AI & Machine Learning

Guiding a Diffusion Model by Swapping Its Tokens

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Researchers have developed Self-Swap Guidance (SSG), a technique that enhances Classifier-Free Guidance for both conditional and unconditional image generation by swapping semantically dissimilar token latents. This method improves the fidelity of generated images and reduces side effects, making it more versatile and robust than existing approaches. Developers can integrate SSG into diffusion models to enhance their performance without significant modifications.

Read the full article at arXiv cs.CV (Vision)

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Guiding a Diffusion Model by Swapping Its Tokens

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