【爆速！】TensorRTのstable diffusion webuiへのインストール方法と効果的な使い方

AI is in wonderland
19 Oct 202319:38

TLDRIn this video, Alice from AI's Wonderland introduces the integration of TensorRT with stable diffusion webUI, developed by NVIDIA to optimize deep learning models for faster performance. Yuki explains the process of installing TensorRT on an RTX4090 GPU and using it with the dev branch of stable diffusion webUI. The video demonstrates how to export TensorRT engines for different image sizes and models, and compares image generation speeds with and without TensorRT. It shows that TensorRT can significantly increase speed, with up to 1.5 times faster iteration rates, and also reduce VRAM consumption. However, using high resolution fixes with TensorRT may increase generation time. The video concludes by noting that the initial complex installation process will be simplified in the future, and encourages viewers to subscribe and like the video.

Takeaways

  • 🚀 TensorRT, developed by NVIDIA, is a high-performance deep learning inference engine that can now be used with stable diffusion webUI to significantly increase image generation speed.
  • ⚠️ The operation of TensorRT with stable diffusion webUI may still be unstable, so it's recommended to wait for further stability before use unless you're eager to try it out.
  • 💻 TensorRT is specifically designed for NVIDIA's GPU, and it won't work with other types of GPUs. The demonstration used an RTX4090.
  • 📁 To install TensorRT, you need to install a new stable diffusion webUI in a new folder and switch to the development branch (dev) from the master branch.
  • 🔄 After switching to the dev branch, you should edit the batch file to include commands for xformers and other model calls.
  • 🛠️ Before installing TensorRT, ensure to delete any previously installed TensorRT folder and the venv folder of stable diffusion webUI to start fresh.
  • 📥 To integrate TensorRT, you must activate venv, update pip, install NVIDIA's cuDNN, install the development version of TensorRT, and then uninstall the initial cuDNN.
  • 🔗 After preparing the environment, you can install TensorRT from the URL provided in the stable diffusion webUI's Extensions tab.
  • 🔍 It's necessary to export the TensorRT engine to the desired checkpoint for the specific image sizes you plan to generate.
  • ⏱️ Using TensorRT can speed up image generation to about 1.5 times faster than normal mode, with the iteration speed increasing from around 30 to 51.12 for RTX4090.
  • 🔧 When using high-resolution fixes with TensorRT, the image generation time may increase, suggesting that there might be room for future improvements in this area.
  • 📈 TensorRT also helps reduce VRAM consumption during the image generation process, which is beneficial for systems with limited video memory.

Q & A

  • What is TensorRT and how is it related to stable diffusion webUI?

    -TensorRT is a high-performance deep learning inference engine developed by NVIDIA that optimizes deep learning models to run quickly. It is related to stable diffusion webUI as it can be used with it to significantly increase the speed of image generation.

  • Why might one consider waiting before using TensorRT with stable diffusion webUI?

    -The operation with TensorRT and stable diffusion webUI may still be unstable. Therefore, if one is not in a hurry, it is recommended to wait a little longer before using it to ensure more stability and reliability.

  • What GPU is required to use TensorRT?

    -TensorRT is an engine for NVIDIA's GPU, so it cannot be used with other GPUs. The environment used in the script is an RTX4090.

  • How does one install the stable diffusion webUI for use with TensorRT?

    -To install the stable diffusion webUI, one needs to create a new folder under the C drive, open a command prompt from this folder, and use the code from automatic1111's GitHub page followed by 'git clone'. Then, change to the dev branch using the commit hash from the stable diffusion commit hash page.

  • What is the purpose of installing the development version of NVIDIA's TensorRT?

    -The development version of NVIDIA's TensorRT is installed to optimize the performance of deep learning models for inference, which is particularly useful for speeding up image generation with stable diffusion webUI.

  • How does exporting the TensorRT engine to the checkpoint work?

