Diffusion Model Alignment

Diffusion model alignment studies how to make generated images, videos, audio, 3D assets, and other outputs better match prompts, human preferences, safety requirements, or task-specific constraints. Common approaches include refining text embeddings and cross-attention, aligning internal representations with vision or domain-specific features, and post-training with preference data, reward models, or reinforcement learning. Researchers also use inference-time guidance and noise or trajectory search to steer frozen models, alongside distillation methods that transfer alignment into faster generators. A central challenge is improving alignment without reward hacking, loss of diversity or fidelity, or excessive training and sampling cost; reliable evaluation across different prompts and user preferences remains difficult.