<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/flux-performance.png">
</div>
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/hunyuan-video-performance.png">
</div>  
Large image and video generation models, such as [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) and [HunyuanVideo](https://huggingface.co/tencent/HunyuanVideo), can be an inference challenge for real-time applications and deployment because of their size.  
[ParaAttention](https://github.com/chengzeyi/ParaAttention) is a library that implements **context parallelism** and **first block cache**, and can be combined with other techniques (torch.compile, fp8 dynamic quantization), to accelerate inference.  
This guide will show you how to apply ParaAttention to FLUX.1-dev and HunyuanVideo on NVIDIA L20 GPUs.
No optimizations are applied for our baseline benchmark, except for HunyuanVideo to avoid out-of-memory errors.  
Our baseline benchmark shows that FLUX.1-dev is able to generate a 1024x1024 resolution image in 28 steps in 26.36 seconds, and HunyuanVideo is able to generate 129 frames at 720p resolution in 30 steps in 3675.71 seconds.  
> [!TIP]
> For even faster inference with context parallelism, try using NVIDIA A100 or H100 GPUs (if available) with NVLink support, especially when there is a large number of GPUs.
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/optimization/para_attn.md