Decoupled Diffusion Models: Simultaneous Image to Zero and Zero to Noise
arxiv(2023)
摘要
We propose decoupled diffusion models (DDMs) for high-quality (un)conditioned
image generation in less than 10 function evaluations. In a nutshell, DDMs
decouple the forward image-to-noise mapping into image-to-zero mapping
and zero-to-noise mapping. Under this framework, we mathematically
derive 1) the training objectives and 2) for the reverse time the sampling
formula based on an analytic transition probability which models image to zero
transition. The former enables DDMs to learn noise and image components
simultaneously which simplifies learning. Importantly, because of the latter's
analyticity in the zero-to-image sampling function, DDMs can avoid the
ordinary differential equation-based accelerators and instead naturally perform
sampling with an arbitrary step size. Under the few function evaluation setups,
DDMs experimentally yield very competitive performance compared with the state
of the art in 1) unconditioned image generation, e.g., CIFAR-10 and
CelebA-HQ-256 and 2) image-conditioned downstream tasks such as
super-resolution, saliency detection, edge detection, and image inpainting.
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