Diffusion models enhance the realism of AI-generated images by iteratively refining noise into coherent images through a series of transformations, leveraging probabilistic methods to capture intricate details and textures. These models, such as those used in Stable Diffusion, progressively improve image quality by reversing a diffusion process, which starts with pure noise and gradually reconstructs a realistic image.

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