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This paper proposes a unified diffusion framework (dubbed UniDiffuser) to fitall distributions relevant to a set of multi-modal data in one model. Our keyinsight is -- learning diffusion models for marginal, conditional, and jointdistributions can be unified as predicting the noise in the perturbed data,where the perturbation levels (i.e. timesteps) can be different for differentmodalities. Inspired by the unified view, UniDiffuser learns all distributionssimultaneously with a minimal modification to the original diffusion model --perturbs data in all modalities instead of a single modality, inputs individualtimesteps in different modalities, and predicts the noise of all modalitiesinstead of a single modality. UniDiffuser is parameterized by a transformer fordiffusion models to handle input types of different modalities. Implemented onlarge-scale paired image-text data, UniDiffuser is able to perform image, text,text-to-image, image-to-text, and image-text pair generation by setting propertimesteps without additional overhead. In particular, UniDiffuser is able toproduce perceptually realistic samples in all tasks and its quantitativeresults (e.g., the FID and CLIP score) are not only superior to existinggeneral-purpose models but also comparable to the bespoken models (e.g., StableDiffusion and DALL-E 2) in representative tasks (e.g., text-to-imagegeneration).

One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale