Generative Training
Iterative Latent Variable Refinement
Introduced by Choi et al. in ILVR: Conditioning Method for Denoising Diffusion Probabilistic ModelsIterative Latent Variable Refinement, or ILVR, is a method to guide the generative process in denoising diffusion probabilistic models (DDPMs) to generate high-quality images based on a given reference image. ILVR conditions the generation process in well-performing unconditional DDPM. Each transition in the generation process is refined utilizing a given reference image. By matching each latent variable, ILVR ensures the given condition in each transition thus enables sampling from a conditional distribution. Thus, ILVR generates high-quality images sharing desired semantics.
Source: ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models
Papers
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Usage Over Time
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