Ultra-Resolution Adaptation with Ease

2025-03-20

AI Summary

To address the challenges of limited training data and computational resources in high-resolution (2K/4K) image generation, this paper proposes URAE—a framework enabling dual efficiency in data and parameter adaptation. Methodologically, it introduces the first lightweight fine-tuning paradigm tailored for super-resolution, revealing that synthetic data significantly accelerates convergence. Theoretical analysis and empirical evaluation demonstrate that freezing the backbone while applying localized weight tuning outperforms low-rank adaptation. Crucially, the work identifies that disabling classifier-free guidance (CFG)—i.e., setting CFG scale to 1—during teacher-guided distillation improves both stability and output quality. The method integrates synthetic data augmentation, structured weight matrix fine-tuning, and CFG-scale normalization. With only 3K samples and 2K optimization steps, URAE achieves 2K generation performance on par with FLUX1.1[Pro] Ultra, and establishes, for the first time, a new high-quality benchmark for 4K image generation.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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 emph{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, extit{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 href{https://github.com/Huage001/URAE}{here}.

Problem

• High-resolution image generation with limited data and resources.
• Efficient adaptation using synthetic data and minor weight tuning.
• Improving 2K and 4K resolution generation performance with URAE.

Innovation

• Synthetic data boosts training convergence.
• Tuning minor weight components enhances efficiency.
• Disabling classifier-free guidance improves performance.