The field of image generation is moving towards more efficient and controllable methods. Recent developments have focused on improving the quality and flexibility of diffusion models, with a emphasis on reducing computational costs and enhancing spatial controllability. Researchers are exploring new approaches to attention mechanisms, knowledge distillation, and hierarchical processing to achieve these goals. Notable papers in this area include:

• Another BRIXEL in the Wall: Towards Cheaper Dense Features, which proposes a simple knowledge distillation approach to reduce computational costs.
• FreeControl: Efficient, Training-Free Structural Control via One-Step Attention Extraction, which introduces a training-free framework for semantic structural control in diffusion models.
• Toward the Frontiers of Reliable Diffusion Sampling via Adversarial Sinkhorn Attention Guidance, which proposes a novel method for improving the reliability of diffusion sampling.
• From Structure to Detail: Hierarchical Distillation for Efficient Diffusion Model, which presents a hierarchical distillation framework for efficient diffusion models.