How to Run Stable-Diffusion using TensorRT and ComfyUI

Brev
7 Jun 202414:26

TLDRIn this tutorial, Carter, a founding engineer at Brev, demonstrates how to utilize ComfyUI and Nvidia's TensorRT for rapid image generation with Stable Diffusion. He guides viewers through setting up the environment on Brev, deploying a launchable, and optimizing the model for faster inference. The video showcases the process from initial setup to generating images based on prompts, highlighting the impressive speed gains achieved with TensorRT.

Takeaways

  • 😀 The video demonstrates how to run Stable Diffusion with ComfyUI and TensorRT, an inference engine by Nvidia.
  • 🚀 TensorRT is used to optimize model inference for speed, which is crucial for applications requiring quick image generation.
  • 🛠️ The tutorial is given by Carter, a founding engineer at Brev, who guides through setting up the environment for image generation using ComfyUI and TensorRT.
  • 🔗 A 'launchable' is introduced as a way to package hardware, software, and container into a clickable link for easy deployment.
  • 💻 The process involves deploying on an Nvidia RTX A6000 GPU, which is capable of fast inference when using TensorRT.
  • 📈 The video shows the speed comparison between generating images with and without TensorRT optimization.
  • 📚 A Jupyter notebook is used to set up the environment, which includes installing ComfyUI, downloading the Stable Diffusion model, and preparing TensorRT.
  • 🎨 ComfyUI is described as a GUI for creating complex workflows, which can be used for applications like AI-generated headshots.
  • 🔧 The script details the steps to optimize Stable Diffusion using TensorRT, including building an engine for faster inference.
  • 💡 The benefits of using TensorRT are highlighted, such as the ability to hyper-optimize models for specific hardware, resulting in faster image generation.
  • 👍 The video concludes by showing the improved speed of image generation after TensorRT optimization, demonstrating the power of leveraging TensorRT with Stable Diffusion and ComfyUI.

Q & A

  • What is the main topic of the video script?

    -The main topic of the video script is how to run Stable Diffusion using TensorRT and ComfyUI for fast image generation.

  • Who is the presenter in the video script?

    -The presenter is Carter, a founding engineer at Brev.

  • What is a 'launchable' as mentioned in the script?

    -A 'launchable' is a method to package up both hardware and software into a link, allowing users to easily deploy and run the same environment as demonstrated in the video.

  • What is TensorRT and how does it relate to the video's content?

    -TensorRT is an inference engine created by Nvidia that optimizes deep learning models for fast execution on Nvidia GPUs. It is used in the video to accelerate the image generation process with Stable Diffusion.

  • What is ComfyUI and how is it used in the video?

    -ComfyUI is a graphical user interface that allows users to create complex workflows for image generation. It is used in the video to generate images based on prompts using Stable Diffusion with TensorRT.

  • What is the purpose of the notebook in the video script?

    -The notebook is used to set up the environment for running ComfyUI and Stable Diffusion with TensorRT, including installing necessary components and preparing the model for image generation.

  • How does the video demonstrate the speed improvement with TensorRT?

    -The video demonstrates the speed improvement by comparing the time it takes to generate images using the base Stable Diffusion model without TensorRT and then with TensorRT optimization.

  • What is the cost implication mentioned in the video for using the service?

    -The video mentions that the instance used for the demonstration costs about 56 cents an hour, suggesting that the entire process could be done for approximately a dollar.

  • How does the video script guide users to follow along with the process?

    -The video script provides a step-by-step guide, including links to resources, instructions on deploying a launchable, and running cells in a notebook to set up the environment for image generation.

  • What is the significance of building the TensorRT engine in the video?

    -Building the TensorRT engine is significant because it optimizes the Stable Diffusion model for fast inference on the specific hardware, resulting in quicker image generation after the initial setup.

  • How does the video script conclude?

    -The video script concludes by showing the improved speed of image generation with TensorRT, encouraging viewers to delete their instances when done to avoid extra charges, and inviting feedback for future guides.

Outlines

00:00

🚀 Introduction to Running Stable Diffusion with Comfy UI and Tensor RT

The video script introduces a tutorial on how to use Stable Diffusion, a powerful image generation model, with Comfy UI and Nvidia's Tensor RT for accelerated inference. The presenter, Carter, a founding engineer at Brev, demonstrates how to set up and deploy a launchable on Brev, which includes an Nvidia RTX A6000 GPU, a container, and software. The process involves creating an account, deploying the launchable, and setting up the environment to run Comfy UI for image generation. The script also mentions the cost-effectiveness of the process and provides a link to a blog for further understanding of the technologies used.

05:01

🛠 Setting Up Comfy UI and Tensor RT for Image Generation

This paragraph delves into the technical setup process for using Comfy UI and Tensor RT with Stable Diffusion. It explains the workflow involved in image generation, including the use of the Stable Diffusion XL turbo model. The script describes how inference works in the context of image generation, with the model turning prompts into images. It also details the process of building a Tensor RT engine for optimized inference speeds, which is specific to the hardware it's built on. The paragraph includes a demonstration of generating images with and without the Tensor RT engine, showcasing the significant speed improvements achieved with Tensor RT.

