NVIDIA TensorRT Accelerates Stable Diffusion Nearly 2x Faster with 8-bit Post-Training Quantization

In the dynamic realm of generative AI, diffusion models stand out as the most powerful architecture for generating high-quality images with text prompts.

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NVIDIA
•
Zhiyu Cheng
•6 min read•intermediate•
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•View Original
PyTorchStable Diffusion

Overview

The article discusses how NVIDIA TensorRT accelerates the inference speed of Stable Diffusion models using 8-bit post-training quantization, achieving nearly 2x faster performance while maintaining image quality. It highlights the effectiveness of TensorRT's quantization techniques and provides a practical guide for implementation.

What You'll Learn

1

How to implement 8-bit post-training quantization with TensorRT for Stable Diffusion models

2

Why TensorRT's Percentile Quant approach improves image quality in generative AI applications

3

How to measure inference speed improvements using TensorRT on NVIDIA GPUs

Prerequisites & Requirements

  • Understanding of generative AI and diffusion models
  • Familiarity with NVIDIA TensorRT and ONNX(optional)

Key Questions Answered

How much faster does TensorRT make Stable Diffusion inference compared to native PyTorch?
NVIDIA TensorRT achieves speedups of 1.72x with INT8 and 1.95x with FP8 on NVIDIA RTX 6000 Ada GPUs compared to native PyTorch's torch.compile running in FP16. This significant improvement enhances the responsiveness of generative AI applications.
What is the Percentile Quant method in TensorRT?
Percentile Quant is a tailored approach developed by TensorRT that focuses on the important percentile of the steps range during quantization. This method minimizes quantization errors and preserves image quality, resulting in images that closely resemble those generated in FP16 precision.
What are the main steps to use TensorRT for accelerating diffusion models?
The main steps include calibrating the model, exporting it to ONNX format, and building the TensorRT engine. This process allows developers to optimize their models for faster inference on NVIDIA GPUs.

Key Statistics & Figures

Speedup with TensorRT INT8
1.72x
Compared to native PyTorch's torch.compile running in FP16.
Speedup with TensorRT FP8
1.95x
Compared to native PyTorch's torch.compile running in FP16.

Technologies & Tools

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Backend
Nvidia Tensorrt
Used for accelerating inference speed of Stable Diffusion models through quantization.
Tools
Onnx
Used for exporting models to a format compatible with TensorRT.

Key Actionable Insights

1
Implementing TensorRT's 8-bit quantization can significantly reduce inference time for generative AI applications.
By adopting this quantization technique, developers can enhance the performance of their models, making them more efficient and cost-effective in production environments.
2
Utilizing the Percentile Quant method allows for better image quality preservation during model quantization.
This approach is particularly beneficial for applications where maintaining visual fidelity is crucial, such as in creative industries.
3
Benchmarking inference speed is essential to evaluate the effectiveness of optimization techniques.
Regularly measuring performance metrics helps developers identify bottlenecks and improve their models iteratively.

Common Pitfalls

1
Users may struggle with manually defining parameters for quantization techniques like SmoothQuant.
This can lead to suboptimal performance and image quality. To avoid this, leveraging TensorRT's fine-grained tuning pipeline can help automate the process and yield better results.

Related Concepts

Generative AI
Diffusion Models
Quantization Techniques
Nvidia Hardware Optimization