# Optimizations

This page describes the various options for speeding up generation times in FastVideo.

## Table of Contents

- Optimized Attention Backends

  - [Flash Attention](#flash-attention)
  - [Sliding Tile Attention (Archived)](#sliding-tile-attention-archived)
  - [Sage Attention](#sage-attention)
  - [Sage Attention 3](#sage-attention-3)

## Attention Backends

### Available Backends

- Torch SDPA: `FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA`
- Flash Attention 2 and 3: `FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN`
- Video Sparse Attention: `FASTVIDEO_ATTENTION_BACKEND=VIDEO_SPARSE_ATTN`
- Sage Attention: `FASTVIDEO_ATTENTION_BACKEND=SAGE_ATTN`
- Sage Attention 3: `FASTVIDEO_ATTENTION_BACKEND=SAGE_ATTN_THREE`
- Attn QAT Infer: `FASTVIDEO_ATTENTION_BACKEND=ATTN_QAT_INFER`
- Attn QAT Train: `FASTVIDEO_ATTENTION_BACKEND=ATTN_QAT_TRAIN`
- Video MoBA Attention: `FASTVIDEO_ATTENTION_BACKEND=VMOBA_ATTN`
- Sparse Linear Attention: `FASTVIDEO_ATTENTION_BACKEND=SLA_ATTN`
- SageSLA Attention: `FASTVIDEO_ATTENTION_BACKEND=SAGE_SLA_ATTN`
- Sliding Tile Attention (archived branch only):
  `FASTVIDEO_ATTENTION_BACKEND=SLIDING_TILE_ATTN`

### Configuring Backends

There are two ways to configure the attention backend in FastVideo.

#### 1. In Python

In python, set the `FASTVIDEO_ATTENTION_BACKEND` environment variable before instantiating `VideoGenerator` like this:

```python
os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
```

#### 2. In CLI

You can also set the environment variable on the command line:

```bash
FASTVIDEO_ATTENTION_BACKEND=SAGE_ATTN python example.py
```

### Flash Attention

**`FLASH_ATTN`**

We recommend always installing [Flash Attention 2](https://github.com/Dao-AILab/flash-attention):

```bash
pip install flash-attn==2.7.4.post1 --no-build-isolation
```

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):

```bash
git clone https://github.com/Dao-AILab/flash-attention.git && cd flash-attention

cd hopper
pip install ninja
python setup.py install
```

### Sliding Tile Attention (Archived)

**`SLIDING_TILE_ATTN`**

The full STA integration in `fastvideo/` is archived from `main` and preserved
at:

- https://github.com/hao-ai-lab/FastVideo/tree/sta_do_not_delete

We keep STA off `main` because we believe VSA is strictly better than STA for
the actively maintained FastVideo path.

Kernel code in `fastvideo-kernel` is still retained. For mask search and STA
inference workflow, see [STA docs](../attention/sta/index.md).

### Video Sparse Attention

**`VIDEO_SPARSE_ATTN`**

Video Sparse Attention is provided by `fastvideo-kernel`.
See [VSA docs](../attention/vsa/index.md) for installation details.

### Sage Attention

**`SAGE_ATTN`**

To use [SageAttention](https://github.com/thu-ml/SageAttention) 2.1.1, please compile from source:

```bash
git clone https://github.com/thu-ml/SageAttention.git
cd sageattention
python setup.py install  # or pip install -e .
```

### Sage Attention 3

FastVideo now exposes two SageAttention3-compatible backends with distinct
environment variable values:

- `SAGE_ATTN_THREE`: the regular upstream SageAttention3 backend imported from
  the `sageattn3` package.
- `ATTN_QAT_INFER`: the inference CUDA-kernel backend imported from the
  in-repo `attn_qat_infer` package.

**`SAGE_ATTN_THREE`**

[SageAttention 3](https://github.com/thu-ml/SageAttention/tree/main/sageattention3_blackwell) is an advanced attention mechanism that leverages FP4 quantization and Blackwell GPU Tensor Cores for significant performance improvements.

#### Hardware Requirements

- RTX5090

#### Installation

Note that Sage Attention 3 requires `python>=3.13`, `torch>=2.8.0`, `CUDA >=12.8`. If you are using `uv` and using `torch==2.8.0` make sure that `sentencepiece==0.2.1` in the pyproject.toml file.

To use Sage Attention 3 in FastVideo, follow the `README.md` in the linked repository to install the package from source.

### Attn QAT Infer

**`ATTN_QAT_INFER`**

This backend uses the `attn_qat_infer` implementation that lives in the
`fastvideo-kernel` repository alongside the `fastvideo_kernel` Triton kernels.
Use this backend when you want to run the dedicated FP4 inference CUDA kernel
directly during inference.

For the full Attention QAT guide, including Wan 2.1 14B checkpoint download,
example editing steps, training launchers, and troubleshooting, see
[Attention QAT](../attention/attn_qat/index.md).

This backend currently assumes access to the in-repo `fastvideo-kernel`
checkout or an equivalent editable/source install that exposes:

- `attn_qat_infer`

Example:

```python
os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "ATTN_QAT_INFER"
```

### QAT Attention

**`ATTN_QAT_TRAIN`**

This backend uses the FastVideoKernel Triton attention implementation from
`fastvideo_kernel.triton_kernels.attn_qat_train`. Use it when you specifically
want the training-oriented Triton attention path rather than the
`attn_qat_infer` CUDA kernel path.

The dedicated [Attention QAT](../attention/attn_qat/index.md) page covers when
to use `ATTN_QAT_TRAIN` versus `ATTN_QAT_INFER`, the ready-made training
launchers, and the end-to-end Wan 2.1 14B inference workflow.

This backend currently assumes access to an install that exposes:

- `fastvideo_kernel`

Example:

```python
os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "ATTN_QAT_TRAIN"
```

### V-MoBA / SLA / SageSLA

These backends are model-specific and require the corresponding kernels and
dependencies. Use the support matrix and model examples to confirm compatibility
before enabling them.

## Benchmarking different optimizations

To benchmark backend performance, generate the same prompt with the same seed and compare end-to-end generation times:

```python
import os
import time

for backend in ["TORCH_SDPA", "FLASH_ATTN", "SAGE_ATTN"]:
    os.environ["FASTVIDEO_ATTENTION_BACKEND"] = backend
    generator = VideoGenerator.from_pretrained("your-model-id")
    start_time = time.perf_counter()
    generator.generate_video(
        prompt="Your prompt",
        seed=1024,
    )
    elapsed = time.perf_counter() - start_time
    print(f"{backend}: {elapsed:.2f}s")
```

Note: reinstantiate `VideoGenerator` after changing `FASTVIDEO_ATTENTION_BACKEND`.