2X PERFORMANCE PLUGIN 🤯 OFFICIAL A11 STABLE DIFFUSION UPDATE GUIDE

TroubleChute
28 May 202315:26

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

00:00

🚀 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.

05:02

🛠️ 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.

10:03

🌐 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.

15:03

🎨 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

Onyx is a term used in the context of the video to refer to a specific type of model used in machine learning and AI, particularly for image generation. It is mentioned as part of the process of converting models to enhance performance. In the video, the speaker discusses the conversion of models to Onyx and then to Tensor RT for improved speed in stable diffusion inference.

💡Tensor RT

Tensor RT is a platform by NVIDIA that optimizes deep learning models for deployment. It is mentioned in the video as a technology that can be utilized to boost the performance of stable diffusion models. The speaker describes the process of converting models to Tensor RT for better performance gains.

💡Direct ML

Direct ML is a term that refers to a direct machine learning execution path on Windows, bypassing some of the traditional layers that can slow down performance. In the video, the speaker talks about the potential support for Direct ML, indicating it as a technology that could enhance the performance of AI models.

💡Stable Diffusion

Stable Diffusion is a type of AI model used for image generation. In the context of the video, it is the primary focus of the performance improvement discussion. The speaker is interested in accelerating the inference process of Stable Diffusion models using various technologies like Tensor RT.

💡Web UI

Web UI refers to the user interface that is accessed through a web browser, which in this context is used to interact with the stable diffusion models. The video discusses an upcoming web UI modification by NVIDIA that will include Tensor RT support.

💡Performance Boost

Performance boost refers to the improvement in the speed or efficiency of a system or process. In the video, the speaker is focused on achieving a performance boost by converting models to Tensor RT and using Direct ML for stable diffusion inference.

💡CUDA

CUDA is a parallel computing platform and application programming interface model created by NVIDIA. It allows software developers to use a CUDA-enabled GPU for general purpose processing. In the video, the speaker mentions the need to match the version of CUDA with the PyTorch library being used, which is crucial for the installation and operation of Tensor RT.

💡Hyper Networks

Hyper Networks, in the context of the video, refer to a type of neural network used in the stable diffusion process. They are mentioned as a feature that needs to be selected and baked into the converted model for them to function properly.

💡VRAM

VRAM stands for Video RAM, which is a type of memory used to store image data for the GPU to process. In the video, the speaker discusses the amount of VRAM used during the conversion process of models to Tensor RT and how it can peak depending on the operation.

💡Extensions

In the context of the video, extensions refer to additional software components that can be added to the stable diffusion web UI to enhance its functionality. The speaker discusses installing and using specific extensions for Tensor RT support.

💡迭代

迭代,在视频中指的是生成图像过程中的重复步骤,通常用于描述生成图像的进度。在讨论Tensor RT优化的性能提升时,迭代速度是一个重要的指标,因为它直接关联到图像生成的速度。

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.