Opting for a lower cache_branch_id or a larger cache_interval can lead to faster inference speed at the expense of reduced image quality (ablation experiments of these two hyperparameters can be found in the paper). Once those arguments are set, use the enable or disable methods to activate or deactivate the DeepCacheSDHelper.  You can find more generated samples (original pipeline vs DeepCache) and the corresponding inference latency in the WandB report. The prompts are randomly selected from the MS-COCO 2017 dataset.  Benchmark We tested how much faster DeepCache accelerates Stable Diffusion v2.1 with 50 inference steps on an NVIDIA RTX A5000, using different configurations for resolution, batch size, cache interval (I), and cache branch (B). Resolution Batch size Original DeepCache(I=3, B=0) DeepCache(I=5, B=0) DeepCache(I=5, B=1) 512 8 15.96 6.88(2.32x) 5.03(3.18x) 7.27(2.20x)  4 8.39 3.60(2.33x) 2.62(3.21x) 3.75(2.24x)  1 2.61 1.12(2.33x) 0.81(3.24x) 1.11(2.35x) 768 8 43.58 18.99(2.29x) 13.96(3.12x) 21.27(2.05x)  4 22.24 9.67(2.30x) 7.10(3.13x) 10.74(2.07x)  1 6.33 2.72(2.33x) 1.97(3.21x) 2.98(2.12x) 1024 8 101.95 45.57(2.24x) 33.72(3.02x) 53.00(1.92x)  4 49.25 21.86(2.25x) 16.19(3.04x) 25.78(1.91x)  1 13.83 6.07(2.28x) 4.43(3.12x) 7.15(1.93x)