Latent diffusion models are a type of generative model that operate in a compressed, latent space rather than directly in pixel space. These models are designed to progressively refine random noise into coherent data samples, such as images or audio, by manipulating representations in a lower-dimensional space. In contrast, pixel-space diffusion models work directly on the high-dimensional pixel data of images, making them computationally intensive and often slower to train. By switching to latent space, latent diffusion models reduce the workload, allowing faster and more efficient training and generation while maintaining high-quality outputs.
The main difference between latent diffusion models and pixel-space diffusion lies in their approach to data representation and processing. Pixel-space diffusion models treat images as arrays of pixels, which can be cumbersome due to the high dimensionality involved, especially for large images. They apply transformations directly on these pixel values throughout the diffusion process. On the other hand, latent diffusion models first encode images into a lower-dimensional latent space using an encoder, such as a Variational Autoencoder (VAE). The diffusion process then operates on these compact representations, which simplifies calculations and speeds up the overall process.
A practical example of this difference can be seen when generating images using deep learning frameworks. A pixel-space model might take several hours to produce high-resolution images because it manipulates millions of pixel values directly. In contrast, a latent diffusion model can achieve similar results in a fraction of the time by leveraging efficient latent space operations. This is particularly useful for developers looking to create applications that require real-time image synthesis, such as in video games or interactive art installations, where performance is crucial. By understanding these distinctions, developers can better choose the appropriate model based on their project’s requirements and available resources.