1.WHAT IS SINFUSION?
SinFusion is a proposed framework for training diffusion models on a single input image or video. It leverages the high-quality imaging capabilities of diffusion models and has some tricks to reduce fine-tuning costs. Optimizing SinFusion can be used to create new images/videos while preserving the dynamics and concepts of the input images/videos.
2.WHICH KIND OF IMAGE THAT SINFUSION IS DISPLAY?
SinFusion exhibits great versatility for generating additional images from a single image, image editing, image generation from sketches, and visual summarization of images. It also covers video upsampling, video extrapolation (both forward and backward in time), and generation of various new videos from a single video.
3.HOW SINFUSION ACHIEVE THIS?
For image generation, SinFusion modifies the existing DDPM structure. SinFusion is trained on a large set of random image slices using the input image. Additionally, the backbone UNet structure is modified to speed up the network.For video generation, SinFusion also uses a set of modified DDPM modules together. image prediction to generate new images, image projection to ensure that the images generated by the predictor are correct, and finally image interpolation to increase the temporal resolution of the generated video.