Background-Preserving Image Editing via Exact Inversion

Diffusion probabilistic models (DPMs) are a popular approach in generative modeling, which involve gradual denoising to create high-quality images. However, this process usually takes a long time, leading to slower generation or sampling times. To address this issue, researchers have proposed various methods to accelerate the sampling process while maintaining image quality.
One common approach is to use dual diffusion implicit models (DDIM), which combine two diffusion processes to generate images more efficiently. This method can be further improved by using backward Euler iteration or high-order term approximation. These techniques can significantly reduce the sampling time without compromising image quality, making them ideal for applications where speed is crucial.
To understand how these methods work, imagine a noisy image as a puzzle with missing pieces. The diffusion process gradually fills in the missing pieces, but it can take a long time to complete. By using DDIM and other techniques, we can speed up this process without sacrificing the quality of the puzzle.
In summary, "Fast Diffusion Models for Image Generation" presents various methods to accelerate the sampling process in diffusion probabilistic models while maintaining image quality. These methods are ideal for applications where speed is essential, making it possible to generate high-quality images more efficiently than ever before.