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Abstract: Consistency Models (CM) (Song et al., 2023) accelerate scorebased diffusion model sampling at the cost of sample quality but lack a natural way to tradeoff quality for speed. To address this limitation, we propose Consistency Trajectory Model (CTM), a generalization encompassing CM and scorebased models as special cases. CTM trains a single neural network that can in a single forward pass output scores (i.e., gradients of logdensity) and enables unrestricted traversal between any initial and final time along the Probability Flow Ordinary Differential Equation (ODE) in a diffusion process. CTM enables the efficient combination of adversarial training and denoising score matching loss to enhance performance and achieves new stateoftheart FIDs for singlestep diffusion model sampling on CIFAR10 (FID 1.73) and ImageNet at 64X64 resolution (FID 2.06). CTM also enables a new family of sampling schemes, both deterministic and stochastic, involving long jumps along the ODE solution trajectories. It consistently improves sample quality as computational budgets increase, avoiding the degradation seen in CM. Furthermore, CTM's access to the score accommodates all diffusion model inference techniques, including exact likelihood computation.

Speaker: Jesse Lai

Twitter Hannes:   / hannesstaerk  
Twitter Dominique:   / dom_beaini  

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Chapters
00:00 Intro + Background
05:58 Consistency Trajectory Model
11:54 Student Beats Teacher
27:15 MultiStep Generation
40:10 Conclusion
42:27 Q&A