Researchers from UCSD and Adobe have introduced Presto!, an AI approach to inference acceleration for score-based diffusion transformers, which reduces both sampling steps and cost per step. Concurrently, researchers from Shanghai Jiao Tong University, the University of Cambridge, and Geely Automobile Research Institute have developed F5-TTS, a fully non-autoregressive text-to-speech system based on flow matching with diffusion transformers. F5-TTS includes features such as zero-shot voice cloning, emotion-based synthesis, long-form synthesis, and speed control, and is trained on 100,000 hours of data. The system supports code-switching and is available under a CC-BY license, making it commercially permissive.