Few-Step Diffusion Sampling

Few-step diffusion sampling asks how to produce images, videos, text, and other data with only a handful of denoising steps instead of hundreds or thousands. One major approach distills a many-step teacher into a faster student using consistency training, trajectory matching, or distribution matching; another improves existing samplers through better time schedules, ODE solvers, or error correction without retraining. Methods are tested on image benchmarks such as CIFAR-10 and ImageNet, text-to-image and video suites, and language-generation tasks, where speed is weighed against fidelity, prompt alignment, motion, and diversity. A central tension is that strong scores on individual samples can conceal lost distribution coverage or incorrect sampling, so evaluations increasingly need to measure both quality and diversity under matched compute budgets.