Latent Diffusion

Latent Diffusion compresses an image into a latent representation with a variational autoencoder, then performs denoising in that space while conditioning on text embeddings through cross attention. After the iterative denoise, the decoder reconstructs the final image. Working on latents cuts memory and compute while preserving detail, so higher resolutions and larger batches are practical even on consumer GPUs. The same architecture supports conditioning from prompts, masks, and reference images, which makes tasks like image to image translation, style transfer, inpainting, and super resolution feel unified. The approach became the backbone for Stable Diffusion and many open tools, since it is easy to fine tune, adapt with lightweight modules, and integrate into creative pipelines without heavy infrastructure.