SILO solves inverse problems with latent diffusion models by replacing pixel-space consistency with a learned latent degradation operator, cutting runtime and improving perceptual metrics at the cost of pixel fidelity.
Decom- posed Diffusion Sampler for Accelerating Large-Scale In- verse Problems
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SILO: Solving Inverse Problems with Latent Operators
SILO solves inverse problems with latent diffusion models by replacing pixel-space consistency with a learned latent degradation operator, cutting runtime and improving perceptual metrics at the cost of pixel fidelity.