10:03

🔧 Optimizing Inference Speeds with Tensor RT and Comfy UI

The final paragraph focuses on the optimization of inference speeds using Tensor RT with Stable Diffusion through Comfy UI. It explains how Tensor RT hyper-optimizes the model for specific hardware, resulting in faster image generation. The script provides a step-by-step guide on building the Tensor RT engine, which includes downloading necessary files and loading them into the workflow. After the engine is built, the script demonstrates the increased speed of image generation with prompts, comparing it to the baseline without Tensor RT. The video concludes with a summary of the session's activities, the time taken, and an invitation for viewers to request more guides, subscribe, and explore Tensor RT further through provided resources.

Mindmap

Keywords

💡Stable Diffusion

Stable Diffusion is a type of generative model that uses artificial intelligence to create images from textual descriptions. It is a significant part of the video's theme as it is the AI model being optimized for faster inference using TensorRT. The script mentions using Stable Diffusion to generate images based on prompts, demonstrating its application in creating unique visuals like 'a handsome Lebanese man making a coding tutorial'.

💡TensorRT

TensorRT is an inference engine developed by Nvidia that optimizes deep learning models to run quickly on Nvidia GPUs. It is central to the video's narrative as it is used to enhance the performance of Stable Diffusion. The script explains that TensorRT packages the model into an engine to accelerate inference times, which is crucial for applications requiring rapid image generation.

💡ComfyUI

ComfyUI is a graphical user interface that simplifies the creation of complex workflows, particularly for image generation tasks. It is highlighted in the video as a user-friendly tool that interfaces with both Stable Diffusion and TensorRT. The script describes using ComfyUI to generate images with specific styles or poses, showcasing its capabilities in creating customized AI-generated content.

💡Inference

Inference in the context of AI refers to the process of making predictions or generating outputs based on input data. The video discusses how TensorRT optimizes inference for Stable Diffusion, making it faster. The script provides an example of inference in image generation, where the AI takes a prompt and turns it into an image.

💡Nvidia RTX

Nvidia RTX is a series of graphics processing units (GPUs) known for their capabilities in handling complex computational tasks, including AI model inference. The video script mentions using an Nvidia RTX A6000 GPU to run the Stable Diffusion model with TensorRT, emphasizing the importance of powerful hardware for accelerating AI processes.

💡Brev

Brev is a platform for deploying and managing machine learning models, which is used in the video to demonstrate the setup and execution of the AI workflow. The script explains how Brev facilitates the process of launching the Stable Diffusion model with TensorRT on an Nvidia RTX GPU, highlighting its role in simplifying the deployment of AI applications.

💡Launchable

A launchable, as mentioned in the script, is a term used to describe a packaged solution that includes hardware, software, and other necessary components for running a specific application or model. In the video, the Stable Diffusion model with TensorRT is made into a launchable, allowing users to easily deploy and run the AI workflow on Brev.

💡Workflow

In the context of the video, a workflow refers to a sequence of steps or processes involved in generating an output, such as an image, from a given input. The script describes using ComfyUI to create workflows for image generation with Stable Diffusion, where each node represents a different stage in the process.

💡Batch Size

Batch size in AI and machine learning refers to the number of inputs processed at one time. The video discusses optimizing the Stable Diffusion model for a batch size of four, meaning the model generates four images at a time. This is an important parameter for optimizing performance and throughput in image generation tasks.

💡Checkpoints

Checkpoints in machine learning are saved states of a model during training, which can be loaded to resume training or for inference. The script mentions loading checkpoints for the Stable Diffusion model, which is a necessary step before performing inference with the model in ComfyUI.

💡Denoising

Denoising in the context of image generation with Stable Diffusion refers to the process of transforming a noisy or random image into a coherent and clear output based on a given prompt. The script explains that Stable Diffusion starts with a noisy image and iteratively refines it to match the desired prompt, such as creating an image of 'Mickey Mouse in front of the Eiffel Tower'.

Highlights

How to run Stable-Diffusion using ComfyUI with TensorRT for fast inference.

Comparison of inference speed with and without TensorRT.

Introduction to Carter, a founding engineer at Brev, guiding through the setup.

Explanation of a 'launchable' as a packaged solution combining hardware, software, and container.

Demonstration of deploying a launchable on Brev using an Nvidia RTX A6000 GPU.

TensorRT's role in optimizing model inference for Nvidia hardware.

Process of setting up the environment for ComfyUI using a Jupyter notebook.

ComfyUI as a GUI for creating complex image generation workflows.

Tutorial on generating images with Stable Diffusion based on a textual prompt.

The cost-effectiveness of running the demo on Brev's platform.

Details on installing and setting up ComfyUI and Stable Diffusion model.

Inference process explanation using Stable Diffusion XL turbo model.

Demonstration of image generation without TensorRT and the time taken.

Building and loading TensorRT engine for optimized inference speed.

Comparison of image generation speed with TensorRT optimization.

Creating various images using different prompts to showcase the flexibility.

Discussion on the implications of fast image generation for applications.

Instructions on how to access and use ComfyUI interface for image generation.

Final thoughts on leveraging TensorRT for Stable Diffusion with ComfyUI on Brev.

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