Double Your Stable Diffusion Inference Speed with RTX Acceleration TensorRT: A Comprehensive Guide

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24 Oct 202341:54

TLDRNVIDIA's latest driver release includes a remarkable extension for the Stable Diffusion Automatic1111 Web UI interface, offering significant speed improvements. This tutorial demonstrates the installation and use of RTX Acceleration with TensorRT, highlighting the potential for up to 70% faster processing times. The guide covers both automatic and manual installation methods, discusses the necessity of updating to the latest Game Ready Drivers and cuDNN files, and explains the process of generating TensorRT versions of various models, including custom-trained models and LoRAs. Despite acknowledging some ongoing stability issues, the video emphasizes the substantial performance enhancements enabled by this extension.

Takeaways

  • ๐Ÿš€ NVIDIA has released a new driver and an extension for RTX Acceleration with TensorRT, significantly improving speed in Stable Diffusion models.
  • ๐Ÿ”ง The tutorial demonstrates how to install and use the RTX Acceleration extension step by step.
  • ๐ŸŒŸ Users can expect up to 70% speed improvements with the new extension, especially when using RTX 3090 TI.
  • ๐Ÿ“ˆ Performance comparisons show substantial speed-ups for various models, including SD 1.5 based model and SDXL.
  • ๐Ÿ”„ The extension uses slightly more VRAM but offers a considerable performance boost.
  • ๐Ÿ› ๏ธ The tutorial covers both automatic and manual installation methods for the extension.
  • ๐Ÿ“‹ It is recommended to install the latest Game Ready Drivers from NVIDIA for optimal performance.
  • ๐Ÿ”„ There are some known issues with the extension, but developers are actively working on them.
  • ๐ŸŽจ The extension also supports the use of custom models and LoRAs (Low-Rank Adaptations) for personalized image generation.
  • ๐Ÿ”— Links to the GitHub readme file, installation scripts, and support platforms are provided in the video description for further assistance.

Q & A

  • What is the main announcement in the transcript?

    -The main announcement is the release of NVIDIA's newest driver along with an extension made for the Stable Diffusion Automatic1111 Web UI interface, which provides significant speed improvements.

  • What does RTX Acceleration with TensorRT do?

    -RTX Acceleration with TensorRT enhances the performance of the Stable Diffusion Web UI by providing up to 2 times speed up with the GeForce RTX 4090.

  • Why should one follow the tutorial in the transcript?

    -Following the tutorial is beneficial for those who want to achieve up to 70% speed improvements in their Stable Diffusion models.

  • What are the speed improvements observed with RTX 3090 TI using TensorRT?

    -The speed improvements observed with RTX 3090 TI using TensorRT range from 7.94 to 13.75 seconds per image for SDXL and from 3.61 to 6.04 seconds per image for SDXL in relaxed TensorRT settings.

  • What is the role of the Github readme file mentioned in the transcript?

    -The Github readme file provides detailed instructions and links necessary for the installation of the NVIDIA extension and the Stable Diffusion Web UI TensorRT extension.

  • How does the automatic installation process work for the NVIDIA extension?

    -The automatic installation process involves using an installer that automatically downloads and installs the necessary files, including the latest cuDNN file, into the correct folders without requiring manual intervention.

  • What are the steps for manual installation of the NVIDIA extension?

    -For manual installation, one needs to clone the extension repository into the extension folder, install the latest cuDNN version from NVIDIA's official website, and ensure that the correct files are placed in the appropriate directories.

  • What is the significance of the development branch of the Automatic1111 Web UI?

    -The development branch of the Automatic1111 Web UI is where active development and updates are taking place, including support for TensorRT and SDXL models. It is necessary to switch to this branch for certain features to work correctly.

  • How does one generate TensorRT versions of the models?

    -To generate TensorRT versions of the models, one must go to the TensorRT tab in the Web UI, select the appropriate model and VAE, set the desired batch size and resolution, and then export the engine to create the TensorRT file.

  • What is the role of the model.json file in the TensorRT process?

    -The model.json file is crucial as it contains the necessary information for the Web UI to use the TensorRT models. Without this file, the TensorRT models will not function properly.

  • What are the implications of using custom extensions with TensorRT?

    -Custom extensions may conflict with the TensorRT process or the Web UI, potentially causing errors. If issues arise, it is recommended to perform a fresh installation of the Automatic1111 Web UI and then generate the TensorRT files.

