How to install cuDNN and TensorRT to enhance the inference performance of Stable Diffusion.

Harnessing the Power of cuDNN and TensorRT: Accelerating Stable Diffusion on NVIDIA's 40-Series GPUs

NVIDIA's 40-series GPUs, when paired with Stable Diffusion, can achieve remarkable inference performance enhancements by enabling cuDNN and TensorRT technologies. These optimizations, tailored for NVIDIA's latest GPU architecture, can double or even surpass inference speeds. This guide delves into the workings of these technologies and demonstrates their seamless implementation within your system.

Unlocking Performance with cuDNN and TensorRT:

1. cuDNN (CUDA Deep Neural Network Library): cuDNN, a meticulously crafted library from NVIDIA, specializes in expediting the inference and training processes of deep neural networks (DNNs). In the context of Stable Diffusion, cuDNN accelerates the computations of convolutional neural networks (CNNs), culminating in reduced inference times and enhanced efficiency.

2. TensorRT: TensorRT, NVIDIA's dedicated deep learning acceleration library for inference optimization, leverages techniques like quantization (reducing precision calculations to FP16 or implementing INT8 quantization) to significantly accelerate tasks such as image generation. The latest version of the Automatic1111 Web UI seamlessly integrates TensorRT support, empowering users to easily enable this performance boost.

Configuring cuDNN and TensorRT for Enhanced Performance:

1. Installing cuDNN:

cuDNN, NVIDIA's GPU acceleration library, is specifically designed to accelerate deep learning tasks, including Stable Diffusion. If your current Stable Diffusion Web UI version doesn't utilize the latest cuDNN accelerated library, manually installing and updating cuDNN can yield substantial performance gains.

Installation Steps:

1. Visit the NVIDIA website to download the cuDNN library compatible with your CUDA version.
2. Extract the downloaded archive and copy the contents of the bin, include, and lib folders into your CUDA installation directory (typically /usr/local/cuda). Windows users can simply install using the provided exe file.
3. Once complete, restart your system to ensure the new library takes effect.

2. Enabling TensorRT Optimization:

TensorRT, through its RTX GPU-specific optimizations, dramatically accelerates the inference process, resulting in up to a 2x speed increase for Stable Diffusion.

Installing the TensorRT Extension:

1. Launch the Stable Diffusion Web UI.
2. Navigate to the Extensions tab and click Install from URL.
3. Enter the following URL to install the TensorRT extension:

txt
https://github.com/NVIDIA/Stable-Diffusion-WebUI-TensorRT

4. After installation, click Apply and restart UI.

Configuring TensorRT:

1. In the Web UI's top navigation bar, you'll find the newly added TensorRT options.
2. Access Settings, select User Interface, and add sd_unet to the Quick Settings List.
3. Return to the main interface and set the sd_unet option to Automatic.
4. Further enhance performance by utilizing the TensorRT tab to generate optimized engines for specific resolutions and batch sizes.

Performance Benchmarking:

• With TensorRT optimization, an RTX 4060 Ti experiences approximately a 1x speed increase at 512x512 and 768x768 resolutions, making batch image generation significantly more efficient. This optimization is particularly valuable for users prioritizing high-speed image generation.

Conclusion:

For NVIDIA 40-series GPU users (e.g., RTX 4060 Ti) seeking to elevate Stable Diffusion's generation speed, enabling cuDNN and TensorRT extensions is a proven path to enhanced performance. These optimizations not only significantly improve inference speed but also fully leverage the GPU's potential, making them ideal for handling demanding image generation tasks.

For more comprehensive guidance, consult the following resources:

• Puget Systems official tutorial
• Civitai's tutorial
• NVIDIA GitHub page