I’m thrilled to share that I’ll present two first-authored papers at #ICCV2025 🌺 in Honolulu together with @mgui7.bsky.social ! 🏝️ (Thread 🧵👇)

🤔 What if you could generate an entire image using just one continuous token? 💡 It works if we leverage a self-supervised representation! Meet RepTok🦎: A generative model that encodes an image into a single continuous latent while keeping realism and semantics. 🧵 👇

Looking forward to attending #CVPR2025 in Nashville next week 🎸🎶 @mgui7.bsky.social and I will be presenting our latest work: 🌊 Diff2Flow: Training Flow Matching Models via Diffusion Model Alignment

🤔When combining Vision-language models (VLMs) with Large language models (LLMs), do VLMs benefit from additional genuine semantics or artificial augmentations of the text for downstream tasks? 🤨Interested? Check out our latest work at #AAAI25: 💻Code and 📝Paper at: github.com/CompVis/DisCLIP 🧵👇

🤔 Why do we extract diffusion features from noisy images? Isn’t that destroying information? Yes, it is - but we found a way to do better. 🚀 Here’s how we unlock better features, no noise, no hassle. 📝 Project Page: compvis.github.io/cleandift 💻 Code: github.com/CompVis/clea... 🧵👇

Amazing blog post on flow matching, stunning visuals! It also makes the connection with normalising flows crystal clear. Incredible effort!

Mathurin Massias@mathurinmassias.bsky.social · 2y ago

Anne Gagneux, Ségolène Martin, @quentinbertrand.bsky.social Remi Emonet and I wrote a tutorial blog post on flow matching: dl.heeere.com/conditional-... with lots of illustrations and intuition! We got this idea after their cool work on improving Plug and Play with FM: arxiv.org/abs/2410.02423