A1111: nVidia TensorRT Extension for Stable Diffusion (Tutorial)

ControlAltAI
10 Dec 202325:40

TLDRIn this video, Seth demonstrates how to optimize the performance of the AI model, Stable Diffusion (SDXL), by generating custom tensor RT engines on an Nvidia RTX GPU. He emphasizes the importance of using the correct installation and configuration methods to avoid errors, and provides a step-by-step guide for installing the tensor RT extension, training profile engines, and setting up the environment for optimal image generation. The tutorial also covers the limitations of the extension and offers tips for troubleshooting common issues.

Takeaways

  • 🚀 The video is a tutorial on optimizing the performance of Stable Diffusion (SD) using custom tensor RT engines on an Nvidia RTX GPU.
  • 🌟 The process is not recommended for beginners as it involves early-stage methods and potential compatibility issues with certain extensions.
  • 🛠️ The tutorial assumes the user is already familiar with SD and has a separate manual install for testing the tensor RT extension.
  • 💡 Tensor cores in RTX GPUs are designed for mixed precision computing, accelerating deep learning and AI applications.
  • 📏 The extension allows for training a predefined checkpoint with specific resolutions and batch processes to create a profile engine for faster image generation.
  • 🔧 The tutorial provides detailed steps for installing and configuring the tensor RT extension, including switching to the dev branch and updating the web UI.
  • 📂 The dev branch and tensor RT extension are kept separate from the existing workflow to avoid conflicts.
  • 🔗 The tutorial includes links for downloading necessary software and provides commands for installation and environment setup.
  • 🛑 The importance of using the correct Python version and upgrading pip is emphasized for the tutorial.
  • 🌐 The Nvidia control panel settings are adjusted to allow the GPU to utilize system RAM for training RT engines, which is VRAM intensive.
  • 🎨 The tutorial concludes with a test of the optimized setup, comparing generation times with and without the tensor RT profile engine.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is about improving the performance of Stable Diffusion (SD) by generating custom tensor RT engines on an Nvidia RTX GPU.

  • What is the significance of using tensor RT engines?

    -Tensor RT engines are designed to optimize the performance of deep learning applications by utilizing tensor cores in Nvidia RTX GPUs, which accelerate mixed precision computing and can significantly reduce the time taken for image generation in SD.

  • Why is the tutorial not recommended for beginners?

    -The tutorial is not recommended for beginners because it deals with early-stage methods and requires a separate manual installation, which may involve complexities that are not suitable for those new to Stable Diffusion.

  • What are some limitations of the tensor RT extension?

    -Some limitations include incompatibility with certain extensions like ControlNet, lack of IP or text-to-image adapters, and the inability to train LURAS for SDXL. Additionally, the extension is only supported via the developer branch.

  • What is the recommended Python version for this tutorial?

    -The recommended Python version for this tutorial is 3.10.1.

  • How does the tensor RT extension reduce overheads during image generation?

    -The tensor RT extension reduces overheads by allowing the training of a predefined checkpoint with specific resolutions and batch processes to create a profile engine. Once this engine is loaded into the GPU's VRAM, it minimizes the extra computational overhead during image generation.

  • What is the recommended way to handle system memory fallback policy in Nvidia control panel for training RT engines?

    -The recommended setting is to select 'Prefer Fallback' in the system memory fallback policy, which allows the GPU to utilize system RAM when running Python applications, helping to prevent crashes due to insufficient VRAM.

  • What are the optimal height and width values for SDXL in the RT exporter settings?

    -The optimal height and width values for SDXL in the RT exporter settings are either 768 or 1024.

  • How does the dynamic option work in the RT exporter settings?

    -The dynamic option trains on a range of settings, allowing generation between a set minimum and maximum batch size. It is more flexible than the static option but requires a separate profile engine for each specific upscale resolution.

  • What is the recommended approach for upscaling images using the RT profile engine?

    -For upscaling, it is recommended to train two profile engines: one for the base resolution and another for the upscale resolution. The first generation will be slower as it loads the engine into VRAM, but subsequent generations will be faster.

