【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

In this video, Alice from AI's Wonderland introduces the integration of NVIDIA's TensorRT with the stable diffusion webUI, which is expected to significantly increase the speed of image generation. Yuki explains that while the operation may be unstable, those eager to try it can follow the detailed installation steps provided. The video demonstrates the process of installing TensorRT, exporting the engine for different image sizes, and using it with various models. It also compares the speed and VRAM consumption of image generation with and without TensorRT. The results show that TensorRT can speed up image generation by approximately 1.5 times and reduce VRAM usage. The video concludes by noting that the initial installation process will be improved in the future.

Takeaways

• 🚀 TensorRT, a high-performance deep learning inference engine by NVIDIA, can now be used with stable diffusion webUI to significantly increase the speed of image generation.
• ⚠️ TensorRT is currently unstable and may not be suitable for everyone; it's recommended to wait for further stability before use.
• 📦 To use TensorRT, you need an NVIDIA GPU, and the guide uses an RTX4090. The stable diffusion webUI is installed in a new folder on the C drive.
• 🔄 The dev branch of stable diffusion webUI is used for TensorRT, which is a development branch and may have features under development.
• 💾 The TensorRT engine needs to be exported to the desired stable diffusion checkpoint for different image sizes, such as 512x512, 1024x1024, and 512x768.
• 🔍 After exporting the TensorRT engine, users can select the engine in the TensorRT tab of the webUI, which will prioritize its use for image generation.
• ⏱️ Using TensorRT with stable diffusion webUI can result in image generation speeds approximately 1.5 times faster than normal mode.
• 🖼️ For high-resolution image generation, TensorRT may increase the generation time, possibly due to the way images are processed in tiles.
• 🔗 The TensorRT engine can be exported with a dynamic preset, allowing for flexibility in choosing image sizes within a range.
• 📉 TensorRT also helps in reducing VRAM consumption compared to generating images without it.
• 🔍 SDXL models can utilize TensorRT on the dev branch, but the engine could not be exported to all SDXL models in the provided environment.

Q & A

What is TensorRT and how does it relate 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 used with stable diffusion webUI to significantly increase the speed of image generation.

Why is it recommended to wait before using TensorRT with stable diffusion webUI despite its benefits?

The operation may still be unstable, so it is recommended to wait a little longer before using it to ensure more reliability, unless one is eager to try it out immediately.

What GPU is required to use TensorRT?

TensorRT is an engine for NVIDIA's GPU and cannot be used with other GPUs. The environment used in the video is an RTX4090.

How does one install the stable diffusion webUI for TensorRT?

To install the stable diffusion webUI for TensorRT, one must create a new folder under the C drive, install the webUI normally, switch to the dev branch using a specific commit hash, and then follow additional steps to prepare the environment for TensorRT.

Why is it necessary to use the dev branch instead of the master branch when installing TensorRT?

The dev branch is a development branch which contains the latest updates and features required for TensorRT integration. The master branch may not have these updates.

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

Using TensorRT can increase the speed of image generation by approximately 1.5 times compared to normal conditions, as demonstrated in the video with an RTX4090 GPU.

How does TensorRT affect VRAM consumption during image generation?

TensorRT seems to reduce VRAM consumption compared to normal mode, which is beneficial for systems with limited VRAM.

What are the steps to export the TensorRT engine to a specific model?

To export the TensorRT engine to a model, one must select the desired model from the stable diffusion checkpoint, choose the image size, and then perform the export process, which may take several minutes.

How does using TensorRT with high-resolution fixes impact image generation time?

Using TensorRT with high-resolution fixes may increase the image generation time compared to normal mode, suggesting that there might be room for future improvements in this area.

What is the 'Dynamic' preset in TensorRT and how does it work?

The 'Dynamic' preset in TensorRT allows for the selection of an image size between one size and one batch size, and one batch size and another batch size, with a slight increase in VRAM consumption.

What are the future prospects for image generation speed with TensorRT?

