Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models
<|reference_start|>Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models: We address the long-standing problem of how to learn effective pixel-based image diffusion models at scale, introducing a remarkably simple greedy growing method for stable training of large-scale, high-resolution models. without the needs for cascaded super-resolution components. The key insight stems from careful pre-training of core components, namely, those responsible for text-to-image alignment {\it vs.} high-resolution rendering. We first demonstrate the benefits of scaling a {\it Shallow UNet}, with no down(up)-sampling enc(dec)oder. Scaling its deep core layers is shown to improve alignment, object structure, and composition. Building on this core model, we propose a greedy algorithm that grows the architecture into high-resolution end-to-end models, while preserving the integrity of the pre-trained representation, stabilizing training, and reducing the need for large high-resolution datasets. This enables a single stage model capable of generating high-resolution images without the need of a super-resolution cascade. Our key results rely on public datasets and show that we are able to train non-cascaded models up to 8B parameters with no further regularization schemes. Vermeer, our full pipeline model trained with internal datasets to produce 1024x1024 images, without cascades, is preferred by 44.0% vs. 21.4% human evaluators over SDXL.<|reference_end|>
@article{vasconcelos2024greedy,
    title={Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models},
    author={Cristina N. Vasconcelos, Abdullah Rashwan, Austin Waters, Trevor
  Walker, Keyang Xu, Jimmy Yan, Rui Qian, Shixin Luo, Zarana Parekh, Andrew
  Bunner, Hongliang Fei, Roopal Garg, Mandy Guo, Ivana Kajic, Yeqing Li, Henna
  Nandwani, Jordi Pont-Tuset, Yasumasa Onoe, Sarah Rosston, Su Wang, Wenlei
  Zhou, Kevin Swersky, David J. Fleet, Jason M. Baldridge, Oliver Wang},
    journal={arXiv preprint arXiv:2405.16759},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16759},
    primaryClass={cs.CV cs.LG}
}