Stable Diffusion is a revolutionary AI model that can generate high-quality images from text descriptions. Unlike traditional image generation methods that work directly with pixels, Stable Diffusion operates in a compressed latent space, making it much more efficient. The process starts with a text prompt, which guides the transformation of random noise into a coherent image through an iterative denoising process.
The first step in Stable Diffusion is text encoding. When you provide a text prompt like 'a cat sitting on a chair', it needs to be converted into a format the AI can understand. A text encoder, typically CLIP, processes the words and converts them into numerical embeddings. These embeddings are vectors that capture the semantic meaning of your description and will guide the entire image generation process.
The second step involves working in what's called latent space. Instead of generating images directly at full resolution, Stable Diffusion works in a compressed representation that's eight times smaller in each dimension. For example, a 512 by 512 pixel image becomes a 64 by 64 latent representation. The process begins by filling this latent space with random noise. This approach is much more efficient than working with full-size images.
The third step is the iterative denoising process performed by a U-Net neural network. Starting with the random noise in latent space, the U-Net predicts what noise to remove at each step, guided by the text embeddings from step one. This process typically takes twenty to fifty iterations, gradually transforming the noise into a structured representation that matches the text description. The text guidance ensures the final result aligns with your original prompt.
To summarize how Stable Diffusion works: First, text prompts are encoded into numerical embeddings. Second, the process begins with random noise in a compressed latent space. Third, a U-Net neural network iteratively removes noise guided by the text embeddings. Finally, the denoised latent representation is decoded back into a high-resolution image. This efficient approach has revolutionized AI image generation.