2X SPEED BOOST for SDUI | TensorRT/Stable Diffusion Full Guide | AUTOMATIC1111
TLDRThe video guide provides an in-depth look at integrating TensorRT with Stable Diffusion WebUI, a move that can significantly boost performance by 50-100% for users with Nvidia RTX GPUs. It walks viewers through the installation process, including updating graphics card drivers and creating TensorRT engines. Despite some minor issues and errors, the guide demonstrates the substantial speed improvement achieved with TensorRT, highlighting its benefits for Nvidia users and the community's role in its development.
Takeaways
- π TensorRT for Stable Diffusion WebUI can significantly boost performance by 50 to 100%, increasing speed up to 2 or 2.5 times the original.
- π» The extension is specifically beneficial for Nvidia RTX GPU owners; it's less useful for those with different GPUs.
- π To use TensorRT with Stable Diffusion, Automatic1111 version is required, though it might also work with other flavors like SD Next.
- π€ Updating your Nvidia graphics driver to the specified minimum version is crucial for the extension to function correctly.
- π Installation involves downloading and setting up Stable Diffusion WebUI and then installing the TensorRT extension through it.
- π¨ Some users may encounter compatibility issues with other plugins; creating a new folder for a fresh install of Automatic1111 is recommended if problems arise.
- π A comprehensive installation guide and troubleshooting tips are available in the video description for viewers needing more detailed instructions.
- π¦ The TensorRT extension requires manual activation within Stable Diffusion's web UI, and installation progress can be tracked via Task Manager.
- π For optimal performance, specific models within Stable Diffusion need to be exported and optimized for TensorRT.
- π§ If encountering errors or compatibility issues after updates or installations, deleting the VM folder and updating Nvidia drivers may resolve problems.
- π TensorRT significantly enhances image generation speed, with tests showing a doubling of iterations per second compared to non-TensorRT setups.
Q & A
What is the main purpose of Tensor RT for Stable Diffusion WebUI?
-The main purpose of Tensor RT for Stable Diffusion WebUI is to significantly boost performance by 50 to 100%, resulting in 2 or even 2.5 times the speed compared to previous versions, particularly for users with an Nvidia RTX GPU.
How does Tensor RT differ from other extensions like Stable Diffusion with X?
-Tensor RT is an Nvidia-specific extension that provides a substantial performance boost over normal state diffusion and even Stable Diffusion with X forcers, making it more efficient for users with Nvidia RTX GPUs.
What is the prerequisite for installing Tensor RT extension?
-To install the Tensor RT extension, you must have Stable Fusion Web UI installed and an Nvidia RTX GPU. Additionally, your graphics card driver should be updated to at least version 5375 as mentioned in the script.
What should you do if your graphics card driver is not up to date?
-If your graphics card driver is not up to date, you should visit the Nvidia website and update your graphics driver to at least version 5375. It is recommended not to rely on Windows Update as it may not always provide the latest versions.
What is the recommended action if you experience difficulties with other plugins on your system?
-If you experience difficulties with other plugins on your system, creating a new folder with a new copy of Automatic 1111 is suggested as a solution.
How do you install the Tensor RT extension?
-To install the Tensor RT extension, you can either find it in the 'Available' tab in Stable Diffusion Web UI and click 'Load' from the list or install it manually by pasting the extension's URL in the 'Install from URL' section and clicking 'Install'.
What is the process for building the Tensor RT engine?
-To build the Tensor RT engine, go to the Tensor RT tab in Stable Diffusion WebUI, click 'Export Default Engine', and follow the prompts. It may take some time, and even if there are errors, the process should continue. Once completed, the engine will be saved as a Tensor RT model on your drive.
How does the Tensor RT engine affect image generation speed?
-The Tensor RT engine significantly increases image generation speed. For example, it can generate images at around 20 iterations per second, which is more than double the speed compared to not using a Tensor RT unit.
What are the advantages of creating optimized models with Tensor RT?
-Creating optimized models with Tensor RT allows for faster generation of images and iterations, especially for users with Nvidia RTX graphics cards. It can be particularly beneficial for those who have favorite models they use frequently.
What is the downside of using Tensor RT for optimized models?
-The downside of using Tensor RT for optimized models is that it requires more hard drive or SSD space, as each optimized model and Lora file created is around 2 GB in size.
Is Tensor RT compatible with all types of GPUs?
-Tensor RT is specifically designed for Nvidia RTX GPUs and is not compatible with other types of GPUs, such as AMD graphics cards.
