  Denoising diffusion models have been a mainstream approach for image
generation, however, training these models often suffers from slow convergence.
In this paper, we discovered that the slow convergence is partly due to
conflicting optimization directions between timesteps. To address this issue,
we treat the diffusion training as a multi-task learning problem, and introduce
a simple yet effective approach referred to as Min-SNR-$\gamma$. This method
adapts loss weights of timesteps based on clamped signal-to-noise ratios, which
effectively balances the conflicts among timesteps. Our results demonstrate a
significant improvement in converging speed, 3.4$\times$ faster than previous
weighting strategies. It is also more effective, achieving a new record FID
score of 2.06 on the ImageNet $256\times256$ benchmark using smaller
architectures than that employed in previous state-of-the-art. The code is
available at https://github.com/TiankaiHang/Min-SNR-Diffusion-Training.