  • What was the result of the speed test comparing non-tensor RT and tensor RT profile engines?

    -Without using tensor RT, image generation took about 59.4 seconds, while with the tensor RT profile engine, it took approximately 43.2 seconds, showing a significant improvement in speed.

Outlines

00:00

🚀 Introduction to Custom Tensor RT Engines for SDXL

The video begins with Seth introducing the topic of optimizing the performance of Stable Diffusion 1.1.1 (SDXL) by generating custom tensor RT engines on an Nvidia RTX GPU. He emphasizes that this tutorial is not for beginners and requires a separate manual install. Seth also thanks the members who joined the channel and provides a disclaimer about the early stages of the method being discussed. He explains the limitations of the extension, including the lack of support for control net and the absence of IP or text-to-image adapters in the workflow. Seth mentions that the tutorial will cover two versions of SDXL: the existing install and the developer branch for the tensor RT extension.

05:03

📋 Setting Up the Development Environment for Tensor RT

Seth guides viewers through the process of setting up the development environment for the Tensor RT extension. He instructs on how to switch to the dev branch of SDXL, install the Tensor RT extension, and handle potential errors. He advises on the importance of using the command prompt for the process and provides troubleshooting tips, such as deleting the VINV folder and reinstalling the extension. Seth also discusses the necessity of upgrading Python and Git to their latest versions and provides a link for easy access. He emphasizes the need to activate the virtual environment correctly to avoid conflicts with the main SDXL UI.

10:08

🛠️ Installing and Configuring Tensor RT Extension

In this section, Seth details the steps for installing the Tensor RT extension, including uninstalling unnecessary packages and installing the required runtime. He addresses a common compatibility error and reassures viewers that the extension will downgrade to the correct version during installation. Seth explains how to test the extension and reinstall it if necessary. He also covers the configuration of the virtual environment and the importance of using the correct command prompt for the process. Seth provides instructions on how to fix the 'entry point not found' error and how to ensure the Tensor RT extension is installed without errors.

15:11

💻 Optimizing GPU Usage and Training RT Engines for SDXL

Seth discusses the optimization of GPU usage when training RT engines for SDXL. He explains the benefits of using the system memory fallback policy in the Nvidia control panel and how it can help when training with high VRAM requirements. Seth shares his experience with training on 24 GB of VRAM and the challenges faced, including crashes and memory errors. He provides a solution that involves using the system's RAM for training, which successfully mitigates crashes and memory issues.

20:13

🎨 Customizing Profile Engines and Settings for SDXL

Seth dives into customizing profile engines and settings for SDXL. He outlines the differences between static and dynamic options for the RT exporter and provides a custom preset for various image formats and upscaling needs. Seth explains the importance of selecting the correct batch size, height, width, and token count for optimal performance. He also discusses the challenges of upscaling and the need to train two profile engines for different resolutions. Seth shares his testing experiences, including the impact of prompt token count on generation errors and the process of exporting the ONNX file to the Tensor RT engine profile.

25:13

🏁 Wrapping Up the Tensor RT Installation and Testing

In the final paragraph, Seth wraps up the installation process and conducts a quick test of the Tensor RT profile engine. He explains how to bake in non-SDXL models like the detailer and enhance the image quality. Seth provides a demonstration of the speed improvement with the RT profile engine and compares it to the non-Tensor RT generation time. He concludes the tutorial by stating that future releases may fix the errors discussed but the core understanding of Tensor RT and profile generation settings will remain relevant.

🎶 Tutorial Conclusion and Sign-off

The video concludes with Seth signing off and thanking viewers for watching. He plays a short music clip as a sign of the end of the tutorial, leaving viewers with a positive note and looking forward to the next video.

Mindmap

Keywords

💡Stable Diffusion

Stable Diffusion is an AI model used for generating images from text descriptions. In the context of the video, it is the primary tool being discussed and optimized for faster image generation. The script mentions 'stable diffusion' in relation to the tutorial not being recommended for beginners and the various extensions and modifications being made to enhance its performance.

