Stable Diffusion is a type of latent diffusion model that can generate images from text. It was created by a team of researchers and engineers from CompVis, Stability AI, and LAION. Stable Diffusion v2 is a specific version of the model architecture. It utilizes a downsampling-factor 8 autoencoder with an 865M UNet and OpenCLIP ViT-H/14 text encoder for the diffusion model. When using the SD 2-v model, it produces 768x768 px images. It uses the penultimate text embeddings from a CLIP ViT-H/14 text encoder to condition the generation process.
Stable Diffusion 3 Large Text-to-Image (SD3 Large) is the latest and most advanced addition to the Stable Diffusion family of image-to-image models. The 8 billion parameter count in SD3 Large empowers it to tackle intricate tasks such as text understanding, typography, and generate highly detailed images. However, SD3 Large might require more powerful hardware to run smoothly. While optimized for performance, it may necessitate additional computational resources due to its larger size.
Detailed descriptions: You can provide detailed descriptions including objects, characters, settings, lighting, and even artistic styles. Stable Diffusion 3 can translate these descriptions into high-quality images.
Complex prompts: It can handle intricate prompts with multiple subjects and even account for slight variations in spelling or phrasing.
Photorealism: The model excels at generating images that are incredibly close to real photographs, overcoming artifacts often seen in hands and faces in previous versions.
Typography: It can render text within the generated images more accurately than previous models.
Take a picture/gif and replace the face in it with a face of your choice. You only need one image of the desired face. No dataset, no training
This model is capable of generating photo-realistic images given any text input, with the extra capability of inpainting the pictures by using a mask
The SDXL model is the official upgrade to the v1.5 model. The model is released as open-source software
Take a picture/gif and replace the face in it with a face of your choice. You only need one image of the desired face. No dataset, no training