models. Our experiments show that our methods scale incredibly well. Excited to see what people will build with this!
Accurate guidance has been a notoriously hard problem, but in this work, we’re bringing TWO (!) solutions to the table. The recipe for success:
1️⃣ Speed: Use distilled models (flow maps, mean flows, consistency models).
2️⃣ Exploration: Inject stochasticity to properly explore your search space.
Because this fundamentally improves anything using flow matching and diffusion, we see a lot of potential for applications across audio, robotics, molecules, and beyond.
Paper: arxiv.org/abs/2602.05993
Code: github.com/PeterHolderrieth/…
Huge thanks to an amazing team: Douglas Chen, @LucaEyring, @ishin_shah, Giri Anantharaman, @electronickale, @zeynepakata, Tommi Jaakkola, @nmboffi, and @max_simchowitz. It was awesome bringing this to life together!
Yaron Lipman retweeted
Why pay full compute for pixels you're not even looking at?
In our new work, Foveated Diffusion, we introduce a new concept for efficient image and video generation, motivated by how the human visual system works.
(See full thread below)
High-resolution image and video generation is hitting a wall because attention in DiTs scales quadratically with token count. But does every pixel need to be in full resolution?
Introducing Foveated Diffusion: a new approach for efficient diffusion-based generation that allocates compute where it matters most.
1/7🧵
Yaron Lipman retweeted
🚀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
Yaron Lipman retweeted
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New at #ICLR2026!!! 🎉
Exploring the Design Space of Transition Matching 📄
arxiv.org/abs/2512.12465
Yaron Lipman retweeted
🚀🎬We introduce TMD (Transition Matching Distillation): 480p videos generated from text prompts in < 3 NFEs!
1️⃣Main backbone for feature extraction and lightweight head for iterative refinement
2️⃣Distilled from Wan2.1 14B T2V combining MeanFlow & DMD2
🔗research.nvidia.com/labs/gen…
Yaron Lipman retweeted
After multiple requests for the code of the visuals from my talk about Transition Matching, I made a notebook that reproduces the DTM vs. FM GIF!
This demo is a good way to build intuition on how TM and FM differ.
github.com/neta93/visual-ill…
@urielsinger
Come work with us!
We are looking for a 24‑week intern at FAIR‑MSL to collaborate on fast LLM sampling with @lipmanya, @helibenhamu, and me. Interested? Reach out to any of us or apply via Meta Careers (link below).
Yaron Lipman retweeted
We are looking for a 24‑week intern at FAIR‑MSL to collaborate on fast LLM sampling with @lipmanya, @helibenhamu, and me. Interested? Reach out to any of us or apply via Meta Careers (link below).
Yaron Lipman retweeted
#NeurIPS2025 "Transition Matching" explores generative Markov processes with expressive transition kernels, going beyond the Gaussian kernel used in diffusion and flow models.
Interested? Let's chat! 📍 Poster #3609 🕒 Wed at 11am - 2pm
📄 arxiv.org/abs/2506.23589
Yaron Lipman retweeted
Incredible atmosphere at the poster session today! Thanks to everyone who visited 🙌
I’ll be at #NeurIPS2025 until Dec 8. If you missed the poster or just want to chat, my DMs are open.
Shoutout to @EliahuHorwitz for the pictures!
#NeurIPS2025 "Transition Matching" explores generative Markov processes with expressive transition kernels, going beyond the Gaussian kernel used in diffusion and flow models.
Interested? Let's chat! 📍 Poster #3609 🕒 Wed at 11am - 2pm
📄 arxiv.org/abs/2506.23589
Yaron Lipman retweeted
Come do a PhD with me 😀! Promise of fun science and great coffee ☕
New work led by @peholderrieth showing how to transform an already trained flow matching model to a stochastic transition/posterior model that can still be sampled via an efficient ODE solver!
New work: “GLASS Flows: Transition Sampling for Alignment of Flow and Diffusion Models”. GLASS generates images by sampling stochastic Markov transitions with ODEs - allowing us to boost text-image alignment for large-scale models at inference time!
arxiv.org/pdf/2509.25170
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Yaron Lipman retweeted
New work: “GLASS Flows: Transition Sampling for Alignment of Flow and Diffusion Models”. GLASS generates images by sampling stochastic Markov transitions with ODEs - allowing us to boost text-image alignment for large-scale models at inference time!
arxiv.org/pdf/2509.25170
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