Outlines

00:00

๐Ÿš€ Introduction to RTX Acceleration with TensorRT

The paragraph introduces the release of NVIDIA's newest driver and an extension for the Stable, Diffusion Automatic1111 Web UI interface. It highlights the significant speed improvements achieved with the GeForce RTX 4090, up to 2 times faster, and promises a tutorial on installing and using this extension. The importance of following the video is emphasized for those seeking up to 70% speed improvements. Comparisons are provided to demonstrate the performance gains, especially when using the RTX 3090 TI. The video also mentions the use of more VRAM but justifies it with the substantial speed improvements. The tutorial includes a Github readme file and encourages viewers to star the repository, follow the creator on various platforms, and subscribe to the channel.

05:01

๐Ÿ› ๏ธ Installing the TensorRT Extension and prerequisites

This paragraph discusses the installation process of the TensorRT extension, starting with an automatic installation method using a .bat file for fresh installations of the Automatic1111 Web UI. It also touches on the necessity of having the latest Game Ready driver from NVIDIA for optimal performance. The paragraph compares the performance between different versions of the NVIDIA driver on the RTX 3090 TI GPU and suggests that there are no significant speed changes observed. The manual installation process is also outlined, which includes using command line arguments and cloning the extension into the extension folder. The importance of upgrading to the latest version of cuDNN for manual installations is stressed, with detailed instructions provided on how to do so.

10:05

๐ŸŽจ Generating TensorRT Models and Addressing Stability Issues

The paragraph explains the process of generating TensorRT versions of the models to be used, including the Realistic Vision version 5.1 and the best VAE files. It discusses the speed of generating images without TensorRT and the expected improvements with it. The paragraph also addresses some issues encountered with the extension, noting that it is still under active development and not yet fully stable. The manual installation process is reiterated, with a focus on the steps for non-Patreon supporters. The paragraph also provides guidance on setting up the Web UI to use the generated TensorRT models and troubleshooting tips for common errors.

15:09

๐Ÿ”„ Generating and Using TensorRT Files

This section details the process of generating TensorRT files for different models, including the Realistic Vision version 5.1 and SDXL base 1.0. It explains the importance of the model.json file for using the TensorRT files and the process of updating this file each time a new TensorRT file is generated. The paragraph also discusses the generation of ONNX files, the use of command line arguments to avoid errors during TensorRT file generation, and the suggestion to use xFormers. It highlights the development branch of the Automatic1111 Web UI and the necessity of switching to it for certain functionalities, such as SDXL TensorRT. The paragraph concludes with a demonstration of the speed improvements achieved with TensorRT and the successful generation of images.

20:13

๐ŸŒŸ Speed Comparisons and Patreon Support

The paragraph presents a speed comparison between generating images with and without TensorRT, showcasing significant improvements in speed. It also discusses the generation of TensorRT for SDXL and the use of pre-compiled models available to Patreon supporters. The paragraph emphasizes the potential for even greater performance with static shapes and the importance of dynamic ranges for varying resolutions and batch sizes. It also touches on the potential for using TensorRT with custom models and LoRAs, and the process for generating TensorRT versions for these. The paragraph concludes with a call to action for viewers to support the creator through Patreon, Github, and other platforms, and provides a brief overview of the creator's activities and offerings.

25:14

๐ŸŽญ Training Custom Models and Combining with LoRAs

This paragraph focuses on the creator's personal experience training a custom model and the intention behind using a low-quality dataset for SDXL DreamBooth. It discusses the potential for achieving impressive results even with subpar data, and the expectation of better outcomes with higher quality datasets. The paragraph then explores the combination of this custom model with LoRAs, specifically the SDXL Pixel Art XL LoRA from CivitAI. It outlines the process of downloading, installing, and using LoRAs with the model, and the expected speed improvements. However, it also acknowledges some issues encountered with the LoRA integration, particularly with the SDXL model, and the need to report these bugs to the developers for future fixes.

30:15

๐Ÿ”ง Troubleshooting and Optimizing LoRA Integration

The paragraph delves into troubleshooting the integration of LoRAs with TensorRT, particularly focusing on the SDXL model. It describes the process of generating TensorRT profiles for the SDXL base model and the subsequent generation of the TensorRT LoRA. The paragraph highlights the necessity of restarting the Web UI after adding new LoRAs and the potential need to switch between different branches of the Web UI to resolve errors. It also discusses the manual selection of LoRA TensorRT for effective use and the observed differences in output. The paragraph concludes with a note on the ongoing development and potential bugs, and the creator's commitment to reporting these issues for resolution.

