The field of image editing is experiencing significant advancements with the integration of diffusion models. Researchers are exploring innovative approaches to improve image generation, editing, and manipulation. A key direction is the development of methods that enable more precise control over the editing process, such as instruction-guided image editing and controllable video editing. Another area of focus is the improvement of image completion and inpainting techniques, which aim to seamlessly integrate generated content with existing images. The use of diffusion models is also being extended to tasks like mirror reflection generation and human matting, demonstrating the versatility and potential of these models.

Noteworthy papers include:

• Early Timestep Zero-Shot Candidate Selection for Instruction-Guided Image Editing, which introduces a zero-shot framework for selecting reliable seeds in image editing.
• From Missing Pieces to Masterpieces: Image Completion with Context-Adaptive Diffusion, which proposes a novel framework for image completion that ensures contextually aligned completion.
• MP-Mat: A 3D-and-Instance-Aware Human Matting and Editing Framework with Multiplane Representation, which introduces a 3D-and-instance-aware matting framework for human instance matting and editing.
• Step1X-Edit: A Practical Framework for General Image Editing, which releases a state-of-the-art image editing model that provides comparable performance to closed-source models.