To address the high computational costs associated with the iterative sampling processes in video diffusion and flow models, researchers have introduced Parallel Decoding Distillation (PDD). Unlike existing acceleration methods like variational score distillation (VSD) or adversarial losses—which are prone to optimization difficulties and mode collapse—PDD offers a scalable, trajectory-based distillation approach compatible with any pre-trained model. The method accelerates inference by predicting multiple denoising steps within a single network evaluation, effectively learning a representation of the mean velocity without the need for regressing derivatives via finite-difference approximations or Jacobian-vector products (JVPs). A key technical advantage is its support for flexible sampling with a varying number of function evaluations (NFE). Experimental results demonstrate that PDD achieves state-of-the-art performance using only 4-8 NFE across several benchmarks, including LTX-2.3 Text-to-Video/Audio, Wan 14B Text-to-Video, and Qwen-Image Text-to-Image. Beyond mere speed, the method significantly improves the diversity of generated video content, overcoming the loss of motion and variety often seen in other distillation-based few-step generators.