35:16

๐Ÿค Encouraging Community Support and Collaboration

In the final paragraph, the creator expresses gratitude for the community's support and encourages viewers to engage with the content by starring the Github repository, forking it, and watching it. The creator also highlights the importance of Patreon support and provides a range of platforms where viewers can follow and support the creator's work. The paragraph mentions the creator's full-time dedication to AI and various activities, including consultation, side projects, and model training. It concludes with an invitation for collaboration and a promise of future tutorials.

Mindmap

Keywords

๐Ÿ’กNVIDIA

NVIDIA is a technology company known for its graphics processing units (GPUs) and artificial intelligence research. In the context of the video, NVIDIA has released a new driver and an extension for the Stable Diffusion Automatic Web UI interface, which is a significant development for users looking to improve their GPU's performance in AI tasks.

๐Ÿ’กRTX Acceleration with TensorRT

RTX Acceleration with TensorRT is a technology developed by NVIDIA that leverages the power of RTX GPUs to accelerate deep learning applications. TensorRT is a high-performance deep learning inference engine that optimizes neural network models for deployment. The video highlights the use of this technology to achieve significant speed improvements in the Stable Diffusion model.

๐Ÿ’กStable Diffusion

Stable Diffusion is an AI model used for generating images from textual descriptions. It is known for its ability to produce high-quality, realistic images. In the video, the presenter discusses the installation and use of an extension that enhances the performance of Stable Diffusion by leveraging NVIDIA's RTX technology.

๐Ÿ’กGeForce RTX 4090

The GeForce RTX 4090 is a high-end graphics card developed by NVIDIA, known for its powerful performance in gaming and professional applications, including AI and machine learning tasks. The video script mentions the significant speed improvements achieved with this GPU when using the RTX Acceleration with TensorRT.

๐Ÿ’กAutomatic Web UI

Automatic Web UI refers to the user interface for the Stable Diffusion model, which allows users to generate images through a web-based platform. The video provides a tutorial on how to install and use an extension to enhance the performance of this interface with NVIDIA's RTX technology.

๐Ÿ’กcuDNN

cuDNN is NVIDIA's GPU-accelerated library for deep neural networks. It provides highly optimized primitives for deep learning, which are crucial for the performance of AI models like Stable Diffusion. The video emphasizes the importance of having the latest version of cuDNN for the installation and operation of the RTX Acceleration with TensorRT extension.

๐Ÿ’กTensorRT

TensorRT is NVIDIA's platform for high-performance deep learning inference. It optimizes neural network models for deployment on production systems, providing faster and more efficient inference. In the video, the presenter discusses the installation of TensorRT to enhance the performance of the Stable Diffusion model.

๐Ÿ’กPatreon

Patreon is a platform that allows creators to receive financial support from their fans or patrons. In the context of the video, the creator mentions Patreon as a way for viewers to support their work, which includes tutorials, AI model training, and development of extensions for AI applications.

๐Ÿ’กGitHub

GitHub is a web-based platform for version control and collaboration that allows developers to work on projects and share code. In the video, the presenter provides a link to a GitHub readme file, which contains important information and links for the installation of the RTX Acceleration with TensorRT extension.

๐Ÿ’กVAE

VAE stands for Variational Autoencoder, which is a type of generative AI model used for unsupervised learning. In the context of the video, the presenter discusses selecting the best VAE for different versions of the Stable Diffusion model to improve the quality of generated images.

๐Ÿ’กSDXL

SDXL refers to a specific version or variant of the Stable Diffusion model. In the video, the presenter provides details on how to install and use the RTX Acceleration with TensorRT extension for this model, highlighting the significant speed improvements that can be achieved.

Highlights

NVIDIA has released a new driver and an extension for Stable Diffusion Automatic Web UI interface.

The RTX Acceleration with TensorRT can provide up to 2 times speed up with a GeForce RTX 4090.

The tutorial demonstrates how to install the RTX Acceleration extension and use it step by step.

Up to 70% speed improvements can be achieved with the extension.

The extension is developed by NVIDIA and is officially part of the Stable Diffusion Automatic Web UI Github repository.

The tutorial covers both fresh installation and manual installation of the extension.

The latest Game Ready Drivers from NVIDIA are recommended for optimal performance.

The cuDNN files need to be upgraded to the latest version for the extension to work correctly.

The extension is still in development and not fully stable yet.

The tutorial shows how to generate TensorRT versions of the models for improved speed.

The development branch of the Automatic Web UI needs to be used for SDXL TensorRT.

The tutorial provides a comparison of speeds with and without TensorRT for different models.

The tutorial also covers the installation of the After Detailer extension for face inpainting.

Custom LoRAs and DreamBooth training models can be used with TensorRT for enhanced results.

The tutorial includes troubleshooting tips and solutions for common errors encountered during the installation process.