estimates the corresponding noise level using an autoregressive model. This allows the model to
capture the dependencies between adjacent pixels and generate high-quality images. Score-based
generative models (SOG) [46] use the score function to model the diffusion process. [40] generates
high-ﬁdelity images conditioned on CLIP representations of text prompts. Latent diffusion model
(LDM)
[41] uses a VAE to encode inputs into latent space to reduce modeling dimension and
improves efﬁciency. The motivation is that image compression can be separated into semantic space
by a diffusion model and perceptual space by an autoencoder. By incorporating temporal modeling
modules and cascading model architectures, video diffusion models have been built upon image
diffusers to generate temporally consistent and inherent frames[14, 19, 21, 44]. Diffusion models have
also been applied to other domains, such as generating audio from text and vision prompts[23, 33].
Any-to-Any Generation via Composable Diffusion