Training a latent diffusion model differs from training standard models primarily in how the data is represented and processed. In a standard diffusion model, the training involves refining an output through multiple noise addition and removal steps directly in the data space. This process can be computationally intensive since it operates on high-dimensional data, such as images. In contrast, a latent diffusion model first transforms input data into a lower-dimensional latent space using an encoder, which helps in reducing the complexity of the learning task. This means that instead of working with raw images straight away, the model learns to generate and manipulate these representations, making the overall training process more efficient.
The training steps for a latent diffusion model involve several key components. First, you need to pre-train an encoder, usually a variational autoencoder (VAE), to learn the compressed representation of the input data. After this, the model runs diffusion processes in the latent space. This requires defining a noise schedule and applying a forward diffusion process that progressively adds noise to the latent representations. The model then learns to reverse this diffusion, effectively recovering the latent representations from their noisy versions. By focusing on this latent space, the model can reduce the necessary computational resources and improve its performance compared to models that operate directly on high-resolution input.
Once the model is trained in the latent space, you will need to handle the decoding to bring the contents back into the original data representation, like reconstructing an image from its latent encoding. This typically involves using the decoder from the pre-trained VAE. During inference, the model generates new latent variables, which the decoder then transforms into the final output data. This structured approach not only accelerates the training process but also provides greater flexibility and effectiveness in generating high-quality outputs while maintaining the resource efficiency that is often critical in deployment scenarios.