T2I Diffusion Models

Text-to-image diffusion models turn prompts into images by iteratively denoising a latent or pixel representation, using latent-diffusion U-Nets, diffusion transformers, and rectified-flow models. Researchers improve prompt and spatial control with classifier-free guidance, cross-attention interventions, LoRA adapters, and reference or layout conditioning, while preference learning and reinforcement learning tune outputs to human or task rewards. Another major effort distills models into few-step generators and uses caching, quantization, and latent compression to reduce inference costs; benchmarks test composition, text rendering, image quality, and human preference. A central challenge is balancing prompt fidelity and diversity with speed, quality, and safeguards: realism or alignment gains do not guarantee robust composition, fair representation, or reliable concept removal.