  Diffusion models are a new class of generative models, and have dramatically
promoted image generation with unprecedented quality and diversity. Existing
diffusion models mainly try to reconstruct input image from a corrupted one
with a pixel-wise or feature-wise constraint along spatial axes. However, such
point-based reconstruction may fail to make each predicted pixel/feature fully
preserve its neighborhood context, impairing diffusion-based image synthesis.
As a powerful source of automatic supervisory signal, context has been well
studied for learning representations. Inspired by this, we for the first time
propose ConPreDiff to improve diffusion-based image synthesis with context
prediction. We explicitly reinforce each point to predict its neighborhood
context (i.e., multi-stride features/tokens/pixels) with a context decoder at
the end of diffusion denoising blocks in training stage, and remove the decoder
for inference. In this way, each point can better reconstruct itself by
preserving its semantic connections with neighborhood context. This new
paradigm of ConPreDiff can generalize to arbitrary discrete and continuous
diffusion backbones without introducing extra parameters in sampling procedure.
Extensive experiments are conducted on unconditional image generation,
text-to-image generation and image inpainting tasks. Our ConPreDiff
consistently outperforms previous methods and achieves a new SOTA text-to-image
generation results on MS-COCO, with a zero-shot FID score of 6.21.
