Reflected Diffusion Models
Diffusion models are trained to reverse a stochastic process through score matching. However, a lot of diffusion models rely on a small but critical implementation detail called thresholding. Thresholding projects the sampling process to the data support after each discretized diffusion step, stabilizing generation at the cost of breaking the theoretical framework. Interestingly, as one limits the number of steps to infinity, thresholding converges to a reflected stochastic differential equation...
Monte Carlo Euler Maruyama Reflected Diffusion Reflected Diffusion Models Normalizing Flows Stable Diffusion
Source: aaronlou.com