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.
Generative modeling by estimating gradients of the data distribution
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Improved MeanFlow (iMF) reaches 1.72 FID on ImageNet 256x256 with one function evaluation by reformulating the training objective as a regression on instantaneous velocity and treating guidance as flexible conditioning variables.
CR-Diff applies block-wise pruning followed by output amplification to diffusion models, improving consistency and fidelity at unseen resolutions while retaining default-resolution 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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Improved Mean Flows: On the Challenges of Fastforward Generative Models
Improved MeanFlow (iMF) reaches 1.72 FID on ImageNet 256x256 with one function evaluation by reformulating the training objective as a regression on instantaneous velocity and treating guidance as flexible conditioning variables.
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Cross-Resolution Diffusion Models via Network Pruning
CR-Diff applies block-wise pruning followed by output amplification to diffusion models, improving consistency and fidelity at unseen resolutions while retaining default-resolution performance.