Are you tired of waiting for hours to process your video data using the Stable Video Diffusion (SVD) model? Well, we have some exciting news for you! Introducing Stable-Fast v1, the ultimate inference performance optimization framework for HuggingFace Diffusers on NVIDIA GPUs. With this model, you can achieve a 2x speedup for SVD without any additional cost. Let’s dive into the details and see how Stable-Fast v1 can revolutionize your video processing workflow.
Table of Contents
What is Stable-Fast v1?
Stable-Fast v1 is an ultra-lightweight inference optimization framework specifically designed for HuggingFace Diffusers on NVIDIA GPUs. It was developed by Chengzeyi and is available on GitHub for easy access and usage. With this model, you can significantly enhance the inference performance of the Stable Video Diffusion (SVD) model, making it faster and more efficient than ever before.
How does Stable-Fast v1 work?
Stable-Fast v1 utilizes a range of cutting-edge techniques and features to optimize the inference process for SVD. Some of the key highlights include:
1. CUDNN Convolution Fusion
Stable-Fast v1 implements a series of fully functional and fully compatible CUDNN convolution fusion operators for various combinations of Convolution and Bias operations. This optimization technique ensures that the computations are performed efficiently, resulting in a significant speed boost.
2. Dynamic Shape Support
It supports dynamic shape, allowing for flexible input sizes during inference. This feature eliminates the need for resizing or preprocessing the input data, saving both time and effort.
3. LoRA and ControlNet Integration
It seamlessly integrates with LoRA and ControlNet, enabling dynamic switching and enhanced performance for SVD. This integration ensures that you can achieve optimal results while working with complex video data.
4. Model Quantization
It offers model quantization capabilities, allowing you to compress the SVD model without sacrificing accuracy. This technique further improves inference speed and reduces memory usage.
| Also Read: AI Model Quantization Showdown: GPTQ vs GGML
Installation and Usage
Simply follow these steps:
- Clone the Stable-Fast v1 repository from GitHub.
- Install the necessary dependencies and prebuilt wheels as per the instructions provided in the repository.
- Use the Stable-Fast v1 framework to optimize your SVD inference pipeline. You can choose from various optimization options, such as StableDiffusionPipeline, LCM Pipeline, StableVideoDiffusionPipeline, Dynamically Switching LoRA, and Model Quantization.
- Enjoy the lightning-fast performance of your SVD model with Stable-Fast v1!
Performance Comparison
To give you a better understanding of the impact of Stable-Fast v1, let’s compare its performance with other popular acceleration libraries on different GPU models:
1. RTX 4080 (512×512, batch size 1, fp16, in WSL2)
It achieves an impressive inference time of 995 milliseconds for StableDiffusion (SD) 1.5, outperforming other libraries like torch, AIT, oneflow, and TensorRT.
| Also Read: ASUS RTX 4070 SUPER Leaked : Powerful Performance Boost for January 2024 Launch
2. H100
Stable-Fast v1 achieves exceptional results with an inference time of 83 seconds for Stable Video Diffusion (SVD-XT), surpassing the performance of other libraries.
| Also Read: NVIDIA H200 Tensor Core GPU: Most Powerful GPU for AI & HPC
3. A100
This model continues to shine with an inference time of 70 seconds for SVD-XT, leaving other libraries in the dust.
It provides a very fast compilation speed within only a few seconds. It is significantly faster than torch.compile, TensorRT and AITemplate in compilation time.
Conclusion
Stable-Fast v1 is a game-changer for anyone working with the Stable Video Diffusion (SVD) model. With its powerful optimization techniques, dynamic shape support, LoRA and ControlNet integration, and model quantization capabilities, this model offers a 2x speedup for SVD with zero additional cost. So why wait? Try Stable-Fast v1 today and experience the future of video processing!
| Also Read:
