2X PERFORMANCE PLUGIN 🤯 OFFICIAL A11 STABLE DIFFUSION UPDATE GUIDE
TLDRThe video discusses the integration of Tensor RT with Stable Diffusion for significantly improved performance in image generation. The creator walks through the process of installing an extension for web UI Tensor RT support, highlighting the need for compatible hardware and software versions. The conversion of models to Tensor RT is detailed, emphasizing the potential for nearly doubled speeds in image generation, with a focus on the limitations and requirements for optimal use. The video concludes with a demonstration of the conversion's effectiveness, showcasing a substantial increase in iteration speed.
Takeaways
- 🚀 The video discusses a method to significantly boost the performance of stable diffusion image generation using Tensor RT and Direct ML.
- 💡 Vlad Mantic mentioned that Onyx support would be limited in favor of supporting Tensority directly.
- 📢 An official announcement from Automatic1111 revealed that Nvidia is developing a web UI with Tensor RT and Direct ML support.
- 🔧 The performance gains for generating 512 pictures with the new extension are reported to be 50-100% faster compared to SD nomam optimization.
- 🔄 The process involves converting models to Onyx and then to Tensor RT for optimized performance.
- 📋 The user's comment highlights the need for up-to-date Automatic 1111 repo and compatibility testing with Windows.
- 🔗 A link to the extension for Tensor RT support and a guide on how to install it manually are provided in the video description.
- 💻 The installation process requires downloading Tensor RT from Nvidia and extracting it into the extension directory.
- 🛠️ The video provides detailed steps on how to install the extension, convert models, and use the Tensor RT optimized models.
- 📈 The video demonstrates a significant speed increase in image generation, reaching up to 20 iterations per second with Tensor RT.
- 🔜 Future improvements are anticipated with the official release of Tensor RT support from Nvidia and integration into the Automatic 1111 web UI.
Q & A
What is the main focus of the video?
-The main focus of the video is to demonstrate how to improve the performance of stable diffusion inference and image generation by using Tensor RT and Direct ML, specifically within the Automatic1111 web UI.
What was the performance gain observed after using the Tensor RT extension?
-The performance gain observed was about 50 to 100 percent faster for generating 512 pictures, which is a significant improvement compared to the SD nomam optimization on large resolutions.
What is the current status of the official Tensor RT support from Nvidia?
-Nvidia is working on releasing a web UI modification with Tensor RT and Direct ML support built-in, but it has not been released yet due to approval issues.
What are the prerequisites for installing the Tensor RT extension?
-To install the Tensor RT extension, one needs to have the Automatic 1111 stable diffusion web UI installed, the Automatic 1111 repo up to date, and the same version of CUDA as the PyTorch library being used (in this case, CUDA 11.8 for torch 2.0.1).
How does one convert models to Tensor RT using the extension?
-To convert models to Tensor RT, one must first convert the models to Onyx, then use the Tensor RT plugin within the Automatic 1111 web UI to convert the Onyx models to Tensor RT, specifying the minimum and maximum width, batch size, and prompt token count.
What are the limitations of the current Tensor RT support?
-The current Tensor RT support does not support Hyper Network or control net, and it requires specific versions of the software and hardware (CUDA 11.8 and compatible GPUs like the RTX 20/30/40 series). Also, the converted models are restricted to the image parameters they were optimized for.
What is the expected performance boost from Nvidia's official Tensor RT integration?
-While the exact numbers are not specified, it is implied that Nvidia's official Tensor RT integration could potentially offer even better performance than the current extension, though it is not yet available.
What happens if you attempt to convert models that have already been optimized with Onyx to Tensor RT?
-Converting models to Onyx will fail if they have already been optimized with Convert From Onyx to Tensor RT at any point prior to conversion. In such cases, a complete restart of the web UI may be required before attempting the conversion again.
How does the use of Tensor RT affect the generation of images?
-The use of Tensor RT significantly increases the speed of image generation, allowing for faster iterations per second. However, it is limited to the image parameters that were baked into the model during the conversion process.
What is the process for using the newly converted Tensor RT models in stable diffusion?
-After conversion, the Tensor RT models can be selected in the stable diffusion settings using the SD Unit option. The 'Automatic' setting will look for a .trt file with the same name as the checkpoint model being used, allowing for the generation of images with the optimized model.
What is the significance of the development mentioned in the video for users who do not use embeddings or lorries?
-For users who do not utilize embeddings or lorries, the development of Tensor RT support offers a significant performance boost. It allows them to speed up their models and achieve nearly double the speed for image generation, as long as they stay within their usual image parameters.
Outlines
🚀 Introduction to Performance Boosting with Tensor RT
This paragraph introduces the viewer to a guide on enhancing the performance of stable diffusion inference for image generation. It discusses a video by Onyx/slash tensor RT and highlights a comment thread where Vlad Mantic suggests supporting tensority directly. An official announcement from Automatic1111 mentions Nvidia's work on a web UI modification with tensor RT and direct ml support, which is not yet released due to approval issues. The speaker shares their own extension for performance gains and mentions plans to integrate differences once Nvidia releases their version.
🛠️ Installation and Setup of Tensor RT Extension
The speaker provides a step-by-step guide on installing the Tensor RT extension for the stable diffusion web UI. This includes downloading Tensor RT from Nvidia, choosing the correct version of Cuda compatible with the Pytorch library in use, and extracting the zip file into the extension directory. The process also involves updating the Automatic1111 repo, switching to the developer branch, and converting models to Onyx before converting to Tensor RT. The speaker emphasizes the need to restart the web UI for successful conversion and notes that some features like Hyper Network support are not tested.
🌐 Converting Models and Performance Testing
In this section, the speaker explains how to convert models to Onyx and then to Tensor RT using the web UI. They provide instructions on setting the correct parameters for the conversion process, such as minimum and maximum width, batch size, and prompt token count. The speaker also discusses the limitations of the current version, including the inability to generate images outside the specified parameters. They share their experience with the conversion process, noting the time taken and the success achieved on a 3080 TI GPU.
🎨 Generating Images with the Optimized Tensor RT Model
The speaker demonstrates how to use the newly converted Tensor RT model in the stable diffusion web UI for image generation. They guide through the process of selecting the optimized model in the settings and generating an image with the base model. The speaker tests the performance by generating images at different iterations per second and discusses the limitations of the current model, such as the inability to use certain features like Hyper Networks. They also explore the use of a textual version for generating images with specific characters, showing the potential and limitations of the current technology.
📚 Conclusion and Future Prospects
The speaker concludes the video by summarizing the process and performance gains achieved through the Tensor RT extension. They mention that the technology is still in its early stages but shows promising results in terms of speed, particularly for users without access to embeddings or other advanced features. The speaker also looks forward to future developments, such as official integration by Automatic1111 and the potential for even greater performance boosts once Nvidia releases their version and when the technology matures further.
Mindmap
Keywords
💡Onyx
💡Tensor RT
💡Direct ML
💡Stable Diffusion
💡Web UI
💡Performance Boost
💡CUDA
💡Hyper Networks
💡VRAM
💡Extensions
💡迭代
Highlights
Introduction to a new method for boosting stable diffusion performance using Tensor RT and Direct ML.
Vlad Mantic's comment on not supporting Onyx but preferring Tensority directly.
An official announcement from Automatic1111 about Nvidia's work on a web UI with Tensor RT and Direct ML support.
The creation of an extension for using Tensor Aussie engines, resulting in 50-100% faster performance gains for generating images.
The requirement for the Automatic 1111 repo to be up to date for the extension to work.
Nvidia's ongoing efforts to release their version of Tensor RT for web UI, potentially offering better performance.
Instructions on how to install the Tensor RT extension for stable diffusion web UI.
The necessity of having the same version of CUDA as the PyTorch library being used.
The process of converting models to Onyx and then to Tensor RT for performance enhancement.
The importance of selecting the correct model and Hyper networks for baking into the new model.
The potential issue of conversion failure if previously used convert from Onyx to Tensor RT.
The impact of disabling extensions not in use to avoid conflicts during the conversion process.
The detailed steps for converting Onyx models to Tensor RT, including command usage and VRAM considerations.
The successful conversion of the model and the resulting increase in image generation speed.
The limitation of generating images within the same parameters as the original model after conversion.
The demonstration of the speed increase in image generation after the conversion, reaching up to 20 iterations per second.
The current limitations and future potential of this technology, emphasizing its early stage and the promise of further improvements.