Cross-view Masked Diffusion Transformers for Person Image Synthesis
Trung X. Pham Zhang Kang Chang D. Yoo
We present X-MDPT (Cross-view Masked Diffusion Prediction Transformers), 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.