Conditional Image Generation

Conditional image generation studies how to make images follow user-provided signals, from text and reference pictures to spatial layouts, poses, segmentation masks, and scientific measurements. Common approaches include conditional GANs, diffusion and flow-matching models, autoregressive transformers, and adapters or inference-time guidance that add controls to pretrained generators. Researchers test these methods on class- and text-conditioned benchmarks as well as tasks such as editing, virtual try-on, medical imaging, and synthetic-data creation, often checking both image quality and downstream usefulness. A central challenge is balancing precise control and fidelity to the input with realism, diversity, and consistency when multiple conditions conflict or are incomplete.