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Beyond Diffusion: Inductive Moment Matching
Inductive Moment Matching surpasses diffusion models in speed and sample quality.
This stagnation has created a bottleneck that prevents us from fully unlocking the potential of rich multi-modal data, which in turn limits the progress on multimodal intelligence. In contrast to consistency models (CMs), which are unstable as a pre-training technique and require special hyperparameter designs, IMM employs a single objective with enhanced stability across diverse settings. This constrains the expressive capacity of each iteration as it is linear with respect to the next timestep, ultimately capping performance regardless of the training method employed (see figure below).
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