Diffusion models are the current state-of-the-art in image generation, synthesizing high-quality images by breaking down the generation process into many fine-grained denoising steps. Despite their good performance, diffusion models are computationally expensive, requiring many neural function evaluations (NFEs). In this work, we propose an anytime diffusion-based method that can generate viable images when stopped at arbitrary times before completion. Using existing pretrained diffusion models, we show that the generation scheme can be recomposed as two nested diffusion processes, enabling fast iterative refinement of a generated image. We use this Nested Diffusion approach to peek into the generation process and enable flexible scheduling based on the instantaneous preference of the user. In experiments on ImageNet and Stable Diffusion-based text-to-image generation, we show, both qualitatively and quantitatively, that our method's intermediate generation quality greatly exceeds that of the original diffusion model, while the final slow generation result remains comparable.

本研究提出了一种基于扩散模型的任意时间图像生成方法，使用两个嵌套扩散过程进行快速迭代细化，并允许根据用户的瞬时偏好进行灵活调度。在 ImageNet 数据集和稳定扩散的文本到图像生成实验中，我们显示了我们的中间生成质量大大超过原始扩散模型，同时最终缓慢的生成结果仍然可比较。

嵌套扩散过程用于实时图像生成