Pre Trained Diffusion Model

Pre-trained diffusion models are generative models used as powerful priors for solving various inverse problems, particularly in image processing and generation. Current research focuses on improving efficiency (e.g., one-step methods, faster sampling algorithms), enhancing control over generation (e.g., through guidance mechanisms and fine-tuning strategies like LoRA), and addressing security concerns (e.g., mitigating membership inference attacks). This work is significant because it leverages the strong generative capabilities of these models to achieve state-of-the-art results in diverse applications, ranging from image restoration and super-resolution to more complex tasks like image composition and 3D reconstruction.