ComfyUI: nVidia TensorRT (Workflow Tutorial)
TLDRThe video tutorial introduces the integration of nVidia TensorRT into ComfyUI for enhanced real-time image generation performance. It covers the performance boost provided by TensorRT for various models like stable diffusion 1.5, sdxl, and stable diffusion 3, highlighting the workflow for building TensorRT engine files. The video also explains the technical aspects of TensorRT optimization, including precision calibration, graph optimization, and dynamic tensor memory management, while noting limitations with certain features like ControlNet and Lura.
Takeaways
- 🚀 TensorRT has been integrated into ComfyUI for significant real-time performance improvements in image generation using models like Stable Diffusion 1.5 and Realistic Vision 6.
- ⏱️ Using TensorRT, image generation times can be reduced by approximately 26%, saving around 20 minutes and 50 seconds per 100 images with a batch of four.
- 🔧 The performance boost varies with different models; for instance, Stable Diffusion 3 with TensorRT is up to 32% faster, saving over an hour and 20 minutes per 100 images.
- 🛠️ TensorRT is an official custom node in ComfyUI that works on all Nvidia RTx GPUs, but compatibility with server-grade GPUs like A6000 or H100 is uncertain without testing.
- 💡 TensorRT optimizes models by converting them into an ONNX format, analyzing the computational graph, and applying various optimizations such as precision calibration, graph optimization, layer fusion, and dynamic tensor memory.
- 📈 Precision calibration in TensorRT involves converting weights and activations to lower precision formats to speed up processing without significantly compromising accuracy.
- 🔄 Graph optimization in TensorRT streamlines the model's computational process, resulting in faster inference times and reduced memory usage.
- 🧩 Layer fusion combines consecutive operations in a neural network into a single operation, improving performance significantly, especially in architectures with reset blocks.
- 🔀 Parallelism in TensorRT allows for the simultaneous processing of multiple images, increasing throughput and speeding up computation.
- 🔧 Kernel autotuning in TensorRT selects the best algorithm for each operation in the model to optimize performance on specific GPU hardware.
- 🏗️ The script provides a comprehensive workflow to build TensorRT engine files using Boolean and logic nodes, which can be customized for different models and parameters.
Q & A
What is the purpose of integrating TensorRT with ComfyUI?
-The integration of TensorRT with ComfyUI aims to provide real-time performance improvements on image generation, offering significant speed enhancements for various models like Stable Diffusion 1.5, SDXL, and Stable Video Diffusion.
How much time can be saved per 100 images with TensorRT using a batch of four?
-With TensorRT, a batch of four saves approximately 20 minutes and 50 seconds per 100 images, which indicates a 26% performance boost.
What is the performance increase when using TensorRT with SDXL Turbo?
-Stable Diffusion XL Turbo offers the least performance increase with TensorRT, saving only 11 minutes per 100 images with a batch of four, which is a 14% improvement.
What percentage of performance boost does Stable Video Diffusion get with TensorRT in the 14 frame model?
-Stable Video Diffusion gets up to a 29% performance boost with TensorRT in the 14 frame model.
How much faster is Stable Diffusion 3 with TensorRT compared to the original model?
-Stable Diffusion 3 has the most performance improvements with TensorRT, being up to 32% faster and saving over 1 hour and 20 minutes per 100 images.
What does TensorRT do in terms of optimizing the computational graph of a model?
-TensorRT optimizes the computational graph by analyzing and transforming it to make it more suitable for execution on the target hardware, which includes operations like Precision Calibration, graph optimization, layer fusion, and dynamic tensor memory management.
What is the role of the TensorRT conversion node in ComfyUI?
-The TensorRT conversion node in ComfyUI is responsible for generating a compatible model into an engine file that runs faster on Nvidia GPUs by converting the model into an ONNX format that TensorRT can understand and optimize.
What are some limitations of using TensorRT with ComfyUI as mentioned in the script?
-Some limitations include incompatibility with ControlNet and Lora, issues with face detailer when generating images with many small faces, and challenges with certain features like IP adapter, Google style align, and focus painting patch.
How does the script describe the process of building a TensorRT engine file in ComfyUI?
-The script describes a comprehensive workflow that includes Boolean and logic nodes to automate the process of building a TensorRT engine file, which involves setting parameters, converting the model to ONNX format, and optimizing the computational graph for the Nvidia GPU.
What is the significance of using a dynamic range for model optimization in TensorRT?
-Using a dynamic range allows a single model to handle various input parameters without needing to change settings repeatedly, making it ideal for scenarios where input parameters can vary, thus providing flexibility and efficiency.
How does the script suggest organizing the workflow for easier use and understanding?
-The script suggests using groups, fast mutant nodes, and control panels to organize the workflow, allowing for easy configuration of resolution, batch, and FPS settings, and enabling the user to switch between predefined settings quickly.
