161 lines
4.8 KiB
Markdown
161 lines
4.8 KiB
Markdown
(inference-optimizations)=
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# Optimizations
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This page describes the various options for speeding up generation times in FastVideo.
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## Table of Contents
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- Optimized Attention Backends
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- [Flash Attention](#optimizations-flash)
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- [Sliding Tile Attention](#optimizations-sta)
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- [Sage Attention](#optimizations-sage)
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- Caching Techniques
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- [TeaCache](#optimizations-teacache)
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(optimizations-backends)=
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## Attention Backends
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### Available Backends
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- Torch SDPA: `FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA`
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- Flash Attention 2 and 3: `FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN`
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- Sliding Tile Attention: `FASTVIDEO_ATTENTION_BACKEND=SLIDING_TILE_ATTN`
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- Video Sparse Attention: `FASTVIDEO_ATTENTION_BACKEND=VIDEO_SPARSE_ATTN`
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- Sage Attention: `FASTVIDEO_ATTENTION_BACKEND=SAGE_ATTN`
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### Configuring Backends
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There are two ways to configure the attention backend in FastVideo.
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#### 1. In Python
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In python, set the `FASTVIDEO_ATTENTION_BACKEND` environment variable before instantiating `VideoGenerator` like this:
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```python
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os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "SLIDING_TILE_ATTN"
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```
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#### 2. In CLI
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You can also set the environment variable on the command line:
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```bash
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FASTVIDEO_ATTENTION_BACKEND=SAGE_ATTN python example.py
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```
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(optimizations-flash)=
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### Flash Attention
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**`FLASH_ATTN`**
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We recommend always installing [Flash Attention 2](https://github.com/Dao-AILab/flash-attention):
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```bash
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pip install flash-attn==2.7.4.post1 --no-build-isolation
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```
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And if using a Hopper+ GPU (ie H100), installing [Flash Attention 3](https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#flashattention-3-beta-release) by compiling it from source (takes about 10 minutes for me):
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```bash
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git clone https://github.com/Dao-AILab/flash-attention.git && cd flash-attention
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cd hopper
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pip install ninja
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python setup.py install
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```
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:::{note}
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FastVideo will automatically detect and use `FA3` if it is installed when using `FLASH_ATTN` backend.
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:::
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(optimizations-sta)=
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### Sliding Tile Attention
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**`SLIDING_TILE_ATTN`**
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```bash
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pip install st_attn==0.0.4
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```
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Please see [this page](#sta-installation) for more installation instructions.
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(optimizations-vsa)=
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### Video Sparse Attention
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**`VIDEO_SPARSE_ATTN`**
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```bash
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git submodule update --init --recursive
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python setup_vsa.py install
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```
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Please see [this page](#vsa-installation) for more installation instructions.
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(optimizations-sage)=
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### Sage Attention
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**`SAGE_ATTN`**
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To use [SageAttention](https://github.com/thu-ml/SageAttention) 2.1.1, please compile from source:
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```bash
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git clone https://github.com/thu-ml/SageAttention.git
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cd sageattention
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python setup.py install # or pip install -e .
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```
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(optimizations-teacache)=
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## Teacache
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TeaCache is an optimization technique supported in FastVideo that can significantly speed up video generation by skipping redundant calculations across diffusion steps. This guide explains how to enable and configure TeaCache for optimal performance in FastVideo.
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### What is TeaCache?
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See the official [TeaCache](https://github.com/ali-vilab/TeaCache) repo and their paper for more details.
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### How to Enable TeaCache
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Enabling TeaCache is straightforward - simply add the `enable_teacache=True` parameter to your `generate_video()` call:
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```python
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# ... previous code
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generator.generate_video(
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prompt="Your prompt here",
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sampling_param=params,
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enable_teacache=True
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)
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# more code ...
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```
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### Complete Example
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At the bottom is a complete example of using TeaCache for faster video generation. You can run it using the following command:
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```bash
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python examples/inference/optimizations/teacache_example.py
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```
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### Advanced Configuration
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While TeaCache works well with default settings, you can fine-tune its behavior by adjusting the threshold value:
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1. Lower threshold values (e.g., 0.1) will result in more skipped calculations and faster generation with slightly more potential for quality degradation
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2. Higher threshold values (e.g., 0.15-0.23) will skip fewer calculations but maintain quality closer to the original
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Note that the optimal threshold depends on your specific model and content.
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## Benchmarking different optimizations
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To benchmark the performance improvement, try generating the same video with and without TeaCache enabled and compare the generation times:
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```python
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# Without TeaCache
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start_time = time.perf_counter()
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generator.generate_video(prompt="Your prompt", enable_teacache=False)
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standard_time = time.perf_counter() - start_time
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# With TeaCache
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start_time = time.perf_counter()
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generator.generate_video(prompt="Your prompt", enable_teacache=True)
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teacache_time = time.perf_counter() - start_time
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print(f"Standard generation: {standard_time:.2f} seconds")
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print(f"TeaCache generation: {teacache_time:.2f} seconds")
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print(f"Speedup: {standard_time/teacache_time:.2f}x")
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```
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Note: If you want to benchmark different attention backends, you'll need to reinstantiate `VideoGenerator`.
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