The future prospects for image generation speed with TensorRT are promising, with potential improvements in installation process, integration with more models, and optimizations for faster generation without tiling.

What is SDXL and how does TensorRT impact its performance?

SDXL is a feature available on the dev branch of stable diffusion webUI. TensorRT can be used with SDXL base models to significantly increase image generation speed, almost doubling it in some cases.

Outlines

00:00

🚀 Introduction to TensorRT with Stable Diffusion WebUI

Alice from AI's Wonderland introduces the integration of TensorRT, NVIDIA's high-performance deep learning inference engine, with the stable diffusion webUI. Yuki explains that TensorRT optimizes models for faster image generation, potentially increasing speed but noting potential instability. The video provides a step-by-step guide to installing TensorRT on an RTX4090 GPU, using the dev branch of stable diffusion webUI. It also covers the process of exporting TensorRT engines for different image sizes and emphasizes the requirement of using NVIDIA's GPU for this engine.

05:01

🔍 Installing and Using TensorRT for Image Generation

The second paragraph details the process of installing TensorRT, including uninstalling the initial cuDNN and installing the development version of TensorRT. It guides viewers on how to install an extension from a GitHub URL and apply updates in the webUI. Yuki demonstrates exporting TensorRT engines for various models and image sizes, including Dreamshaper, Magic Mix, and anime bluepencil. The paragraph also includes a performance test, showing that image generation with TensorRT is significantly faster, reaching 51.12 iterations per second compared to the usual 30 for RTX4090.

10:10

📈 Comparing TensorRT Performance with Normal Mode

This section compares the performance of TensorRT with the normal mode for image generation. Yuki generates images with fixed seed values to analyze consistency and uses Hi-Res Fix to create high-resolution images. The results indicate that while TensorRT improves speed, it may slow down when using high-resolution fixes. The paragraph also discusses the Dynamic preset for exporting TensorRT engines, which allows flexibility in image size and batch size. Yuki observes that TensorRT reduces VRAM consumption and suggests potential improvements for faster image generation without tiling.

15:10

🔧 Exploring SDXL and Future Optimizations

The final paragraph explores the use of TensorRT with SDXL, a feature only available on the dev branch. Yuki demonstrates exporting the TensorRT engine for the SDXL base model and compares image generation speeds with and without a refiner. The results show that TensorRT can nearly double the speed of image generation. The video concludes with Yuki's intention to continue exploring TensorRT integration and encourages viewers to subscribe and like the video for updates.

Keywords

TensorRT

TensorRT is a high-performance deep learning inference engine developed by NVIDIA. It is designed to optimize deep learning models to run quickly on NVIDIA GPUs. In the context of this video, TensorRT is used to accelerate the image generation process with stable diffusion webUI, significantly increasing the speed of generating images, as demonstrated by the improved iterations per second during the testing phase.

stable diffusion webUI

Stable diffusion webUI is a user interface for the stable diffusion model, which is used for image generation. In the video, it is mentioned that TensorRT can now be integrated with this webUI to enhance the speed of image generation. The process of installation and the benefits of using TensorRT with stable diffusion webUI are discussed in detail, showing how it can improve the user experience by reducing the time taken to generate images.

RTX4090

RTX4090 is a high-end graphics processing unit (GPU) developed by NVIDIA. It is used in the video as the environment for demonstrating the capabilities of TensorRT with stable diffusion webUI. The use of RTX4090 highlights the potential for increased performance when using TensorRT, as it is specifically designed to take advantage of NVIDIA's advanced GPU technologies.

dev branch

The dev branch, short for development branch, refers to a version of the stable diffusion webUI that is under active development and may contain new features or improvements. In the video, the dev branch is used to install the webUI with TensorRT support, as it is necessary to use this branch for the integration of the two technologies. The dev branch is a crucial element in the process of trying out and refining new features before they are merged into the main or master branch.

cuDNN

cuDNN is NVIDIA's GPU-accelerated library for deep neural networks. It provides highly optimized primitives for deep learning, including convolution, pooling, normalization, and activation functions. In the context of the video, cuDNN is a prerequisite for installing TensorRT, and its development version is installed as part of the process. The use of cuDNN is essential for achieving the high-performance deep learning inference that TensorRT is known for.

