Stable Diffusion
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images.
About
Stable Diffusion is a deep learning, text-to-image model that can generate high-quality images based on text descriptions, as well as other tasks such as inpainting, outpainting, and image-to-image translations. It uses a latent diffusion architecture, which consists of three parts: the variational autoencoder, U-Net, and an optional text encoder. Stable Diffusion was trained on pairs of images and captions taken from the LAION-5B dataset, which consists of 5 billion image-text pairs classified by resolution and aesthetic score. The model weights and code are publicly available, and it can run on most consumer hardware with a modest GPU of at least 8 GB VRAM. Stable Diffusion is a powerful tool for generating high-quality images based on text prompts, and its architecture offers computational efficiency for training and generation.
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