Consistency Models

Train a model to directly map noise to data in a single step through consistency distillation, enabling one-step generation with quality approaching multi-step diffusion. Consistency models enforce that the model produces the same output for any point along a diffusion trajectory — the “consistency” property. By learning this mapping, the model can jump from pure noise directly to the data distribution in a single step. Training uses either consistency distillation (from a pre-trained diffusion model) or consistency training (from scratch). Latent Consistency Models apply this in the latent space of an autoencoder for efficient text-to-image generation.