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[Doc] Add DeepCache in section optimization/General optimizations (#6390)
* add documentation for DeepCache * fix typo * add wandb url for DeepCache * fix some typos * add item in _toctree.yml * update formats for arguments * Update deepcache.md * Update docs/source/en/optimization/deepcache.md Co-authored-by: Sayak Paul <[email protected]> * add StableDiffusionXLPipeline in doc * Separate SDPipeline and SDXLPipeline * Add the paper link of ablation experiments for hyper-parameters * Apply suggestions from code review Co-authored-by: Steven Liu <[email protected]> --------- Co-authored-by: Sayak Paul <[email protected]> Co-authored-by: Steven Liu <[email protected]>
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docs/source/en/_toctree.yml

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title: xFormers
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- local: optimization/tome
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title: Token merging
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- local: optimization/deepcache
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title: DeepCache
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title: General optimizations
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- sections:
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- local: using-diffusers/stable_diffusion_jax_how_to
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<!--Copyright 2023 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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-->
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# DeepCache
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[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.
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Start by installing [DeepCache](https://github.com/horseee/DeepCache):
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```bash
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pip install DeepCache
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```
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Then load and enable the [`DeepCacheSDHelper`](https://github.com/horseee/DeepCache#usage):
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```diff
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import torch
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from diffusers import StableDiffusionPipeline
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pipe = StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5', torch_dtype=torch.float16).to("cuda")
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+ from DeepCache import DeepCacheSDHelper
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+ helper = DeepCacheSDHelper(pipe=pipe)
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+ helper.set_params(
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+ cache_interval=3,
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+ cache_branch_id=0,
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+ )
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+ helper.enable()
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image = pipe("a photo of an astronaut on a moon").images[0]
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```
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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.
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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`.
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<div class="flex justify-center">
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<img src="https://github.com/horseee/Diffusion_DeepCache/raw/master/static/images/example.png">
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</div>
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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.
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## Benchmark
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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).
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| **Resolution** | **Batch size** | **Original** | **DeepCache(I=3, B=0)** | **DeepCache(I=5, B=0)** | **DeepCache(I=5, B=1)** |
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|----------------|----------------|--------------|-------------------------|-------------------------|-------------------------|
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| 512| 8| 15.96| 6.88(2.32x)| 5.03(3.18x)| 7.27(2.20x)|
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| | 4| 8.39| 3.60(2.33x)| 2.62(3.21x)| 3.75(2.24x)|
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| | 1| 2.61| 1.12(2.33x)| 0.81(3.24x)| 1.11(2.35x)|
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| 768| 8| 43.58| 18.99(2.29x)| 13.96(3.12x)| 21.27(2.05x)|
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| | 4| 22.24| 9.67(2.30x)| 7.10(3.13x)| 10.74(2.07x)|
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| | 1| 6.33| 2.72(2.33x)| 1.97(3.21x)| 2.98(2.12x)|
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| 1024| 8| 101.95| 45.57(2.24x)| 33.72(3.02x)| 53.00(1.92x)|
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| | 4| 49.25| 21.86(2.25x)| 16.19(3.04x)| 25.78(1.91x)|
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| | 1| 13.83| 6.07(2.28x)| 4.43(3.12x)| 7.15(1.93x)|

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