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Loading...Introduction to Image Synthesis
Last quarter, our team discovered the power of image synthesis using diffusion models and generative adversarial networks (GANs). We were tasked with generating high-quality images for a client's marketing campaign, and after exploring various options, we decided to compare the performance of Stable Diffusion 2.1 and StyleGAN3. In this article, I'll share our findings, including the strengths and weaknesses of each approach, and provide a step-by-step guide on how to implement them.
Background on Diffusion Models and GANs
Diffusion models, such as Stable Diffusion 2.1, have gained popularity in recent years due to their ability to generate high-quality images with minimal training data. These models work by iteratively refining the input noise signal until it converges to a realistic image. On the other hand, GANs, like StyleGAN3, consist of a generator network that produces synthetic images and a discriminator network that evaluates the generated images and tells the generator whether they are realistic or not.
Implementing Stable Diffusion 2.1
To implement Stable Diffusion 2.1, we started by installing the required libraries, including PyTorch and the Stable Diffusion 2.1 repository.
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