AsymFlow uses rank-asymmetric velocity prediction to reach 1.57 FID on ImageNet 256x256 and enables finetuning of latent flow models into superior pixel-space text-to-image generators.
Score-based generative modeling through stochastic differential equations
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ADM uses interdependent score-based diffusion models and iterative Langevin sampling to achieve state-of-the-art alignment of SFI-UWFI retinal image pairs, with reported mAUC gains of 5.2 and 0.4 points over prior methods.
StitchVM stitches clean-image reward models with diffusion backbones to enable efficient value estimation for noisy latents, speeding up diffusion alignment methods like DPS by 3.2x and halving memory.
Particle Diffusion Matching uses diffusion-guided random walk searches to align challenging standard and ultra-widefield retinal images, claiming state-of-the-art benchmark performance.
citing papers explorer
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Asymmetric Flow Models
AsymFlow uses rank-asymmetric velocity prediction to reach 1.57 FID on ImageNet 256x256 and enables finetuning of latent flow models into superior pixel-space text-to-image generators.
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Active Diffusion Matching: Score-based Iterative Alignment of Cross-Modal Retinal Images
ADM uses interdependent score-based diffusion models and iterative Langevin sampling to achieve state-of-the-art alignment of SFI-UWFI retinal image pairs, with reported mAUC gains of 5.2 and 0.4 points over prior methods.
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Stitched Value Model for Diffusion Alignment
StitchVM stitches clean-image reward models with diffusion backbones to enable efficient value estimation for noisy latents, speeding up diffusion alignment methods like DPS by 3.2x and halving memory.
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Particle Diffusion Matching: Random Walk Correspondence Search for the Alignment of Standard and Ultra-Widefield Fundus Images
Particle Diffusion Matching uses diffusion-guided random walk searches to align challenging standard and ultra-widefield retinal images, claiming state-of-the-art benchmark performance.