Nvidia擴充TensorRT使用教學，算圖速度提升最多兩倍!!
TLDRIn this video, the hosts Jack and Ellie discuss the recent developments with TensorRT, an NVIDIA tool that promises to significantly speed up graphics processing. They explain the limitations and installation process, including the need for a static or dynamic model conversion and the impact on tools like controlnet and FreeU. They also cover the integration of lora into models and provide tips for efficient use and removal of TensorRT models. The video is a practical guide for users looking to optimize their graphics performance and save on electricity costs.
Takeaways
- 📣 Introduction to TensorRT: A feature claimed to double the speed of graph calculations, but initially had complexity in usage and support issues.
- 🚀 NVIDIA's recent expansion support: The creators of TensorRT have provided an update to make it more accessible and efficient.
- 🔄 Model Conversion: Users need to convert their models into TensorRT (trt) models, which come with certain limitations.
- ⚙️ Limitations of TensorRT: Tools like controlnet and FreeU that are specific to U-Net architectures do not work with TensorRT.
- 💡 Performance Advantage: A 20% increase in graph calculation speed can lead to a 20% reduction in electricity costs.
- 🔧 SDXL Web UI Requirement: Users must switch to the dev version of the web UI to utilize TensorRT, or wait for the main version update.
- 🛠️ Installation Process: The installation of the TensorRT expansion requires additional environment packages due to existing bugs.
- 🔄 Dynamic vs. Static Models: TensorRT supports both dynamic models with adjustable parameters and static models with fixed values at conversion.
- 📏 VRAM Consumption: Static models consume less VRAM compared to dynamic models, with a difference of approximately 1G in the discussed example.
- 🔄 Exporting TensorRT Models: The conversion process takes about 3-5 minutes, and users can choose the appropriate model type based on their needs.
- 🔍 Testing and Comparison: Users are encouraged to test the speed improvements on their own computers, with an example speed increase of 30%.
- 🔄 LoRA Integration: Instructions on how to incorporate LoRA into models and convert them for use with TensorRT, including potential bugs and the need for base model compatibility.
Q & A
What is the main topic of the video?
-The main topic of the video is an introduction to TensorRT, its benefits, limitations, and a step-by-step guide on how to install and use it for accelerating deep learning models.
What is TensorRT's primary function?
-TensorRT's primary function is to accelerate deep learning models by allowing faster computation, potentially doubling the speed of graph execution.
What are the limitations of using TensorRT?
-The limitations of using TensorRT include the requirement to convert models into a TRT format, incompatibility with certain tools like controlnet and FreeU, and the inability to dynamically adjust certain parameters in static models.
How does TensorRT save on electricity costs?
-By increasing computation speed, TensorRT can save on electricity costs proportionally. For example, a 20% increase in computation speed can result in a 20% reduction in electricity usage.
What are the two types of models that TensorRT supports?
-TensorRT supports dynamic models, which allow adjustable parameters like image dimensions and batch size during computation, and static models, which use fixed values set during conversion.
What is the difference in VRAM consumption between dynamic and static models?
-Static models generally consume less VRAM compared to dynamic models. The video mentions a difference of approximately 1GB in VRAM consumption between the two.
How to install the TensorRT extension if there is a bug in the direct installation process?
-To install the TensorRT extension when there is a bug, one must first install certain environment packages by downloading and executing a batch file provided in the instructions. After that, the extension can be installed through the web UI from a URL.
How to enable automatic switching to TensorRT models in the user interface?
-To enable automatic switching, go to the settings, find the 'SD' section, and under the quick settings list, apply the setting for 'SD_unet'. After applying, the UI will reload and present an option for automatic switching to TensorRT models if available.
What is the process for converting a model to TensorRT?
-To convert a model to TensorRT, go to the TensorRT menu, select the model weights file, and choose to export an engine with either static or dynamic shapes based on the desired parameters. The conversion process takes about 3-5 minutes.
How can Lora be used with TensorRT?
-Lora can be directly integrated into the model before conversion. For dynamic adjustment of Lora weights, one can use the official test version available in the 'lora_v2' branch of the extension. The converted Lora model can then be used in text-to-image and image-to-image tasks.
How to remove the TensorRT models if needed?
-To remove the TensorRT models, go to the SD's models folder and delete the contents of the 'Unet-onnx' and 'Unet-trt' folders. It is advised not to remove only some models unless you understand the contents of the 'model.json' file to avoid additional errors.
Outlines
🚀 Introduction to TensorRT and Its Benefits
The paragraph introduces the speakers, Jack and Aili, and their return after a hiatus. They discuss TensorRT, a feature developed by NVIDIA that promises to double the speed of graphics processing. Despite its complex usage and support issues, a recent update by NVIDIA has reignited interest. The speakers explain the limitations of TensorRT, such as the incompatibility with U-Net tools like controlnet and FreeU, and the requirement to convert models to trt format. They highlight the potential energy cost savings from increased processing speed and provide guidance on the installation process, including dealing with bugs and the need for updated drivers. The paragraph concludes with a brief mention of the SDXL webui and the anticipation of its update.
🛠️ Utilizing TensorRT with Dynamic and Static Models
This paragraph delves into the specifics of using TensorRT with dynamic and static models. The speakers explain the difference between the two, noting that dynamic models allow adjustable parameters like image dimensions and batch size during processing, while static models use fixed values set during conversion. They guide the audience through the process of converting a model to a static version, including setting parameters and exporting the engine. The paragraph also touches on the benefits of static models, such as lower VRAM consumption, and provides a comparison of VRAM usage between dynamic and static models. Additionally, the speakers discuss the process for using dynamic models and the advantages of not needing to switch models at high resolutions. The section ends with instructions on how to apply settings for automatic model switching in the user interface and a comparison of processing speeds with TensorRT enabled.
Mindmap
Keywords
💡TensorRT
💡Model Conversion
💡Dynamic Model
💡Static Model
💡VRAM
💡SDXL
💡Web UI
💡LoRA
💡Installation
💡Performance
💡Bug
Highlights
杰克和艾粒回归分享最新动态，讨论TensorRT的使用和优化。
TensorRT是一个能够显著提升图形处理速度的工具，号称速度翻倍。
TensorRT的使用有一定复杂性，并且存在一些支持问题。
Nvidia推出了TensorRT的扩展，带来了新的优化和改进。
使用TensorRT前需要将模型转换为TRT模型，但有使用限制。
TensorRT不支持U-Net工具，如controlnet和FreeU。
转换为TensorRT模型后，可以节省电费，提升效率。
SDXL用户需要将webui切换到dev版本以使用TensorRT。
安装TensorRT扩展前需要安装部分环境套件。
TensorRT支持动态模型和静态模型的转换，各有优缺点。
静态模型的VRAM消耗较低，但使用时需固定参数。
动态模型允许在计算时调整图片尺寸和批量大小。
转换模型到TensorRT后，可以在UI中自动切换使用。
使用TensorRT后，图形处理速度可以提升约30%。
LoRA可以融合进模型并转换为TensorRT，但动态调整LoRA权重的功能还在测试阶段。
转换LoRA模型后，只能在对应的base模型上运行。
移除TensorRT模型需要删除特定文件夹中的文件。
使用通配符可以一次性测试多种提词。