How to Install & Run TensorRT on RunPod, Unix, Linux for 2x Faster Stable Diffusion Inference Speed

SECourses
27 Oct 202313:02

TLDRThis video tutorial demonstrates the significant speed improvement achieved by installing TensorRT on RunPod for generating Stable Diffusion XL (SDXL) images. The comparison shows that the TensorRT-accelerated pod is 66% faster than the default pod, despite using the same GPU. The guide also covers the installation process of TensorRT on Unix systems and RunPod, highlighting the potential for even greater speed gains with updated Nvidia drivers. The video encourages viewers to watch the full tutorial for detailed instructions on leveraging TensorRT for enhanced image generation performance.

Takeaways

  • 🚀 TensorRT RTX acceleration offers significant speed improvements for generating Stable Diffusion XL (SDXL) images.
  • 📈 The speed difference is evident, with the TensorRT pod taking approximately 1.5 minutes compared to 3 minutes for the default setup.
  • 🔧 Installation of TensorRT on RunPod is applicable to Unix users, as RunPod operates on the Ubuntu OS.
  • 🎨 The image quality between the default pod and TensorRT pod remains consistent, with only minor differences possibly due to xFormers.
  • 📊 The TensorRT pod demonstrated a 66% faster processing time for generating 50 SDXL images.
  • 🚫 A limitation exists with the inability to upgrade Nvidia drivers on the RunPod template, which affects the full potential of TensorRT speed.
  • 💡 Despite using an older Nvidia driver, the TensorRT pod still showed a significant speed increase.
  • 🔄 The tutorial provides a step-by-step guide on how to install TensorRT on Unix systems, including changes to the relauncher.py file.
  • 📚 The Patreon post linked in the video description offers detailed instructions and necessary attachments for the installation process.
  • 🌐 The video script suggests that further speed improvements can be achieved with updated Nvidia drivers and local computer installations.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is the demonstration of the speed difference when generating Stable Diffusion XL (SDXL) images with and without TensorRT RTX acceleration on RunPod.

  • What are the two identical pods running on RunPod?

    -The two identical pods running on RunPod are the same in terms of hardware, with one pod running on the default setup and the other using TensorRT RTX acceleration.

  • How much faster is the TensorRT pod compared to the default pod in generating SDXL images?

    -The TensorRT pod is 66 percent faster than the default pod in generating SDXL images.

  • What is the time taken by the default pod and the TensorRT pod to generate 50 images?

    -The default pod took 2 minutes and 53 seconds, while the TensorRT pod took only 1 minute and 44 seconds to generate 50 images.

  • Is there a difference in image quality between the images generated by the default pod and the TensorRT pod?

    -No, the image quality is the same between the images generated by the default pod and the TensorRT pod.

  • What is the Nvidia driver version currently used on RunPod?

    -The Nvidia driver version currently used on RunPod is 525, which is an older version.

  • What is the recommended Nvidia driver version for Linux according to TensorRT developers?

    -The recommended Nvidia driver version for Linux according to TensorRT developers is 450.

  • How can the speed of image generation be further improved?

    -The speed of image generation can be further improved by upgrading the Nvidia driver on RunPod or by using the latest driver on a local computer.

  • What is the purpose of the 'install_tensorRT.sh' and '1_click_auto1111_SDXL.sh' files mentioned in the script?

    -The 'install_tensorRT.sh' file is used to install the latest version of TensorRT along with its necessary dependencies, and the '1_click_auto1111_SDXL.sh' file is used to download the latest VAE files for both SDXL and SD 1.5 based versions.

  • How does the video script guide the user to change the default behavior of the Stable Diffusion Web UI?

    -The script guides the user to change the 'relauncher.py' file from the Stable Diffusion Web UI template to prevent it from permanently relaunching the Web UI instance upon restart.

  • What is the significance of using the RTX 3090 GPU for TensorRT installation in the video?

    -The significance of using the RTX 3090 GPU is to demonstrate the speed increase that can be achieved even with an older Nvidia driver, and to show that the performance improvement is substantial when using TensorRT.

Outlines

00:00

🚀 TensorRT Acceleration on RunPod: Speed Comparison

This paragraph introduces a comparison between two identical pods running on RunPod, one with the default setup and the other utilizing TensorRT RTX acceleration. The presenter demonstrates the significant speed difference when generating Stable Diffusion XL (SDXL) images, with the TensorRT pod being approximately 66% faster. The video aims to guide viewers on how to install TensorRT on RunPod, which uses the Ubuntu operating system, and emphasizes the potential for even greater speed improvements with updated Nvidia drivers.

05:03

📚 Installation Guide for TensorRT on Unix Systems

The second paragraph delves into the process of installing TensorRT on Unix systems, specifically within the context of RunPod. It covers the necessary steps to download and apply modifications to the Stable Diffusion Web UI template, including updating the relauncher.py file and installing the required dependencies. The section also discusses the impact of using an older Nvidia driver on RunPod and provides tips for optimizing TensorRT performance, both on RunPod and personal computers. The presenter promises a comprehensive tutorial on TensorRT, including its benefits and installation process.