Outlines
π Introducing Tensor RT for Stable Diffusion WebUI
The video begins with an introduction to Tensor RT for Stable Diffusion WebUI, a tool designed to significantly boost performance by 50-100%. The speaker explains that this NVIDIA-specific extension, despite being released recently, offers a substantial performance enhancement over standard state diffusion and stable diffusion with X forcers. The video is aimed at users with an Nvidia RTX GPU, as the extension is not compatible with other types of GPUs. The speaker then advises on the prerequisites for using Tensor RT, including having Stable Fusion Web UI installed and updating the graphics card driver to at least version 5375, as mentioned in the video.
π§ Installation and Troubleshooting Tensor RT
This paragraph delves into the installation process of Tensor RT, emphasizing the ease of installation through the Stable Diffusion Web UI's available extensions tab. The speaker provides a detailed guide on how to install the extension, including updating the graphics card driver and dealing with potential plugin conflicts by creating a new folder with a fresh copy of Automatic 1111. The speaker also discusses the need to rebuild the Tensor RT engine if errors occur and suggests updating NVIDIA drivers to the latest version for optimal performance.
π οΈ Configuring and Using Tensor RT Engines
The speaker explains the process of configuring Tensor RT engines once Stable Diffusion is set up. This includes exporting the default engine and creating a Tensor RT model. The video outlines the importance of selecting the right model based on the type of images to be generated, such as choosing between static and dynamic options for image size and batch size. The speaker also discusses the creation of different Tensor RT engines for various models and the impact of prompt length on the generation process. The paragraph concludes with a demonstration of the speed improvement provided by Tensor RT, showcasing the generation of images with significantly reduced time.
π¨ Generating Images with Tensor RT and Performance Comparison
The speaker demonstrates the image generation process using Tensor RT, highlighting the speed at which images are created with the extension enabled compared to when it is not. The video shows the generation of a Spaceman image and provides a comparison of the time taken with and without Tensor RT, emphasizing the substantial speed improvement. The speaker also explores the use of Tensor RT with different models, such as Pepe the Frog and Laura, and notes the need to recreate luras for each checkpoint when using Tensor RT. The paragraph concludes with a discussion on the storage space required for the Tensor RT models and the benefits of creating optimized models for favorite checkpoints.
π Final Thoughts and Additional Resources
In the final paragraph, the speaker reflects on the overall value and impact of Tensor RT for NVIDIA users, appreciating the significant speed improvements it offers. The speaker also acknowledges that while the extension is not compatible with AMD graphics cards, it is a welcome enhancement for those using NVIDIA RTX GPUs. The video ends with a thank you note to NVIDIA, the Stable Diffusion community, and viewers, and provides a link in the description for those interested in a comprehensive guide on using Stable Diffusion WebUI.
Mindmap
Keywords
π‘Tensor RT
π‘Stable Diffusion
π‘Nvidia RTX GPU
π‘Performance Boost
π‘Graphics Card Driver
π‘Installation Guide
π‘Web UI
π‘Export Default Engine
π‘Batch Size
π‘SD Unit
π‘Checkpoint
Highlights
Tensor RT is an Nvidia-specific extension that boosts performance by 50-100% for stable diffusion webUI.
The extension was updated recently and is particularly beneficial for users with Nvidia RTX GPUs.
To use Tensor RT, stable diffusion webUI must be installed, and other plugins may need to be compatible.
Updating the graphics card driver to version 5375 or higher is crucial for optimal performance.
The installation process involves downloading and installing the Tensor RT extension from the available tab in stable diffusion webUI.
Users may need to delete the VN folder and reinstall packages like pytorch and Tensor RT if they encounter issues.
Creating a Tensor RT engine can take some time, but it significantly speeds up image generation.
The default Tensor RT model is a 1.5 pruned Onyx model, which is saved on the drive for optimized performance.
Users can customize the Tensor RT engine settings based on their image creation needs, such as image size and batch size.
The Tensor RT extension automatically chooses the best engine for the output configuration settings.
The speed improvement with Tensor RT is noticeable, with images generating at around 20 iterations per second.
The Tensor RT unit can be added to the quick settings for easy access during the image generation process.
The Tensor RT extension may not be compatible with all models, and users may need to create specific engines for each model.
Despite some error messages, the Tensor RT extension functions as intended and provides a significant speed boost.
Each Tensor RT model created is around 2 GB in size, which should be considered if storage space is limited.
The performance improvement offered by Tensor RT is a welcome change for Nvidia RTX GPU users.