We took an open source implementation of a popular text-to-image diffusion model as a starting point and accelerated its generation using two optimizations available in PyTorch 2: compilation and fast attention implementation. Together with a few minor memory processing improvements in the code these optimizations give up to 49% inference speedup relative to the original implementation without xFormers, and 39% inference speedup relative to using the original code with xFormers (excluding the compilation time), depending on the GPU architecture and batch size. Importantly, the speedup comes without a need to install xFormers or any other extra dependencies.