10:07

🔧 Optimizing TensorRT for Maximum Efficiency

The final paragraph focuses on the optimization of TensorRT for maximum efficiency, detailing the steps to export the engine and the expected duration based on GPU capabilities. It highlights the significant speed increase achieved with the RTX 3090 GPU, despite using an older Nvidia driver. The presenter encourages viewers to watch the full tutorial for in-depth information on TensorRT and its advanced setup options. The paragraph concludes with a call to action for viewers to like and subscribe to the channel for more informative content.

Mindmap

Keywords

💡RunPod

RunPod is a cloud-based platform that allows users to run various types of pods, which are essentially virtual machines, for different purposes. In the context of the video, it is used to run pods with GPUs for generating Stable Diffusion XL (SDXL) images. The video compares the speed difference between a default pod and one with TensorRT RTX acceleration.

💡Stable Diffusion XL (SDXL)

Stable Diffusion XL (SDXL) is a type of AI model used for generating high-quality images. It is an enhanced version of the Stable Diffusion model, capable of producing larger and more detailed images. In the video, the focus is on demonstrating the speed at which SDXL images can be generated using different configurations.

💡TensorRT

TensorRT is a software library for deep learning inference developed by NVIDIA. It optimizes neural network models for deployment by reducing the computational complexity and memory requirements of the models, thereby speeding up the inference process. In the video, TensorRT is used to accelerate the generation of SDXL images on RunPod.

💡RTX acceleration

RTX acceleration refers to the use of NVIDIA's RTX series GPUs to speed up computational tasks, particularly those related to AI and deep learning. RTX stands for 'Real Time Ray Tracing,' and these GPUs are designed to handle complex图形 and AI computations efficiently. In the video, RTX acceleration is used to enhance the performance of the pod running SDXL image generation.

💡ChatGPT GPT4

ChatGPT GPT4 is a reference to an advanced language model developed by OpenAI, capable of generating human-like text based on prompts. While not directly the focus of the video, the mention of ChatGPT GPT4 highlights the AI-driven nature of the tasks being performed, such as generating prompts for image creation.

💡Unix system

A Unix system refers to an operating system that follows the Unix model, which is a powerful, multi-user, and multitasking system with a simple, clear, and portable interface. In the video, the mention of Unix system is in the context of RunPod using Ubuntu, which is a Unix-like operating system, and the tutorial's applicability to Unix users.

💡Nvidia driver

The Nvidia driver refers to the software provided by NVIDIA that allows the operating system to communicate with and utilize Nvidia GPUs. The performance of GPU-accelerated tasks, such as image generation in the video, can be significantly impacted by the version of the Nvidia driver being used.

💡VRAM usage

VRAM usage refers to the amount of video memory (VRAM) being utilized by a GPU during operations. In the context of the video, it is used to compare the efficiency of the two pods running SDXL image generation, showing that both configurations use a similar amount of VRAM despite the difference in processing speed.

💡JupyterLab

JupyterLab is an open-source web-based user interface for Project Jupyter, which allows users to create and share documents that contain live code, equations, visualizations, and narrative text. In the video, JupyterLab is used as the environment to connect to the pod and execute commands for installing TensorRT and generating images.

💡Web UI

Web UI stands for Web User Interface, which refers to the visual and interactive part of a software application that is accessed through a web browser. In the context of the video, the Web UI is the interface used within JupyterLab to interact with the Stable Diffusion Web UI template for generating images.

💡ONNX file

An ONNX file is a standard file format for artificial intelligence models that enables models to be used across different platforms and frameworks. It stands for Open Neural Network Exchange. In the video, the ONNX file is generated as part of the process to enable TensorRT to accelerate the image generation process.

Highlights

Two identical pods running on RunPod are demonstrated, one with default setup and the other using TensorRT RTX acceleration.

The TensorRT RTX acceleration pod is shown to generate Stable Diffusion XL (SDXL) images significantly faster than the default pod.

A speed comparison reveals the TensorRT pod takes approximately 1.5 minutes for image generation, compared to 3 minutes for the default pod.

The tutorial aims to guide users on how to install TensorRT on RunPod and Unix systems, as RunPod uses the Ubuntu operating system.

The current setup uses older Nvidia drivers, which limits the speed potential of TensorRT on RunPod.

Despite the older drivers, the TensorRT pod is shown to be 66% faster in image generation than the regular pod.

Image quality between the regular pod and TensorRT pod is confirmed to be the same, with only minor differences possibly due to xFormers.

The tutorial provides a detailed guide on how to install TensorRT on Unix systems and how to apply it on RunPod for improved performance.

The process of creating a new pod on RunPod is demonstrated, including selecting the RTX 3090 for testing speed differences.

Instructions are given on how to download necessary files from a Patreon post to facilitate the TensorRT installation process.

The relauncher.py file is replaced to prevent unwanted relaunching of the Web UI instance upon restart.

The installation of TensorRT and its dependencies is shown, with a focus on the specific commands and steps required.

The current Nvidia driver version on RunPod is identified as outdated, with a suggestion to upgrade for better performance.

Additional tips are provided for optimizing TensorRT performance, including using high-resolution fixes and following specific instructions.

The Web UI is shown starting up with the necessary packages installed, and the process of connecting to it is demonstrated.

The speed increase with TensorRT is quantified, showing a 69% improvement on RTX 3090 with an old Nvidia driver.

The tutorial encourages viewers to watch a full tutorial for more details on TensorRT and its advanced setup options.

The potential for even better performance with future driver upgrades and active development of TensorRT is highlighted.

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