💡Tensor RT

Tensor RT is an NVIDIA software library for optimizing and deploying deep learning models. It is used to create custom tensor engines that can improve the performance of AI applications by leveraging the tensor cores in RTX GPUs. In the video, the creator explains how to generate these engines for Stable Diffusion, which is an AI model for image generation.

💡Nvidia RTX GPU

Nvidia RTX GPUs are high-performance graphics processing units designed by NVIDIA, featuring specialized cores called tensor cores that accelerate AI and deep learning tasks. In the video, the creator talks about leveraging the capabilities of these GPUs to speed up the image generation process in Stable Diffusion.

💡Performance Optimization

Performance optimization refers to the process of enhancing the efficiency and speed of a software application or system. In the video, the creator focuses on optimizing the performance of Stable Diffusion by using Tensor RT engines and other modifications to reduce image generation time.

💡Extensions

In the context of the video, extensions refer to additional software components or plugins that can be added to the Stable Diffusion application to enhance or modify its functionality. The creator discusses which extensions are compatible and how to install the Tensor RT extension.

💡Virtual Environment

A virtual environment is a isolated space within a computer's operating system where applications and their dependencies can be installed and managed without affecting the system as a whole. In the video, the creator instructs how to create a virtual environment for the Stable Diffusion application and its extensions.

💡Checkpoints

In the context of AI and machine learning, checkpoints are points during the training process where the model's state is saved. These can be used to resume training or to initialize models for inference. In the video, the creator talks about training and using checkpoints with Tensor RT engines for image generation.

💡VRAM

Video RAM (VRAM) is the memory used by graphics cards to store图像 data for rendering. In the video, the creator discusses the importance of having sufficient VRAM for training RT engines for Stable Diffusion, as it is an intensive process.

💡Python

Python is a high-level programming language known for its readability and ease of use. In the video, Python is the language used to run the Stable Diffusion application and its extensions, including the Tensor RT engine.

💡GitHub

GitHub is a web-based platform that provides version control and collaboration features for software development. In the video, the creator directs the viewer to the GitHub page of the Stable Diffusion web UI to download necessary files for the tutorial.

💡Command Prompt

The command prompt is a text-based interface in Windows operating systems that allows users to interact with the computer by entering commands. In the video, the creator uses the command prompt to switch branches, install extensions, and perform other necessary actions for setting up the development environment.

Highlights

Seth introduces the tutorial on improving the performance of stable diffusion (SD) by generating custom tensor RT engines on an Nvidia RTX GPU.

The tutorial is not recommended for beginners as it involves early-stage methods and requires a separate manual installation.

Many extensions are not supported in the workflow when enabling the tensor RT extension, and control net is not compatible.

The tutorial involves creating a developer branch for the tensor RT extension, ensuring it doesn't interfere with the existing workflow.

Tensor cores in Nvidia RTX GPUs are designed for mixed precision computing, accelerating deep learning and AI applications.

The extension allows training a predefined checkpoint with specific resolutions and batch processes to create a profile engine for faster image generation.

The tutorial emphasizes the importance of having Python and Git installed, with specific version recommendations.

Instructions are provided for downloading and installing the necessary components from the web UI GitHub page.

The process of switching to the dev branch of automatic 1111 and applying the tensor RT extension is detailed, including troubleshooting steps.

The tutorial explains how to train RT engines for SDXL, noting that it is VRAM intensive and may require using system RAM for higher resolutions.

Nvidia control panel settings are discussed, including configuring system memory fallback policy for efficient GPU usage.

The importance of selecting the correct checkpoints and VAE settings for the engines is emphasized for optimal performance.

The RT exporter settings are explored, including custom presets for different image formats and resolutions.

The tutorial provides insights into the limitations of static and dynamic options in the RT exporter and how they affect image generation.

The impact of batch size, height, width, and token count on the performance and VRAM usage of the profile engine is discussed.

The process of exporting the ONNX file to the TensorRT engine profile is described, including potential issues and solutions.

The tutorial concludes with a test of the improved generation speed using the RT profile engine, demonstrating its practical application.