Stable Diffusion에 관한 간략한 개념적 설명

Stable Diffusion, a groundbreaking text-to-image diffusion model, emerged in 2022 with the remarkable capability of generating photorealistic images from any given text input. This deep learning model has revolutionized tasks such as inpainting, outpainting, and image-to-image translations, all guided by text prompts.

With the Stable Diffusion tool, one can effortlessly remove unimportant parts from an image or add new features, resulting in a natural and seamless transformation. As a latent diffusion model, a specific type of deep generative artificial neural network, Stable Diffusion's code and model weights have been generously made public.

Despite the model's training process involving 256 Nvidia A100 GPUs and costing $600,000, it has been released as open-source. This move has democratized access to such advanced technology, as Stable Diffusion can operate on consumer hardware equipped with a modest GPU. This is a significant shift from previous models, where diffusion was recognized for its excellent image generation capabilities but was resource-intensive.

Stable Diffusion employs a latent diffusion architecture that amalgamates a variational autoencoder (VAE), U-Net, and an optional text encoder. It serves as a bridge between user-generated text and deep learning-produced images. The model processes human language, tokenizes it, and encodes images using random noises before decoding them. Astonishingly, this process results in the generation of the highest quality images, marking a significant milestone in the field of generating images.