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by @danabra.mov
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by @danabra.mov
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by @jimpick.com
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by @atsui.org
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🎉 Our work MIRO is accepted to #ICML2026 @icmlconf.bsky.social We integrate human preferences directly during pretraining with multi-reward conditioning. ⚡MIRO is 19x faster than baselines and 370x cheaper at inference! 🤗 Try out the models: huggingface.co/spaces/nicol... See you in Seoul 🇰🇷 !
24d
Multi-reward conditioned text-to-image diffusion (ICML 2026)
huggingface.co
MIRO - a Hugging Face Space by nicolas-dufour
Lucas Degeorge
Thrilled to share that MIRO is accepted to ICML 2026 @icmlconf.bsky.social ! 🎉 By training on the reward scores, we can simply condition the model on high rewards at inference time to guarantee top-tier, aligned outputs. We’ve updated our paper with some additional results!
24d
Nicolas Dufour
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 to the reward, typi...
arxiv.org
MIRO: MultI-Reward cOnditioned pretraining improves T2I quality and efficiency
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 📉
7mo
Nicolas Dufour