Unlock Faster Image Generation in Stable Diffusion Web UI with NVIDIA TensorRT

Unlocking Faster Image Generation in Stable Diffusion Web UI with NVIDIA TensorRT

Summary

Stable Diffusion, an open-source generative AI model, can be significantly accelerated using NVIDIA TensorRT. This article explores how TensorRT can enhance the performance of Stable Diffusion, particularly in the Web UI, and provides insights into its implementation and benefits.

Understanding Stable Diffusion and NVIDIA TensorRT

Stable Diffusion is a powerful tool for generating images based on text descriptions. It uses diffusion processes to create images, but this process can be computationally intensive. NVIDIA TensorRT is a software development kit (SDK) that provides tools for optimizing deep learning models, including Stable Diffusion. By leveraging TensorRT, users can significantly speed up image generation in Stable Diffusion.

How TensorRT Accelerates Stable Diffusion

TensorRT offers several features that accelerate Stable Diffusion:

• Layer Fusion: Combines multiple layers into a single operation, reducing overhead and improving performance.
• Precision Calibration: Adjusts the precision of model weights and activations to reduce memory usage and increase speed.
• Kernel Auto-Tuning: Automatically selects the best kernel for each operation, ensuring optimal performance on NVIDIA GPUs.

Implementing TensorRT in Stable Diffusion Web UI

To integrate TensorRT into Stable Diffusion Web UI, users need to install and optimize the required engine. This process involves:

1. Installation: Download and install the TensorRT extension for Stable Diffusion Web UI.
2. Optimization: Run the optimization script to create optimized engines for your specific GPU.

Benefits of Using TensorRT with Stable Diffusion

Using TensorRT with Stable Diffusion offers several benefits:

• Faster Image Generation: TensorRT can double the speed of image generation in Stable Diffusion.
• Improved Performance: TensorRT optimizations can reduce the number of sampling steps needed, further improving performance.
• Real-Time Image Generation: With TensorRT, users can generate images in real-time, particularly with models like SDXL Turbo.

New Stable Diffusion Models Accelerated by TensorRT

NVIDIA has announced several new Stable Diffusion models that are accelerated by TensorRT:

• SDXL Turbo: Achieves state-of-the-art performance with a new distillation technology, enabling single-step image generation.
• LCM-LoRA: Uses a training technique for fine-tuning Stable Diffusion models, reducing the number of sampling steps needed.
• Stable Video Diffusion: Runs up to 40% faster with TensorRT, potentially saving up to minutes per generation.

Performance Analysis

A performance analysis conducted by Puget Systems found that the TensorRT extension for Stable Diffusion Web UI provided significant performance gains:

• 2.3x Increase: Over a basic Automatic 1111 installation on an RTX 4090.
• 1.7x Increase: Compared to a setup utilizing xFormers.

Table: Performance Comparison

Table: New Stable Diffusion Models

Table: Benefits of Using TensorRT

Conclusion

NVIDIA TensorRT is a powerful tool for accelerating Stable Diffusion, particularly in the Web UI. By leveraging TensorRT, users can significantly speed up image generation, making it possible to generate images in real-time. With the new Stable Diffusion models accelerated by TensorRT, users can enjoy even faster performance and improved workflows.