Editing images using Stable Diffusion
Do you recall the background swap example we discussed in Chapter 1? In this section, we will introduce a solution that can assist you in editing the content of an image.
Before we can edit anything, we need to identify the boundary of the object we want to edit. In our case, to obtain the background mask, we will use the CLIPSeg [1] model. CLIPSeg, which stands for CLIP-based Image Segmentation, is a model trained to segment images based on text prompts or reference images. Unlike traditional segmentation models that require a large amount of labeled data, CLIPSeg can achieve impressive results with little to no training data.
CLIPSeg builds upon the success of CLIP, the same model used by SD. CLIP is a powerful pre-trained model that learns to connect text and images. The CLIPSeg model adds a small decoder module on top of CLIP, allowing it to translate the learned relationships into pixel-level segmentation. This means we can provide CLIPSeg...
