PixNerd: Pixel Neural Field Diffusion
Authors: name: Shuai Wang, name: Ziteng Gao, name: Chenhui Zhu, name: Weilin Huang, name: Limin Wang

The current success of diffusion transformers heavily depends on the
compressed latent space shaped by the pre-trained variational autoencoder(VAE).
However, this two-stage training paradigm inevitably introduces accumulated
errors and decoding artifacts. To address the aforementioned problems,
researchers return to pixel space at the cost of complicated cascade pipelines
and increased token complexity. In contrast to their efforts, we propose to
model the patch-wise decoding with neural field and present a single-scale,
single-stage, efficient, end-to-end solution, coined as pixel neural field
diffusion~(PixelNerd). Thanks to the efficient neural field representation in
PixNerd, we directly achieved 2.15 FID on ImageNet 256times256 and 2.84 FID
on ImageNet 512times512 without any complex cascade pipeline or VAE. We also
extend our PixNerd framework to text-to-image applications. Our PixNerd-XXL/16
achieved a competitive 0.73 overall score on the GenEval benchmark and 80.9
overall score on the DPG benchmark.