Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models

Cristina Nader Vasconcelos, Abdullah Rashwan, Austin Waters, Trevor Walker, Keyang Xu, Jimmy Yan, Rui Qian, Yeqing Li, SHIXIN LUO, Yasumasa Onoe, Zarana Parekh, Ivana Kajic, Mandy Guo, Wenlei Zhou, Sarah Rosston, Roopal Garg, Hongliang Fei, Jordi Pont-Tuset, Su Wang, Henna Nandwani et al. (5 additional authors not shown)

Abstract: We address the long-standing problem of how to learn effective pixel-based image diffusion models at scale, introducing a remarkably simple greedy 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 vs. high resolution rendering. We first demonstrate the benefits of scaling a 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 1024×1024 images, without cascades, is preferred by 44.0% vs. 21.4% human evaluators over SDXL.

Submission Length: Long submission (more than 12 pages of main content)

Previous TMLR Submission Url: https://openreview.net/forum?id=q5vK4MGPIK

Changes Since Last Submission: Removed teaser image from front page, as requested, so that the abstract now is immediately below the title.

Assigned Action Editor: ~Ran_He1

Submission Number: 2683