Check out our new work: MIRO

No more post-training alignment!
We integrate human alignment right from the start, during pretraining!

Results:
✨ 19x faster convergence ⚡
✨ 370x less compute 💻

🔗 Explore the project: nicolas-dufour.github.io/miro/

We introduce MIRO: a new paradigm for T2I model alignment integrating reward conditioning into pretraining, eliminating the need for separate fine-tuning/RL stages. This single-stage approach offers unprecedented efficiency and control.

- 19x faster convergence ⚡
- 370x less FLOPS than FLUX-dev 📉

🚀 DinoV3 just became the new go-to backbone for geoloc!
It outperforms CLIP-like models (SigLip2, finetuned StreetCLIP)… and that’s shocking 🤯
Why? CLIP models have an innate advantage — they literally learn place names + images. DinoV3 doesn’t.

I will be at #CVPR2025 this week in Nashville.

I will be presenting our paper "Around the World in 80 Timesteps:
A Generative Approach to Global Visual Geolocation".

We tackle geolocalization as a generative task allowing for SOTA performance and more interpretable predictions.

Masked Diffusion Models (MDMs) are a hot topic in generative AI 🔥 — powerful but slow due to multiple sampling steps.
We @polytechniqueparis.bsky.social and @inria-grenoble.bsky.social introduce Di[M]O — a novel approach to distill MDMs into a one-step generator without sacrificing quality.

Check out our latest work on Text-to-Image generation! We've successfully trained a T2I model using only ImageNet data by leveraging captioning and data augmentation.

Our paper got accepted at TMLR!

TLDR; You can improve your diffusion samples by increasing guidance during the sampling process. A simpler linear scheduler suffice and is more robust than more elaborated methods.

🌍 Guessing where an image was taken is a hard, and often ambiguous problem. Introducing diffusion-based geolocation—we predict global locations by refining random guesses into trajectories across the Earth's surface!

🗺️ Paper, code, and demo: nicolas-dufour.github.io/plonk