The model for Stable Diffusion Inpainting, a latent text-to-image diffusion model that can generate photo-realistic images based on any text input and has the additional capability of inpainting pictures using a mask. The model was initialized with the weights of Stable-Diffusion-v-1-2 and underwent regular training for 595k steps followed by inpainting training for 440k steps at a resolution of 512x512 using the "laion-aesthetics v2 5+" dataset. The model also underwent 10% dropping of text-conditioning to improve classifier-free guidance sampling. For inpainting, the UNet has 5 additional input channels, and synthetic masks were generated during training, with 25% of the input masked.