Image Editing
Overview
Image editing studies how to change an image according to text, references, masks, sketches, clicks, or structured controls while retaining its identity and context. Common approaches adapt diffusion and flow-matching models through inversion, attention or latent guidance, spatial masks, and reward-based training; newer systems also use autoregressive models and agents that plan edits, call tools, and check results. Benchmarks test instruction following, local preservation, identity, geometry, text rendering, and multi-turn consistency across settings such as portraits, products, medical images, and scenes. A central challenge is balancing strong, precise changes with fidelity to everything outside the requested edit, especially for complex instructions and long editing sessions.
Latest papers
312 papers53 papers in the last four weeks, up 89% on the four weeks before. 0.3% of all new papers.