title: MegaFusion: Extend Diffusion Models towards Higher-resolution Image
    Generation without Further Tuning
summary: 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.
paper_authors._id: 66c54a9336a75deef8a188e9
paper_authors.name: Haoning Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-22T19:51:23.864Z
paper_authors._id: 632c7a0d1d303f5f9acf01b8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/632c7a0d1d303f5f9acf01b8/T010IFuCp6UaOeIyWhbCk.jpeg
paper_authors.fullname: Haoning Wu
paper_authors.type: user
paper_authors.user: haoningwu
paper_authors._id: 66c54a9336a75deef8a188ea
paper_authors.name: Shaocheng Shen
paper_authors._id: 66c54a9336a75deef8a188eb
paper_authors.name: Qiang Hu
paper_authors._id: 66c54a9336a75deef8a188ec
paper_authors.name: Xiaoyun Zhang
paper_authors._id: 66c54a9336a75deef8a188ed
paper_authors.name: Ya Zhang
paper_authors._id: 66c54a9336a75deef8a188ee
paper_authors.name: Yanfeng Wang
paper_title: MegaFusion: Extend Diffusion Models towards Higher-resolution Image
    Generation without Further Tuning
paper_summary: 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.
paper_ai_summary: MegaFusion enhances diffusion-based text-to-image generation for high-resolution images using a truncate and relay strategy with dilated convolutions and noise re-scheduling.
paper_ai_keywords: diffusion models text-to-image generation high-resolution image generation truncate and relay strategy dilated convolutions noise re-scheduling latent-space pixel-space computational cost