Even within the same computational budget, a ReNO-optimized one-step model outperforms popular multi-step models such as SDXL and PixArt-α. Additionally, our strongest model, ReNO-enhanced HyperSDXL, is on par even with SOTA proprietary models, achieving a win rate of 54% vs SD3.

ReNO optimizes the initial noise in one-step T2I models at inference based on human preference reward models. We show that ReNO achieves significant improvements over five different one-step models quantitatively on common benchmarks and using comprehensive user studies.

Thanks to @fffiloni.bsky.social and @natanielruiz.bsky.social, we have a running live Demo of ReNO, play around with it here:

🤗: huggingface.co/spaces/fffil...

We are excited to present ReNO at #NeurIPS2024 this week!
Join us tomorrow from 11am-2pm at East Exhibit Hall A-C #1504!

Can we enhance the performance of T2I models without any fine-tuning?

We show that with our ReNO, Reward-based Noise Optimization, one-step models consistently surpass the performance of all current open-source Text-to-Image models within the computational budget of 20-50 sec!
#NeurIPS2024