Text-Driven Image Editing Based on Diffusion Models

In recent years, large-scale text-driven image synthesis models have garnered widespread attention for their ability to generate highly diverse images from textual prompts. Techniques like DALL.E2 and CLIP have shown significant success in producing high-quality images from descriptions. However, the challenge of achieving precise and creative image editing while preserving the original content persists. This study proposes a novel image editing method based on the Prompt-to-Prompt approach combined with large language models. The method aims to provide an intuitive and efficient editing experience, allowing users to perform detailed modifications without needing professional expertise in image processing. By leveraging advanced diffusion models, this approach enhances the flexibility of text-driven image editing, making it more accessible to a broader audience.