Latent Diffusion-based Model
(Redirected from latent diffusion techniques)
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A Latent Diffusion-based Model is a generative model that functions by applying diffusion processes in a latent space.
- Context:
- It can (typically) generate high-resolution images by gradually adding noise and then learning to reverse this process to create coherent images.
- It can (often) be used for tasks like text-to-image generation, image inpainting, style transfer, and image super-resolution.
- It can be more efficient than traditional diffusion models because it operates in a lower-dimensional latent space, making it computationally less intensive.
- ...
- Example(s):
- Counter-Example(s):
- a Traditional Diffusion Model that operates directly on image pixels (without the use of a latent space).
- See: Diffusion Model, Generative Model, Variational Autoencoder.