Reflected Diffusion Models are a new class of diffusion models that respect general boundary constraints and correctly train for thresholded sampling. They bridge the gap between theoretical frameworks and practical implementation details, leading to improved performance and image quality in generative modeling tasks.

Diffusion models are generative models that work by perturbing data using a stochastic process, then reversing this process to generate new data. However, when discretized into discrete steps (as in most practical implementations), the sampling process can negatively affect data quality. To alleviate this issue, a technique called thresholding is employed, resulting in better samples but breaking the theoretical framework.

Reflected Diffusion Models use a reflected stochastic differential equation (rSDE) to correctly model the thresholding process. This allows the models to train appropriately while respecting boundary constraints, like images with pixel values in the [0, 255] range.

The benefits of Reflected Diffusion Models include improved perceptual quality, correct handling of boundary constraints, better image generation with guidance, and general applicability to different shapes of data domains. This has the potential to expand the applications of diffusion models in fields such as image, language, and molecule generation.