MegaFusion: Extend Diffusion Models towards Higher-resolution Image Generation without Further Tuning
<|reference_start|>MegaFusion: Extend Diffusion Models towards Higher-resolution Image Generation without Further Tuning: Diffusion models have emerged as frontrunners in text-to-image generation, however, their fixed image resolution during training often leads to challenges in high-resolution image generation, such as semantic deviations 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.<|reference_end|>
@article{wu2024megafusion:,
    title={MegaFusion: Extend Diffusion Models towards Higher-resolution Image
  Generation without Further Tuning},
    author={Haoning Wu, Shaocheng Shen, Qiang Hu, Xiaoyun Zhang, Ya Zhang, Yanfeng
  Wang},
    journal={arXiv preprint arXiv:2408.11001},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.11001},
    primaryClass={cs.CV}
}