    -To export the TensorRT engine to the checkpoint, one needs to select the model they want to incorporate the engine into from the stable diffusion checkpoint. Different TensorRT engines are required for each image size, so one would export engines for various sizes such as 512x512, 1024x1024, and others as needed.

  • What is the impact of using TensorRT on image generation speed?

    -Using TensorRT can make image generation significantly faster. For instance, with an RTX4090, the number of iterations per second can reach around 51.12 compared to the usual 30, making the process about 1.5 times faster.

  • How does TensorRT affect VRAM consumption during image generation?

    -TensorRT seems to reduce VRAM consumption compared to normal mode. For example, when generating a 512x512 image and upscaling it to 2x with Hi-Res Fix using TensorRT, the VRAM usage was 5.04GB, whereas with SD Unet set to none, it was 6.19GB.

  • What is the recommended method for upscaling images when using TensorRT?

    -The future of image upscaling with TensorRT might be better served by using the img to img method rather than using high resolution fix, as it can be faster and more efficient.

  • What is SDXL and how does it relate to TensorRT?

    -SDXL is a feature that is currently only available on the dev branch of stable diffusion webUI. It allows for the export of a 1024x1024 TensorRT engine for use with specific base models, which can significantly increase the speed of image generation.

  • What are the future improvements expected for TensorRT integration with stable diffusion webUI?

    -The initial installation process for TensorRT is expected to be improved for ease of use. Additionally, there may be future enhancements to the way high resolution fixes are handled to improve image generation times, and potential optimizations for VRAM usage based on individual GPU capabilities.

Outlines

00:00

🚀 Introduction to TensorRT with Stable Diffusion WebUI

Alice from AI's Wonderland introduces the integration of NVIDIA's TensorRT, a high-performance deep learning inference engine, with the stable diffusion webUI. Yuki explains that TensorRT optimizes models for faster image generation, but warns of potential instability and recommends waiting for stabilization unless eager to try. The video provides a step-by-step guide on installing TensorRT, switching to the dev branch of stable diffusion, and utilizing it for accelerated image generation with the RTX4090 GPU. The process includes creating a new folder, installing the webUI, switching to the dev branch, and configuring the environment for TensorRT.

05:01

📈 TensorRT Installation and Image Generation Speed Test

The video continues with the detailed process of installing TensorRT, including uninstalling the initial cuDNN and installing the development version of TensorRT. After installation, the user is guided to install an extension from a GitHub URL and verify the successful integration by checking for a new TensorRT tab in the webUI. Yuki demonstrates exporting TensorRT engines for different image sizes and configuring the webUI for using TensorRT with various models. A comparison of image generation times with and without TensorRT highlights the significant speed increase, with up to 51.12 iterations per second observed for RTX4090.

10:10

🔍 Comparing Image Quality and VRAM Usage

Yuki investigates the consistency of image generation using TensorRT against normal mode with a fixed seed value, finding no discernible difference in image quality. The video also compares VRAM consumption between TensorRT and normal mode, noting that TensorRT is more efficient. A test using Hi-Res Fix for upscaling is performed, but it's observed that TensorRT may slow down the process at high resolutions. The video discusses the potential for future improvements and the option to export a dynamic TensorRT engine for flexibility in image sizes. Yuki also touches on the limitations when exporting TensorRT engines for certain models and the need for future optimization.

15:10

🔧 Exploring SDXL and Future Prospects

The final part of the video explores the capabilities of SDXL on the dev branch, demonstrating the export of a TensorRT engine for SDXL base models. Yuki compares the image generation speed of normal mode versus TensorRT with and without a refiner, showing that TensorRT can be nearly twice as fast. The video concludes with a look at upscaling using the img to img method and a prediction that this approach might offer better performance in the future. The presenter expresses optimism for the forthcoming improvements in the installation process and the integration of TensorRT into常用的 (commonly used) webUI. The video ends with a call to action for viewers to subscribe and like the video.

