Generative models mapping noise to data, often enabling faster high-quality generation than diffusion models.
Detailed Explanation
Consistency Models are a class of generative models in deep learning that transform random noise directly into realistic data samples. They achieve high-quality outputs with faster generation times compared to diffusion models by utilizing consistency training methods, which ensure the model's outputs remain stable and coherent across different perturbations, improving both efficiency and sample fidelity in tasks like image synthesis.
Use Cases
•Generate high-quality images rapidly for real-time content creation in multimedia applications.