Cross-view Masked Diffusion Transformers for Person Image Synthesis

We present X-MDPT (\underline{Cross}-view \underline{M}asked \underline{D}iffusion \underline{P}rediction \underline{T}ransformers), a novel diffusion model designed for pose-guided human image generation. X-MDPT distinguishes itself by employing masked diffusion transformers that operate on latent patches, a departure from the commonly-used Unet structures in existing works. The model comprises three key modules: 1) a denoising diffusion Transformer, 2) an aggregation network that consolidates conditions into a single vector for the diffusion process, and 3) a mask cross-prediction module that enhances representation learning with semantic information from the reference image. X-MDPT demonstrates scalability, improving FID, SSIM, and LPIPS with larger models. Despite its simple design, our model outperforms state-of-the-art approaches on the DeepFashion dataset while exhibiting efficiency in terms of training parameters, training time, and inference speed. Our compact 33MB model achieves an FID of 7.42, surpassing a prior Unet latent diffusion approach (FID 8.07) using only 11\times fewer parameters. Our best model surpasses the pixel-based diffusion with \frac{2}{3} of the parameters and achieves 5.43 \times faster inference. The code is available at https://github.com/trungpx/xmdpt.