Mindmap

Keywords

💡TensorRT

TensorRTは、NVIDIAが開発した高性能な深層学習推論エンジンです。これにより、Stable Diffusionのような画像生成の速度を大幅に向上させることができます。ビデオでは、このTensorRTを使ってStable DiffusionのWebUIで画像生成を行う方法について説明しています。

💡Stable Diffusion WebUI

Stable Diffusion WebUIは、Stable Diffusionのためのユーザーインターフェースです。ビデオでは、TensorRTをStable Diffusion WebUIにインストールする方法やその効果的な使い方について紹介しています。

💡SDXL

SDXLは、Stable Diffusionの開発ブランチであり、実験的な機能が含まれています。ビデオでは、SDXLのブランチを使用してTensorRTを導入する手順が説明されています。

💡RTX4090

RTX4090は、NVIDIAの高性能GPUの一つです。ビデオでは、このGPUを使用してTensorRTのパフォーマンスを示しています。

💡深層学習

深層学習は、人工知能の一分野で、ニュートラルネットワークを使ってデータを学習します。TensorRTは、深層学習モデルを最適化して高速に動作させるためのエンジンです。

💡イメージ生成

イメージ生成は、画像を生成するプロセスです。ビデオでは、TensorRTを使用してStable Diffusion WebUIで画像を生成する方法が説明されています。

💡高解像度修正

高解像度修正（Hi-Res Fix）は、Stable Diffusion WebUIで使用される機能で、低解像度の画像を高解像度に変換するプロセスです。ビデオでは、TensorRTを使って高解像度修正による画像生成の速度を比較しています。

💡SD Unet

SD Unetは、Stable Diffusionの構造の一部です。ビデオでは、TensorRTを使用してSD Unetを活用する方法が説明されています。

💡Dynamicモード

Dynamicモードは、TensorRTのプリセットの一つで、異なるサイズやバッチサイズに対応できるようにします。ビデオでは、このモードを使用して、さまざまなサイズの画像を生成する方法が説明されています。

💡バッチ処理

バッチ処理は、一度に複数の画像を生成するプロセスです。ビデオでは、TensorRTを使用して、バッチ処理による画像生成の速度を比較しています。

Highlights

TensorRT, a high-performance deep learning inference engine developed by NVIDIA, can now be used with stable diffusion webUI.

TensorRT optimizes deep learning models for faster image generation with stable diffusion.

The operation of TensorRT with stable diffusion may still be unstable, suggesting users to wait before use unless they want to try it immediately.

TensorRT is exclusive to NVIDIA's GPU, and the demonstration uses an RTX4090.

A new stable diffusion webUI and dev branch are installed to use TensorRT without affecting other functions.

The dev branch is a development branch, indicating ongoing work.

The process of installing the webUI and switching to the dev branch is detailed, including commands and steps.

TensorRT requires the installation of NVIDIA's cuDNN and the development version of TensorRT, followed by uninstalling the initial cuDNN.

After installation, the WEB UI user bat is used to start the webUI, which may take time due to model installation.

If TensorRT was previously installed incorrectly, specific steps are provided to restore the system.

Various commands are entered into the command prompt for the final installation of TensorRT.

The TensorRT engine can be exported to different models and image sizes, with specific instructions provided.

The image generation speed is significantly faster with TensorRT, reaching up to 51.12 iterations per second for RTX4090.

The use of TensorRT with high-resolution fixes may increase image generation time, suggesting potential areas for future improvement.

TensorRT also reduces VRAM consumption compared to standard image generation processes.

An alternative upscaling method using 'img to img' is suggested for faster results without tiling.

SDXL, available only on the dev branch, demonstrates even faster image generation when used with TensorRT.

The future of image generation with TensorRT is promising, with potential for further speed and efficiency improvements.

The video concludes with a call to action for viewers to subscribe and like the video for more updates.