UniDiffuser The UniDiffuser model was proposed in One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale by Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, Jun Zhu. The abstract from the paper is: This paper proposes a unified diffusion framework (dubbed UniDiffuser) to fit all distributions relevant to a set of multi-modal data in one model. Our key insight is — learning diffusion models for marginal, conditional, and joint distributions can be unified as predicting the noise in the perturbed data, where the perturbation levels (i.e. timesteps) can be different for different modalities. Inspired by the unified view, UniDiffuser learns all distributions simultaneously with a minimal modification to the original diffusion model — perturbs data in all modalities instead of a single modality, inputs individual timesteps in different modalities, and predicts the noise of all modalities instead of a single modality. UniDiffuser is parameterized by a transformer for diffusion models to handle input types of different modalities. Implemented on large-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 proper timesteps without additional overhead. In particular, UniDiffuser is able to produce perceptually realistic samples in all tasks and its quantitative results (e.g., the FID and CLIP score) are not only superior to existing general-purpose models but also comparable to the bespoken models (e.g., Stable Diffusion and DALL-E 2) in representative tasks (e.g., text-to-image generation). You can find the original codebase at thu-ml/unidiffuser and additional checkpoints at thu-ml. There is currently an issue on PyTorch 1.X where the output images are all black or the pixel values become NaNs. This issue can be mitigated by switching to PyTorch 2.X. This pipeline was contributed by dg845. ❤️  Usage Examples Because the UniDiffuser model is trained to model the joint distribution of (image, text) pairs, it is capable of performing a diverse range of generation tasks:  Unconditional Image and Text Generation Unconditional generation (where we start from only latents sampled from a standard Gaussian prior) from a UniDiffuserPipeline will produce a (image, text) pair:   Copied import torch