title: Ultra-Resolution Adaptation with Ease
summary: Text-to-image diffusion models have achieved remarkable progress in recent
  years. However, training models for high-resolution image generation remains
  challenging, particularly when training data and computational resources are
  limited. In this paper, we explore this practical problem from two key
  perspectives: data and parameter efficiency, and propose a set of key
  guidelines for ultra-resolution adaptation termed URAE. For data
  efficiency, we theoretically and empirically demonstrate that synthetic data
  generated by some teacher models can significantly promote training
  convergence. For parameter efficiency, we find that tuning minor components of
  the weight matrices outperforms widely-used low-rank adapters when synthetic
  data are unavailable, offering substantial performance gains while maintaining
  efficiency. Additionally, for models leveraging guidance distillation, such as
  FLUX, we show that disabling classifier-free guidance, i.e., setting
  the guidance scale to 1 during adaptation, is crucial for satisfactory
  performance. Extensive experiments validate that URAE achieves comparable
  2K-generation performance to state-of-the-art closed-source models like FLUX1.1
  [Pro] Ultra with only 3K samples and 2K iterations, while setting new
  benchmarks for 4K-resolution generation. Codes are available
  https://github.com/Huage001/URAE{here}.
paper_authors._id: 67dce4c10784200359ab2494
paper_authors.name: Ruonan Yu
paper_authors._id: 67dce4c10784200359ab2495
paper_authors.name: Songhua Liu
paper_authors._id: 67dce4c10784200359ab2496
paper_authors.name: Zhenxiong Tan
paper_authors._id: 67dce4c10784200359ab2497
paper_authors.name: Xinchao Wang
paper_title: Ultra-Resolution Adaptation with Ease
paper_summary: Text-to-image diffusion models have achieved remarkable progress in recent
  years. However, training models for high-resolution image generation remains
  challenging, particularly when training data and computational resources are
  limited. In this paper, we explore this practical problem from two key
  perspectives: data and parameter efficiency, and propose a set of key
  guidelines for ultra-resolution adaptation termed URAE. For data
  efficiency, we theoretically and empirically demonstrate that synthetic data
  generated by some teacher models can significantly promote training
  convergence. For parameter efficiency, we find that tuning minor components of
  the weight matrices outperforms widely-used low-rank adapters when synthetic
  data are unavailable, offering substantial performance gains while maintaining
  efficiency. Additionally, for models leveraging guidance distillation, such as
  FLUX, we show that disabling classifier-free guidance, i.e., setting
  the guidance scale to 1 during adaptation, is crucial for satisfactory
  performance. Extensive experiments validate that URAE achieves comparable
  2K-generation performance to state-of-the-art closed-source models like FLUX1.1
  [Pro] Ultra with only 3K samples and 2K iterations, while setting new
  benchmarks for 4K-resolution generation. Codes are available
  https://github.com/Huage001/URAE{here}.
paper_ai_summary: URAE guidelines improve data and parameter efficiency in ultra-resolution text-to-image diffusion models, achieving high-resolution generation performance with limited data and iterations.
paper_ai_keywords: synthetic data weight matrices low-rank adapters classifier-free guidance text-to-image diffusion models ultra-resolution adaptation