Outlines
🚀 Introduction to Tensor RT in Comfy UI
Seth introduces the video by highlighting the release of Tensor RT for Comfy UI, which significantly boosts the performance of image generation using models like Stable Diffusion 1.5 and realistic Vision 6. He mentions the time saved in image generation with different batch sizes and models, such as SDXL and Stable Video Diffusion. Seth also promises to explain how Tensor RT works and to guide viewers through creating a Tensor RT engine file using a workflow with Boolean and logic nodes, acknowledging the support of paid channel members.
🔍 Understanding Tensor RT's Optimization Techniques
This paragraph delves into the technical aspects of Tensor RT's optimization techniques, including precision calibration, graph optimization, layer fusion, and parallelism. Seth explains how these techniques work to improve the computational efficiency of models on Nvidia GPUs. He also discusses the role of Onyx in converting models into a format that Tensor RT can optimize and the dynamic memory management in Tensor RT that minimizes memory usage and fragmentation.
🏗️ Building the Tensor RT Engine Files Workflow
Seth provides a step-by-step guide on constructing the Tensor RT engine files workflow in Comfy UI. He emphasizes the importance of using Boolean and logic nodes for automation and introduces the nodes required for the workflow, such as the tensor RT node, impact pack, and comfy math nodes. He also explains the process of configuring the workflow for different models and resolutions, and the benefits of using a workflow over manual settings for each checkpoint.
🛠️ Customizing Tensor RT Workflow for Different Models
The paragraph focuses on customizing the Tensor RT workflow for various models, including SD 1.5, SDXL, and SD3. Seth demonstrates how to set up the workflow for static and dynamic resolution ranges, and how to organize the nodes for easy testing and use. He also discusses the limitations of certain models with Tensor RT and the workarounds he has discovered, such as using a 'cheese in' method for models that are not officially supported.
🌐 Organizing and Testing the Tensor RT Workflow
Seth continues to elaborate on organizing the workflow, including setting up groups for different resolution types and adding nodes for stable video diffusion (SVD). He explains the importance of testing the workflow to ensure the correct outputs and the use of various nodes like the 'if none' node for handling frame values in SVD. The paragraph also covers the process of renaming and recoloring nodes for clarity and the setup for dynamic tensor RT nodes.
📈 Adjusting Dynamic Ranges for Optimal Performance
This section discusses adjusting the dynamic ranges for different use cases and the importance of selecting appropriate ranges to balance performance with VRAM usage. Seth provides guidance on setting the minimum, optimal, and maximum values for SD 1.5, SDXL, and SD3, and how to group and connect these values with dynamic switches for flexibility in image generation.
🔧 Finalizing the Tensor RT Workflow Setup
Seth wraps up the setup process by detailing the final steps for the Tensor RT workflow, including adding nodes for batch size and context optimization, and the use of the 'fast mutant' node to include both static and dynamic groups. He also touches on the considerations for SD3 and the use of a VAE save node to manage VRAM usage effectively.
🎬 Conclusion and Next Steps with Comfy UI
In the concluding paragraph, Seth summarizes the video's content, expressing hope that viewers have gained valuable insights into using Tensor RT with Comfy UI. He provides a final reminder about ensuring software compatibility and updates, and hints at continuing the exploration of Comfy UI in future videos, ending with a sign-off and background music.
Mindmap
Keywords
💡TensorRT
💡ComfyUI
💡Performance Boost
💡Batch Processing
💡Stable Diffusion
💡Image Generation
💡Workflow
💡ONNX
💡Precision Calibration
💡Graph Optimization
💡Dynamic Tensor Memory
Highlights
TensorRT has been integrated with ComfyUI for improved real-time performance in image generation.
Significant performance boost is observed with Stable Diffusion 1.5 and Realistic Vision 6 using TensorRT.
Batch processing of images sees a time-saving of 20 minutes and 50 seconds per 100 images with TensorRT.
SDXL and Stable Video Diffusion models benefit from a 28% speed increase with TensorRT.
SDXL Turbo offers the least performance improvement, saving only 11 minutes per 100 images.
Stable Video Diffusion 14 frame model achieves up to a 29% performance boost with TensorRT.
Stable Diffusion 3 exhibits the most significant performance improvement, being up to 32% faster with TensorRT.
A step-by-step guide on creating a TensorRT workflow in ComfyUI is provided.
TensorRT is a custom node in ComfyUI compatible with all Nvidia RT X GPUs.
The workflow includes Boolean and logic nodes for automation.
The benefits of using TensorRT in a business environment are highlighted, emphasizing time and resource savings.
TensorRT optimizes models by converting them into a format called ONNX for better GPU execution.
Precision Calibration and Graph Optimization are key processes in TensorRT's model optimization.
Layer Fusion and Dynamic Tensor Memory are techniques used to improve model performance.
Kernel Autotuning in TensorRT selects the best algorithms for model operations to optimize performance on specific GPU hardware.
An analogy of building a house is used to simplify the understanding of how TensorRT optimizes models.
Certain limitations of TensorRT in ComfyUI are discussed, including compatibility issues with some features.
A comprehensive workflow for building TensorRT engine files is demonstrated, including setting up control panels and organizing nodes.
The tutorial concludes with practical advice on using TensorRT for different models and scenarios in ComfyUI.