SmartPhotoCrafter: Unified Reasoning, Generation and Optimization for Automatic Photographic Editing

Automatic photographic enhancement requires more than executing predefined editing instructions. A model must first infer whether an image should be edited, determine what needs to be changed, and estimate the appropriate strength of each adjustment. This problem becomes particularly challenging in real photographs, where technical degradations and aesthetic deficiencies often co-occur, while successful edits must preserve the original content and photographic intent. We introduce SmartPhotoCrafter, a unified framework for automatic photographic optimization across restoration, retouching, and their compositions. SmartPhotoCrafter consists of an Image Critic that diagnoses image quality and infers editing decisions, and a Photographic Artist that realizes these decisions through conditional generation. Instead of relying on a text-only cascade, the Artist is conditioned directly on the Critic's contextual and reasoning representations, providing a richer interface between visual assessment and image generation. We further align reasoning and generation through executable editing targets and outcome-aligned reinforcement learning, with task-specific signals that evaluate editing direction, photometric agreement, perceptual quality, and content preservation. Experiments on automatic optimization, mixed restoration–retouching, and conventional restoration tasks demonstrate the effectiveness of the proposed method.