Diffusion models have emerged as invaluable tools for modern-day Da Vinci’s, revolutionizing image generation by producing intricate, composable visual outputs. With a diverse array of over 100 diffusion models available, each possessing its unique stylistic prowess, creators can seamlessly choose a model and prompt it to generate their desired artwork. Given the computational intensity of diffusion models, they find a natural ally in GPUs, capitalizing on their processing power for enhanced performance.

This enlightening session aims to delve into the art of optimizing the latest iteration of stable diffusion models, including SDXL-1.0, using the powerful capabilities of NVIDIA TensorRT. In the realm of image generation, achieving the desired output often necessitates multiple prompts. Moreover, in scenarios where numerous creators concurrently request multiple images, the imperative of optimizing comes to the forefront. By meticulously leveraging TensorRT's optimization techniques, attendees will witness a significant reduction in inference times for image generation in comparison to CPU-based execution and unoptimized models.

Who should attend:

1. A data scientist who is interested in developing and deploying large-scale SD models
2. Designers (2D/3D, interior designers) with the ability to quickly produce artifacts

• Generating such realistic images requires a high level of computation, which explains the role of GPUs

• How GPUs can satisfy such compute requirements and generate images even more rapidly

• How to optimize the existing models using NVIDIA SDK so that it runs even faster on a single GPU or on hundreds of GPUs at once

Content

content goes here