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# 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('stable-diffusion-v1-5/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`.

<div class="flex justify-center">
    <img src="https://github.com/horseee/Diffusion_DeepCache/raw/master/static/images/example.png">
</div>

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)|
