Semantic Diffusion Models for Image Generation
Abstract
This paper investigates the application of diffusion models to image generation, focusing on incorporating semantic understanding for enhanced control and quality. Traditional diffusion models, while effective in generating realistic images, often lack precise control over the generated content. We propose a novel approach utilizing semantic annotations and context to guide the reverse diffusion process, effectively learning to generate images that align with specific desired properties. The core concept involves training a diffusion model where the denoising process is conditioned on semantic information, resulting in more targeted and controlled image generation. This method demonstrates the potential to achieve higher image quality and greater artistic control compared to standard diffusion models. The key innovations lie in the integration of semantic understanding within the diffusion framework, leading to a significantly improved generation process. We detail the model architecture, training methodology, and evaluation results, showcasing the effectiveness of this approach.
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