A unified framework and targeted changes to parameterization, architecture, and objectives stabilize continuous-time consistency models, achieving FID scores of 2.06 on CIFAR-10 and 1.88 on ImageNet 512x512 with two-step sampling at 1.5B scale.
As shown in Table 3, our proposed sCT significantly outperforms ECT during the training, demonstrating the effectiveness of the compute efficiency and faster convergence of sCT
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Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models
A unified framework and targeted changes to parameterization, architecture, and objectives stabilize continuous-time consistency models, achieving FID scores of 2.06 on CIFAR-10 and 1.88 on ImageNet 512x512 with two-step sampling at 1.5B scale.