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.
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Stable Diffusion 3 Large Image-to-Image is the latest and most advanced addition to the Stable Diffusion family of image-to-image models. Boasting a massive 8 billion parameters, SD3 Large offers significant improvements in image quality. The increased parameter count in SD3 Large empowers it to tackle intricate tasks and generate highly detailed images. However, this enhanced capability comes with a trade-off: 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.
Targeted edits: You can provide an existing image and use text prompts to specify the desired changes. This allows for edits like adding or modifying colors, or applying different artistic styles.
Versatility: It can be used for various image editing tasks, from simple tweaks to more creative manipulations.
Audio-based Lip Synchronization for Talking Head Video
Monster Labs QrCode ControlNet on top of SD Realistic Vision v5.1
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
CodeFormer is a robust face restoration algorithm for old photos or AI-generated faces.