TensorRT engine

The TensorRT engine is a term used to describe the optimized runtime environment created by TensorRT for executing deep learning models. In the video, the TensorRT engine is exported to different models, such as Dreamshaper and Magic Mix, and used to generate images at various resolutions. The engine is tailored to the specific image size and model, allowing for faster and more efficient image generation compared to using the standard models without TensorRT optimization.

SD Unet

SD Unet is a model used in the stable diffusion webUI for image generation. It is mentioned in the video as one of the models to which the TensorRT engine can be exported. The use of SD Unet with TensorRT optimization demonstrates the potential speed improvements in image generation, with the video showcasing the ability to generate images at a rate of 51.12 iterations per second, which is significantly faster than the standard rate.

Hi-Res Fix

Hi-Res Fix is a feature used in the stable diffusion webUI for upscaling and enhancing the quality of generated images. It is mentioned in the video as a method for creating high-resolution images, such as 1024x1024 pixels. However, it is noted that using Hi-Res Fix with TensorRT may increase the image generation time, suggesting that there might be room for optimization in this area to fully leverage the speed benefits of TensorRT.

VRAM consumption

VRAM, or video RAM, is the memory used by GPUs to store图像 data. In the context of the video, VRAM consumption is discussed when comparing the use of TensorRT with the standard image generation process. It is noted that TensorRT seems to reduce VRAM usage, which is beneficial for users with limited GPU memory. This reduction in VRAM consumption allows for more efficient use of the GPU and can enable faster image generation, especially when working with large models and high-resolution images.

SDXL

SDXL refers to a larger version of the stable diffusion model, which is capable of generating higher quality images. In the video, it is mentioned that the use of TensorRT with SDXL is currently only possible on the dev branch. The video also notes that there may be challenges with exporting the TensorRT engine for SDXL models other than the base model, indicating that this is an area of active development and potential future improvement.

image generation speed

Image generation speed refers to the rate at which images are produced by the stable diffusion webUI. The video focuses on the improvements in image generation speed achieved by integrating TensorRT with the webUI. It is demonstrated that using TensorRT can increase the speed to 1.5 times faster than the standard process, and in some cases, almost twice as fast. This is a key benefit of using TensorRT, as it allows users to generate images more quickly, which can be particularly useful for those working with large models or high-resolution images.

Highlights

TensorRT can now be used with stable diffusion webUI, potentially increasing the speed of image generation.

TensorRT is a high-performance deep learning inference engine developed by NVIDIA, optimizing models for faster execution.

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, demonstrated here with an RTX4090.

A new stable diffusion webUI and dev branch must be installed for using TensorRT.

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

The process for installing the webUI involves cloning from a GitHub page and switching to the dev branch using a commit hash.

The TensorRT engine needs to be exported to the checkpoint for the desired image sizes.

Different image sizes require different TensorRT engines, such as 512x512, 1024x1024, and 512x768.

The user interface settings need to be configured to use TensorRT with the correct model and settings.

Image generation with TensorRT is significantly faster, achieving over 50 iterations per second compared to 30 with RTX4090 under normal conditions.

TensorRT reduces VRAM consumption, offering a more efficient image generation process.

The video shows a side-by-side comparison of image generation times between normal mode and TensorRT mode.

Using TensorRT with Hi-Res Fix increases image generation time, suggesting possible areas for future improvement.

The Dynamic preset in TensorRT allows for flexibility in image size and batch size, albeit with increased VRAM usage.

When using img to img upscaling, TensorRT shows faster performance compared to the normal mode.

SDXL models can utilize TensorRT for even faster image generation, nearly doubling the speed in some cases.

The future of TensorRT integration with stable diffusion webUI is promising, with expectations of smoother installation and broader compatibility.