Diffusion Model Acceleration

Diffusion model acceleration studies how to make iterative denoising models faster and cheaper to train and run across images, video, language, and scientific applications. Common approaches reduce the number of denoising steps through distillation, consistency learning, or adaptive solvers, and reduce work per step through caching, token or attention sparsity, quantization, and efficient architectures. Research also targets training throughput and deployment, using optimized kernels, parallelism, and compact models on settings such as text-to-image benchmarks, long-video generation, and medical imaging. The central challenge is balancing real wall-clock speed and memory savings against fidelity, diversity, and temporal consistency, since effective shortcuts can depend on the model, task, and denoising stage.