The field of image editing is rapidly advancing with the introduction of diffusion models, which have shown remarkable success in text-to-image generation. Current research is focused on improving the capabilities of these models to handle complex editing tasks, such as non-rigid motions, object deformations, and content generation. One of the key challenges in this area is the ability to preserve the structure, texture, and identity of the source image while making significant edits. To address this, researchers are exploring the use of correspondence-aware noise correction, interpolated attention maps, and semantic encoders to better understand the relationships between different parts of the image. Another area of research is the development of controllable diffusion models that can generate high-quality text in multiple languages and layouts. Noteworthy papers in this area include Cora, which introduces a novel editing framework that addresses the limitations of existing few-step editing approaches, and EasyText, which proposes a text rendering framework based on Diffusion Transformer. Additionally, RelationAdapter and ByteMorph are notable for their work on visual relation transfer and instruction-guided image editing with non-rigid motions, respectively. UniWorld and RefEdit are also significant, as they present unified generative frameworks and benchmarks for image editing and referring expression tasks. Image Editing As Programs and SeedEdit 3.0 demonstrate the potential of diffusion models in handling structurally inconsistent edits and improving edit instruction following. MARBLE is also an important contribution, as it enables material blending and recomposition in CLIP-space.