 horse on mars with 50 DDIM steps on a Nvidia Titan RTX, demonstrating the speed-up you can expect.  latency speed-up original 9.50s x1 fp16 3.61s x2.63 channels last 3.30s x2.88 traced UNet 3.21s x2.96 memory efficient attention 2.63s x3.61  Use TensorFloat-32 On Ampere and later CUDA devices, matrix multiplications and convolutions can use the TensorFloat-32 (TF32) mode for faster, but slightly less accurate computations. By default, PyTorch 