PixNerd: Pixel Neural Field Diffusion

Shuai Wang; Ziteng Gao; Chenhui Zhu; Weilin Huang; Limin Wang

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Paper · arXiv 2507.23268

PixNerd: Pixel Neural Field Diffusion

Shuai Wang, Ziteng Gao, Chenhui Zhu, Weilin Huang, Limin Wang

52 upvotesJuly 31, 2025arXiv 预印本

AI 摘要

Pixel Neural Field Diffusion (PixNerd) achieves high-quality image generation in a single-scale, single-stage process without VAEs or complex pipelines, and extends to text-to-image applications with competitive performance.

diffusion transformerscompressed latent spacepre-trained variational autoencoderpixel spacepatch-wise decodingneural fieldsingle-scalesingle-stageend-to-end solutionpixel neural field diffusionPixNerdFIDImageNettext-to-imageGenEval benchmarkDPG benchmark

Abstract

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.