Natural images are continuous, yet most generative models synthesize them on discrete grids, limiting resolution-flexible generation. Continuous neural fields enable resolution-free rendering, but prior methods introduce continuity only at the decoding stage as an interpolation module, leaving the generative latent space discretized and reconstruction-oriented. We propose RaPD (Resolution-agnostic Pixel Diffusion), which performs diffusion in a continuous Neural Image Field (NIF) latent space. RaPD bridges this reconstruction-generation gap with Semantic Representation Guidance for generation-aware latent learning and a Coordinate-Queried Attention Renderer for coordinate-conditioned, scale-aware rendering. A single denoised latent can be rendered at arbitrary resolutions by changing only the query coordinates, keeping diffusion cost fixed. Experiments demonstrate superior generation quality and resolution scalability.
@article{arxiv.2605.15908,
title = {RaPD: Resolution-Agnostic Pixel Diffusion via Semantics-Enriched Implicit Representations},
author = {Yanhao Ge and Shanyan Guan and Weihao Wang and Ying Tai and Mingyu You},
journal= {arXiv preprint arXiv:2605.15908},
year = {2026}
}