【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
TLDRIn this video, 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, which can be beneficial for users looking to speed up their workflow. The video provides a step-by-step guide on how to install TensorRT alongside the stable diffusion webUI, noting that it's currently an unstable operation and recommends waiting for further stabilization unless users are eager to experiment. The environment used is RTX4090, and the dev branch of stable diffusion webUI is installed for compatibility. The video demonstrates the process of exporting TensorRT engines for different image sizes and models, and compares the speed and VRAM usage of image generation with and without TensorRT. It concludes by highlighting the potential for even faster image generation in the future and encourages viewers to subscribe and like the video for more updates.
Takeaways
- 🚀 TensorRT is now compatible with stable diffusion webUI, promising significant speed improvements for image generation.
- 💡 NVIDIA's TensorRT is a high-performance deep learning inference engine that optimizes models to run faster on their GPUs.
- ⚠️ The integration is still in its early stages and may be unstable, suggesting caution for users not requiring immediate use.
- 📋 The tutorial outlines the process of installing the stable diffusion webUI on the RTX4090 environment using the development branch.
- 🔄 Switching to the dev branch is necessary and involves using a specific commit hash from the stable diffusion GitHub repository.
- 🔧 Post-installation steps include adjusting the batch file and installing additional NVIDIA software, including the latest version of pip, cuDNN, and TensorRT.
- 🔄 To revert changes, deleting TensorRT and venv folders and restarting the process is recommended.
- 📈 Testing showed that image generation with TensorRT can be up to 1.5 times faster than without it, depending on the model and settings used.
- 📊 Exporting TensorRT engines is required for different image sizes, and the process is detailed for various models like Dreamshaper and Magic Mix.
- 🔍 The video provides a comparison of VRAM consumption and image generation times between normal mode and TensorRT mode.
- 🔮 Future improvements are anticipated to simplify the installation process and further optimize performance and VRAM usage.
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 might someone be advised to wait before using TensorRT with stable diffusion webUI?
-The operation may still be unstable, so users who are not in a hurry are recommended to wait a little longer before using it to ensure more stability and reliability.
What is the GPU requirement for using TensorRT with stable diffusion webUI?
-TensorRT is an engine for NVIDIA's GPU, so it cannot be used with other GPUs. The environment used in the video is an RTX4090.
How does one install the stable diffusion webUI for use with TensorRT?
-To install the stable diffusion webUI, one should create a new folder under the C drive, open a command prompt from this folder, and use the code from the GitHub page to clone the repository. Then, change to the dev branch and proceed with the installation.
What is the significance of using the dev branch instead of the master branch for SDXL?
-The dev branch is a development branch, which means it is under active development and may contain newer features or improvements. For using SDXL with TensorRT, the dev branch is necessary.
How does one activate the virtual environment (venv) for the stable diffusion webUI?
-To activate the venv, one needs to type the command to activate it in the command prompt. If a line starting with 'venv' appears, it indicates that the venv is activated.
What are the steps to install TensorRT after setting up the stable diffusion webUI?
-After setting up the webUI, one should install the new version of pip, install NVIDIA's cuDNN, install the development version of NVIDIA's TensorRT, and then uninstall the initially installed cuDNN.
How does exporting the TensorRT engine to a checkpoint work?
-One must select the model they want to incorporate the TensorRT 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, etc.
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 mode, as demonstrated in the video with an RTX4090 GPU.
How does the VRAM consumption compare when using TensorRT versus normal mode?
-TensorRT seems to reduce VRAM consumption compared to normal mode. For instance, when generating a 512x512 image and upscaling it with Hi-Res Fix, TensorRT used 5.04GB of VRAM, whereas normal mode used 6.19GB.
What is the current limitation when exporting the TensorRT engine for different image sizes?
-The TensorRT engine must be exported for the specific size of the final image. If the engine for a particular size, such as 1024x1024, is not exported, the image of that size cannot be generated.
What is the future outlook for image generation speed improvements with TensorRT?
-The future may hold even faster image generation with TensorRT, especially if improvements are made to the high-resolution fix process, and if there are optimizations for tiling or a mode that selects tiles depending on the individual GPU.
Outlines
🚀 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 notes potential instability and recommends waiting for stabilization. For eager users, Yuki provides a step-by-step guide to installing TensorRT on an RTX4090 GPU, including switching to the dev branch of stable diffusion webUI, installing necessary libraries, and configuring the environment. The process involves creating a new folder, cloning the webUI repository, switching to the dev branch, and adjusting the batch file for compatibility. The video emphasizes that TensorRT is exclusive to NVIDIA GPUs.
📈 Speed and Usage of TensorRT in Image Generation
The video demonstrates the significant speed increase in image generation using TensorRT. Yuki details the process of exporting the TensorRT engine to different models, such as Dreamshaper, Magic Mix, and anime bluepencil, for various image sizes. The user is guided through the settings adjustments in the webUI, including selecting the appropriate model and engine in the TensorRT tab. Yuki then compares the speed and iterations per second between normal mode and TensorRT mode, showing a substantial improvement in speed with TensorRT. The video also explores generating multiple images with a fixed seed value to assess consistency and the impact of high-resolution fixes on image generation time.
🔍 Comparing TensorRT with Normal Mode and VRAM Usage
Yuki conducts a comparison between the image generation speed in normal mode and with TensorRT, noting a 1.5 times faster speed with TensorRT. The video also investigates the VRAM consumption when using TensorRT, showing that it uses less VRAM compared to normal mode. Yuki discusses the potential for faster image generation without tiling and suggests future improvements. Additionally, the video touches on the use of the 'Dynamic' preset for exporting TensorRT engines, which allows for flexibility in image size and batch size, and its effect on VRAM usage.
🔧 SDXL Integration and Future Prospects
The video concludes with an exploration of SDXL integration with TensorRT, which is currently only possible on the dev branch. Yuki guides viewers through exporting the TensorRT engine for the SDXL base model and compares the image generation speed without a refiner. The results show a nearly doubled speed when using TensorRT. Yuki mentions encountering difficulties exporting the TensorRT engine for SDXL models other than the base model and expresses intent to investigate further. The video wraps up with a teaser for future updates and a call to action for viewers to subscribe and like the video.
Mindmap
Keywords
💡TensorRT
💡stable diffusion webUI
💡RTX4090
💡dev branch
💡cuDNN
💡VRAM
💡Hi-Res Fix
💡Dynamic Export
💡SD Unet
💡img to img
💡SDXL
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.
The operation may still be unstable, so it's recommended to wait before using it unless you're eager to try it.
TensorRT is an engine for NVIDIA's GPU and cannot be used with other GPUs.
The environment used in the demonstration is an RTX4090.
A new stable diffusion webUI and its dev branch are installed for the demonstration.
The dev branch is a development branch and is currently under development.
The process of installing the webUI and switching to the dev branch is detailed in the transcript.
After installing the webUI, the command prompt is used to activate venv and install necessary components.
The TensorRT engine is exported to the checkpoint for specific image sizes, such as 512x512 and 1024x1024.
The image generation speed using TensorRT is approximately 1.5 times faster than normal mode.
When using high resolution fixes with TensorRT, the image generation time may increase.
TensorRT also reduces VRAM consumption compared to generating an image with SD Unet set to none.
The future of image generation with TensorRT is discussed, including potential improvements and optimizations.
The use of img to img upscaling is suggested as a faster alternative to high resolution fix.
SDXL is demonstrated, showing that TensorRT can significantly speed up image generation without a refiner.
The initial installation process for TensorRT is expected to be improved for easier use in the future.
The video concludes with a call to action for viewers to subscribe and like the video.