2X Speed A1111 TensorRT extension
TLDRIn this video tutorial, the host guides viewers through the installation of Intense Variety for Stable Diffusion, an extension ideal for large projects like movies, books, or web apps. The process involves cloning a git repository, switching branches, and downloading necessary files. The host also demonstrates how to disable other extensions and convert models to TensorRT for optimized performance. The video concludes with a speed comparison between the original Stable Diffusion and the new TensorRT implementation, showcasing significant improvements in image generation speed.
Takeaways
- ๐ The video is a tutorial on installing 'Intense Variety for Stable Diffusion', an extension ideal for large projects.
- ๐ Early development allows usage of Control Net or Loras, but not Text Full Inversion.
- ๐ The extension enables fast image production, suitable for projects like movies, books, or web apps with large user bases.
- ๐ ๏ธ Installation involves cloning a git repository and switching to a specific branch.
- ๐ฝ Downloading Tensority from Nvidia and placing it in the correct folder structure is necessary.
- ๐ Proper file hierarchy is crucial for Tensority to function correctly within the extension.
- ๐ Disabling all other extensions in Stable Diffusion is required before using the new one.
- ๐ Restarting the UI is needed to apply changes and enable the new extension.
- ๐ Conversion of models to Onyx and then to TensorRT is detailed, showcasing the process and time taken.
- ๐ผ๏ธ Testing the speed of image generation with the old Stable Diffusion versus the new TensorRT is demonstrated.
- ๐ The video concludes with a comparison of speeds, showing significant improvement with TensorRT.
Q & A
What is the main topic of the video?
-The main topic of the video is the installation and use of Intense Variety for Stable Diffusion, a new extension for generating images quickly, ideal for large projects.
What type of projects would benefit from using Intense Variety with Stable Diffusion?
-Projects like movies, books, or web apps that serve a large user base would benefit from using Intense Variety with Stable Diffusion due to its ability to produce images rapidly.
What are some of the features or tools mentioned in the video that can be used with Intense Variety?
-The video mentions the use of Control Net or Loras, as well as Text Full Inversion, as features or tools that can be used with Intense Variety.
How does the video guide the viewer in installing the Intense Variety extension?
-The video guides the viewer through cloning the extension's git repository, switching to a specific branch, and downloading and placing necessary files in the correct directories within the Stable Diffusion folder.
What is Tensority and how is it used in the context of this video?
-Tensority is a component downloaded from Nvidia, which is used by copying it into the Stable Diffusion directory to enhance the functionality of the Intense Variety extension.
How does the video address the process of converting images using the new extension?
-The video demonstrates the conversion process by showing how to set up the necessary parameters, such as batch size and maximum tokens, and then converting Onyx to TensorRT for optimized performance.
What issue does the video highlight regarding the current version of Intense Variety?
-The video highlights that the current version of Intense Variety does not support different sizes and that it might not be as efficient as other tools like L Smith, which is mentioned as a topic for a future tutorial.
How does the video compare the speed of the old Stable Diffusion with the new TensorRT?
-The video compares the speed by showing the time it takes to generate images using both the old Stable Diffusion and the new TensorRT, noting that TensorRT is almost twice as fast.
What is the significance of the 'Silvana style magic' mentioned in the video?
-The 'Silvana style magic' refers to a setting or feature used in the image generation process. The video suggests that it might need to be adjusted to improve the quality of the generated images.
What advice does the video give about the future of the Intense Variety extension?
-The video suggests that in the future, when the Intense Variety extension becomes more refined and supports different sizes and features like floras, it could be a valuable tool for users, especially for large-scale image generation tasks.
What is the next step or topic that the video promises to cover?
-The video promises to cover the installation of Volta ml in a future tutorial, which is another tool that could potentially offer better performance and features compared to the current Intense Variety extension.
Outlines
๐ Installing Intense Variety for Stable Diffusion
This paragraph outlines the process of installing Intense Variety, a new extension for Stable Diffusion, which is ideal for large projects like movies, books, or web apps. It explains the initial steps, including cloning the git repository, switching branches, and downloading necessary files like TensorRT from Nvidia. The paragraph emphasizes the early development stage of the extension and mentions that while it supports control net or loras, text full inversion is also available. It concludes with the anticipation of a better implementation in a future video.
๐ Configuring and Testing Stable Diffusion Extensions
The second paragraph details the configuration of Stable Diffusion extensions, including disabling other extensions and converting files to Onyx. It describes the process of launching the web UI and updating the system with a git pull. The paragraph then focuses on the conversion of a 1.5 mole to TensorRT, highlighting the time taken for the conversion. It also discusses the testing of image generation speeds, comparing the old Stable Diffusion with the new TensorRT, and mentions the use of specific samplers and settings for optimal results.
๐จ Evaluating Image Quality and Speed with TensorRT
In the final paragraph, the focus shifts to evaluating the image quality and speed achieved with TensorRT. It compares the performance of the system with and without TensorRT, noting a significant increase in speed. The paragraph discusses the use of different settings and samplers, such as Silver Magic, and the impact on image generation time. It concludes with a reflection on the results, noting that the images produced may need further refinement and expressing optimism for future improvements with Volta ML.
Mindmap
Keywords
๐กIntense Variety for Stable Diffusion
๐กStable Diffusion
๐กControl Net and Loras
๐กTensority
๐กWeb UI
๐กExtensions
๐กOnyx and TensorRT
๐กBatching
๐กSample and Samplers
๐กPerformance Testing
Highlights
Installing Intense Variety for Stable Diffusion, a new extension ideal for large projects.
Producing images super fast due to early development of the technology.
Using ControlNet or LoRaS in the current development stage.
Text full inversion is available but not fully optimized.
Upcoming video will show a better implementation of Tense or TIG.
Instructions on installing the extension by cloning the git repository.
Switching to the 'depth' branch for the extension's functionality.
Downloading Tensority from Nvidia and placing it in the correct directory structure.
Launching the web UI for Stable Diffusion and ensuring it's up to date.
Disabling all other extensions to avoid conflicts.
Restarting the UI for changes to take effect.
Converting all models to Onyx for compatibility with the new extension.
Adding extra networks in the settings for enhanced capabilities.
Converting a 1.5 mole to TensorRT for optimized performance.
Adjusting parameters for batch size and maximum tokens for efficient processing.
Speed comparison between old Stable Diffusion and new TensorRT.
Testing different samplers and settings for image generation.
Results show almost twice the speed with TensorRT, maintaining the same quality.
Potential future improvements and additional features to be explored in upcoming tutorials.