---
title: Consistency Models
type: vocabulary
url: "https://www.envisioning.com/vocab/consistency-models"
summary: Generative models trained so any point on a diffusion trajectory maps directly to its endpoint in one step, collapsing multi-step denoising into single-step generation.
year: 2023
generality: 0.55
---

# Consistency Models

Generative models trained so any point on a diffusion trajectory maps directly to its endpoint in one step, collapsing multi-step denoising into single-step generation.
Consistency models are generative models trained to map any point on a diffusion process's noise-to-data trajectory directly to that trajectory's endpoint, in a single evaluation. Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever introduced them in 2023 (arXiv:2303.01469). The model's core requirement, called consistency, is that mapping two different points on the same trajectory should produce the same output. This lets it collapse the many small denoising steps a diffusion model normally takes into one step, while still allowing extra steps to trade compute for quality.

A consistency model can be trained by distilling an already-trained diffusion model, using that model's own trajectories as a training signal. It can also be trained from scratch as a standalone generative model with no diffusion teacher. Trained by distillation, consistency models set a new state of the art for one-step sampling on ImageNet and CIFAR-10 at the time. Trained standalone, they outperformed prior one-step, non-adversarial generative models on the same benchmarks. They also carry over diffusion models' capacity for zero-shot editing tasks like inpainting and colorization without task-specific training.

Consistency models established few-step and one-step generation as a distinct research area alongside standard multi-step diffusion. Later work such as consistency trajectory models and latent consistency models applies the same idea inside latent diffusion pipelines for image and video generation. Recent methods avoid a distillation stage altogether, learning a one-step map directly from data, and position themselves explicitly against this original distillation-based formulation.

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Source: Envisioning — Technology Research Institute (https://www.envisioning.com/vocab/consistency-models)