What is Stable Diffusion?

Stable Diffusion is an open-source latent diffusion model that generates high-quality images from text descriptions through a denoising process. This deep learning architecture operates in latent space rather than pixel space, making it computationally efficient while producing detailed visual outputs. The model uses a variational autoencoder to compress images into lower-dimensional representations, then applies a U-Net neural network to progressively remove noise guided by text embeddings. Stable Diffusion employs CLIP (Contrastive Language-Image Pre-training) for text encoding, enabling precise semantic understanding of prompts. Unlike proprietary alternatives, its open-source nature allows customization, fine-tuning, and integration into diverse applications. The model supports various sampling methods including DDIM and DPM-Solver, offering control over generation speed and quality. Its architecture enables inpainting, outpainting, and image-to-image translation, making it versatile for creative workflows, content generation, and AI-powered design systems.