Subject-Driven Image Generation
Overview
These methods condition diffusion models and related generators on reference images so a person, object, or character remains recognizable in new poses, scenes, and styles. Common approaches include fine-tuning with LoRA or textual inversion, reference-image adapters, and training-free attention or feature routing; multi-subject systems also use masks, layouts, and separate identity pathways. Benchmarks increasingly test attribute binding, pose control, and identity retention as scenes grow more complex. A central tension is balancing subject fidelity with prompt alignment and visual diversity, while evaluation remains difficult because global similarity scores can miss identity swaps or collapse.
Latest papers
61 papers7 papers in the last four weeks, up 133% on the four weeks before. 0.0% of all new papers.