# DeepCache [DeepCache](https://huggingface.co/papers/2312.00858) accelerates [`StableDiffusionPipeline`] and [`StableDiffusionXLPipeline`] by strategically caching and reusing high-level features while efficiently updating low-level features by taking advantage of the U-Net architecture. Start by installing [DeepCache](https://github.com/horseee/DeepCache): ```bash pip install DeepCache ``` Then load and enable the [`DeepCacheSDHelper`](https://github.com/horseee/DeepCache#usage): ```diff import torch from diffusers import StableDiffusionPipeline pipe = StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5', torch_dtype=torch.float16).to("cuda") + from DeepCache import DeepCacheSDHelper + helper = DeepCacheSDHelper(pipe=pipe) + helper.set_params( + cache_interval=3, + cache_branch_id=0, + ) + helper.enable() image = pipe("a photo of an astronaut on a moon").images[0] ``` The `set_params` method accepts two arguments: `cache_interval` and `cache_branch_id`. `cache_interval` means the frequency of feature caching, specified as the number of steps between each cache operation. `cache_branch_id` identifies which branch of the network (ordered from the shallowest to the deepest layer) is responsible for executing the caching processes. 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](https://arxiv.org/abs/2312.00858)). 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](https://wandb.ai/horseee/DeepCache/runs/jwlsqqgt?workspace=user-horseee). The prompts are randomly selected from the [MS-COCO 2017](https://cocodataset.org/#home) dataset. ## Benchmark We tested how much faster DeepCache accelerates [Stable Diffusion v2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1) 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)|