4 papers
MIRO: MultI-Reward cOnditioned pretraining improves T2I quality and efficiency
Nicolas Dufour, Lucas Degeorge, Arijit Ghosh +2
The default paradigm of post-training text-to-image generators includes post-hoc selection of generated images, and subsequent training with one reward model to align the generator…
PoM: A Linear-Time Replacement for Attention with the Polynomial Mixer
David Picard, Nicolas Dufour, Lucas Degeorge +14
This paper introduces the Polynomial Mixer (PoM), a novel token mixing mechanism with linear complexity that serves as a drop-in replacement for self-attention. PoM aggregates inpu…
One-step Diffusion Models with Bregman Density Ratio Matching
Yuanzhi Zhu, Eleftherios Tsonis, Lucas Degeorge +1
Diffusion and flow models achieve high generative quality but remain computationally expensive due to slow multi-step sampling. Distillation methods accelerate them by training fas…
How far can we go with ImageNet for Text-to-Image generation?
L. Degeorge, A. Ghosh, N. Dufour +2
Recent text-to-image (T2I) generation models have achieved remarkable sucess by training on billion-scale datasets, following a `bigger is better' paradigm that prioritizes data qu…