Diffusion models have emerged as frontrunners in text-to-image generation for their impressive capabilities. Nonetheless, their fixed image resolution during training often leads to challenges in high-resolution image generation, such as semantic inaccuracies and object replication. This paper introduces MegaFusion, a novel approach that extends existing diffusion-based text-to-image generation models towards efficient higher-resolution generation without additional fine-tuning or extra adaptation. Specifically, we employ an innovative truncate and relay strategy to bridge the denoising processes across different resolutions, allowing for high-resolution image generation in a coarse-to-fine manner. Moreover, by integrating dilated convolutions and noise re-scheduling, we further adapt the model's priors for higher resolution. The versatility and efficacy of MegaFusion make it universally applicable to both latent-space and pixel-space diffusion models, along with other derivative models. Extensive experiments confirm that MegaFusion significantly boosts the capability of existing models to produce images of megapixels and various aspect ratios, while only requiring about 40% of the original computational cost.

本研究解决了现有扩散模型在高分辨率图像生成中的语义不准确和对象复制问题。通过提出一种名为MegaFusion的新方法，利用创新的截断和传递策略，使得模型可以在不进行额外微调的情况下实现高分辨率生成。实验结果显示，MegaFusion显著提高了现有模型在多种长宽比下生成百万像素图像的能力，同时只需约40%的原始计算成本。

MegaFusion：扩展扩散模型以无进一步调优实现更高分辨率图像生成