Linear Diffusion

Linear diffusion models are a class of generative models that leverage stochastic processes to generate data by iteratively removing noise from a random sample. Current research focuses on improving the efficiency and quality of these models, particularly through the development of novel algorithms like those based on ordinary differential equations and the optimization of the diffusion process itself, often employing architectures such as transformers for efficient processing. This work aims to address limitations in speed and sample quality compared to other generative models, with applications in areas like high-quality speech synthesis and image generation. The resulting advancements promise faster and more efficient generative modeling across various domains.