7 Generate high-resolution images with diffusion models
This chapter covers
- The denoising diffusion implicit model (DDIM) noise scheduler
- Adding the attention mechanism in denoising U-Net models
- Generating high-resolution images with advanced diffusion models
- Interpolating initial noise tensors to generate a series of images that smoothly transition from one image to another
In the previous two chapters, you built a foundational understanding of diffusion models, learning how they add noise to clean images, then reverse this process to generate new images from pure noise. By utilizing the powerful denoising U-Net architecture, you saw how a model can be trained to transform pure noise into grayscale clothing-item images, step by step.
But what does it take to move from simple grayscale images to richly detailed, high-resolution color images? And how can we make these models not only more accurate, but also faster and more efficient at generating such images? This chapter tackles these questions by introducing advanced tools and techniques that are now the backbone of state-of-the-art text-to-image generators.