A1111: nVidia TensorRT Extension for Stable Diffusion (Tutorial)

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.

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.

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.

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.

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.

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.

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.