June 2024
Intermediate to advanced
352 pages
8h 8m
English
Content preview from Using Stable Diffusion with Python
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7
Optimizing Performance and VRAM Usage
In the previous chapters, we covered the theory behind the Stable Diffusion models, introduced the Stable Diffusion model data format, and discussed conversion and model loading. Even though the Stable Diffusion model conducts denoising in the latent space, by default, the model’s data and execution still require a lot of resources and may throw a CUDA Out of memory error from time to time.
To enable fast and smooth image generation using Stable Diffusion, there are some techniques to optimize the overall process, boost the inference speed, and also reduce VRAM usage. In this chapter, we are going to cover the following optimization solutions and discuss how well these solutions work in practice:
- Using ...
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I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.