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Diffusion Models Outperform GANs by KaiWang is a document available to read on EtoBox.
What is Diffusion Models Outperform GANs about?
Diffusion models generate images by gradually adding noise to images over multiple time steps, and then learning to reverse the process by denoising images starting from pure noise. Recent improvements include using a cosine rather than linear noise schedule, learning the covariance matrix, and injecting classifier gradients to improve fidelity. Latent diffusion models separate image generation into a perceptual compression stage and diffusion stage in latent space to improve efficiency.
- Author
- KaiWang
- Language
- EN