Dynamic Attention Guided Diffusion

Dynamic attention-guided diffusion models enhance the capabilities of diffusion models by selectively focusing computational resources on relevant image regions during the generation process. Current research emphasizes improving efficiency through training-free methods like attention map pruning and developing novel architectures that incorporate attention mechanisms to achieve better control over image generation, including spatial control and concept disentanglement. This work is significant for improving the speed, quality, and controllability of image generation tasks, with applications ranging from image super-resolution and inpainting to medical image synthesis and 3D shape modeling.