Image Generation

Image generation studies how to produce realistic, useful images from inputs such as text prompts, reference pictures, sketches, spatial layouts, or scientific measurements. Common approaches include diffusion and flow-matching models that refine images step by step, autoregressive models that generate visual tokens in sequence, and GANs used in some specialized settings. Researchers improve control and efficiency with techniques such as reference conditioning, geometric constraints, preference optimization, and iterative agent feedback, and evaluate results on benchmarks such as ImageNet, COCO, and GenEval as well as domain-specific tasks. A central challenge is balancing visual quality, prompt accuracy, identity and structural consistency, diversity, and generation cost; realistic appearance alone does not guarantee that an image is correct or safe.