HRSino is a training-free adaptive diffusion inference approach for high-resolution sinogram completion that reduces peak memory by up to 30.81% and inference time by up to 17.58% while maintaining accuracy.
Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps
2 Pith papers cite this work. Polarity classification is still indexing.
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2ndMatch finetunes pruned diffusion models via second-order Jacobian matching inspired by Finite-Time Lyapunov Exponents to reduce the quality gap with dense models on image generation tasks.
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Training-Free Inference for High-Resolution Sinogram Completion
HRSino is a training-free adaptive diffusion inference approach for high-resolution sinogram completion that reduces peak memory by up to 30.81% and inference time by up to 17.58% while maintaining accuracy.
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2ndMatch: Finetuning Pruned Diffusion Models via Second-Order Jacobian Matching
2ndMatch finetunes pruned diffusion models via second-order Jacobian matching inspired by Finite-Time Lyapunov Exponents to reduce the quality gap with dense models on image generation tasks.