Subject-Driven Image Generation

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.