Yesterday I watched a movie about an AI4Science researcher who gets angry after having his research grant application rejected (badly explained movie plot). Maybe we should take it as a warning about research funding cuts

NeurIPS submissions confirmed to be a heat-loving species. Warmer year, bigger bloom. Every degree we add, the deadline gets denser 🌻 Good news for the field, we're having a really good growing season 👨‍🌾

We show that increasing the alignment score at inference time improves source fidelity, leading to generated images that better preserve the source content, even when the model is trained on image pairs with low/medium/high alignment (see figure).

The key idea is to condition the bridge on image-pair alignment, providing a simple and effective way to improve source fidelity at inference time. We explore both metadata-based alignment and embedding-based alignment scores, enabling our method to be applied even when metadata are unavailable

The last project of my PhD is finally out! 🪴 It was a pleasure collaborating with Aimi on this work! We introduce A²BM: Alignment-Aware Bridge Matching, a new framework for image-to-image translation with weakly aligned image pairs. Paper 📄: arxiv.org/pdf/2607.16294

Heading to #ICML2026 🇰🇷 and interested in diffusion models, flow matching, and their generalization capabilities? Don't miss the excellent tutorial by @mathurinmassias.bsky.social and @quentinbertrand.bsky.social on Monday! 📍 Hall D1 🗓️ Monday, July 6 🕘 9:00–11:30 AM Details: icml.cc/virtual/2026...

My PhD thesis manuscript will be available in the coming months, but I’ve written two blog posts based on the related work chapter: 1. Generative modeling with flow-based models 🪚 2. Data-translation with flow and diffusion bridges 🔨 Open to feedback and discussions! lebellig.github.io/blog/

Delighted to have successfully defended my PhD thesis on "Generative models for Earth Observation, from denoising to domain adaptation" 🪴 This wouldn’t have been possible without the support of my colleagues, my PhD advisor, and my family and friends. Thank you all!

Self-Supervised Flow Matching for Scalable Multi-Modal Synthesis by Hila Chefer et al. (arxiv.org/abs/2603.06507). New SSL loss for flow matching that encourages meaningful representation learning without relying on an external visual encoder for alignment. Improves generation on many modalities.

The Spacetime of diffusion models: an information geometry perspective by Rafał Karczewski et al. (arxiv.org/abs/2505.17517) blog: rafalkarczewski.github.io/blog/2026/di... Geodesics in the (xt, t) spacetime of diffusion models -> new distance between clean data points + transition path sampling!!

Meta Flow Maps enable scalable reward alignment, Peter Potaptchik et al. (arxiv.org/abs/2601.14430) This article introduces Meta Flow Maps: a stochastic generalization of consistency models (one-step generation) that allows efficient reward steering at inference time or during fine-tuning.

I use drum mic kits for punchier presentations

Calling it for today... I tried using the gemini 3 Pro preview to build some js animations, and it went well

Sorry but it's getting pretty when leaving python

Interpolation between two gaussian distributions on a flat torus (my personal benchmark for new llms)

"Back to Basics: Let Denoising Generative Models Denoise" by Tianhong Li & Kaiming He arxiv.org/abs/2511.13720 Diffusion models in pixel-space, without VAE, with clean image prediction = nice generation results. Not a new framework but a nice exploration of the design space of the diffusion models.

"Curly Flow Matching for Learning Non-gradient Field Dynamics" @kpetrovvic.bsky.social et al. arxiv.org/pdf/2510.26645 Solving the Schrödinger bridge pb with a non-zero drift ref. process: learn curved interpolants, apply minibatch OT with the induced metric, learn the mixture of diffusion bridges.

Great article! But can the preference score go up to 2? You know, because 1 just isn’t aesthetic enough.

“Entropic (Gromov) Wasserstein Flow Matching with GENOT” by D. Klein et al. arxiv.org/abs/2310.09254 Transport between two distributions defined on different spaces by training a noise-to-data flow models in the target space, conditioned on the source data and leveraging Gromov–Wasserstein couplings

Diffusion Transformers with Representation Autoencoders by Boyang Zheng, et al (arxiv.org/abs/2510.116...) Unexpected result: swapping the SD-VAE for a pretrained visual encoder improves FID, challenging the idea that encoders' information compression is not suited for generative modeling!

"How to build a consistency model: Learning flow maps via self-distillation" by @nmboffi.bsky.social et al (arxiv.org/abs/2505.18825) New method to train flow maps without any pretrained flow matching/diffusion models!

"Be Tangential to Manifold: Discovering Riemannian Metric for Diffusion Models" Shinnosuke Saito et al. arxiv.org/abs/2510.05509 High-density regions might not be the most interesting areas to visit. Thus, they define a new Riemannian metric for diffusion models relying on the Jacobian of the score

Grateful for the opportunity to speak at tomorrow’s Learning Machines seminar (RISE+@climateainordics.com) on generative domain adaptation and geospatial foundation models benchmarking for robust Earth observation 🌍 Join on Sept 11 at 15:00 CET! www.ri.se/en/learningm...

Late to the party but I like the fact that you can use geodesic random walk (like really simulating the random walks) to derive the SDEs necessary for diffusion models on Riemannian manifolds (from arxiv.org/abs/2202.02763)

I'll be at #GRETSI in Strasbourg next week! Friday morning, I'll present our work on Riemannian flow matching for SAR interferometry (generation and denoising) 🛰️ Also really looking forward to the poster sessions and all the exciting conferences on the program! 📄 hal.science/hal-05140421

Anyone aware of a cats --> pure evil creatures image translation benchmark? not even neural networks’ dreams reached this level of nightmare fuel

New episode in this line of work from @giannisdaras.bsky.social et al. on training diffusion models with mostly bad/low-quality/corrupted data (+few high-quality samples). This time for proteins! 📄 Ambient diffusion Omni: arxiv.org/pdf/2506.10038 📄 Ambient Proteins: www.biorxiv.org/content/10.1...

Added to my reading list: Adjoint Schrödinger Bridge Sampler by Guan-Horng Liu et al. arxiv.org/abs/2506.22565

Drop the "conditional", just "flow matching", it's cleaner.

I was intrigued by "Mean Flows for One-Step Generative Modeling" and, in particular, how it handles averaging the marginal velocity field during training. In practice, they don't and replace it with the conditional one in their loss function. I wonder how mismatches impact generation...