title: Omegance: A Single Parameter for Various Granularities in
    Diffusion-Based Synthesis
summary: In this work, we introduce a single parameter omega, to effectively
  control granularity in diffusion-based synthesis. This parameter is
  incorporated during the denoising steps of the diffusion model's reverse
  process. Our approach does not require model retraining, architectural
  modifications, or additional computational overhead during inference, yet
  enables precise control over the level of details in the generated outputs.
  Moreover, spatial masks or denoising schedules with varying omega values can
  be applied to achieve region-specific or timestep-specific granularity control.
  Prior knowledge of image composition from control signals or reference images
  further facilitates the creation of precise omega masks for granularity
  control on specific objects. To highlight the parameter's role in controlling
  subtle detail variations, the technique is named Omegance, combining "omega"
  and "nuance". Our method demonstrates impressive performance across various
  image and video synthesis tasks and is adaptable to advanced diffusion models.
  The code is available at https://github.com/itsmag11/Omegance.
paper_authors._id: 67480fd786d0329c44dcdfa0
paper_authors.name: Xinyu Hou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-28T11:36:46.015Z
paper_authors._id: 64a7780d87cbd4dc73fd1889
paper_authors.avatarUrl: /avatars/d680cdc58d69ef0f5091cbc122f3383b.svg
paper_authors.fullname: HOU XINYU
paper_authors.type: user
paper_authors.user: itsmag11
paper_authors._id: 67480fd786d0329c44dcdfa1
paper_authors.name: Zongsheng Yue
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-28T11:37:04.861Z
paper_authors._id: 630ad0dd2ff113e0fb31c6b0
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1671174653229-630ad0dd2ff113e0fb31c6b0.jpeg
paper_authors.fullname: Zongsheng Yue
paper_authors.type: user
paper_authors.user: OAOA
paper_authors._id: 67480fd786d0329c44dcdfa2
paper_authors.name: Xiaoming Li
paper_authors._id: 67480fd786d0329c44dcdfa3
paper_authors.name: Chen Change Loy
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-28T11:36:53.340Z
paper_authors._id: 67459d997a49660f7f62452f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/X25yTdxC_hcLVxpx4voZo.png
paper_authors.fullname: Chen Change Loy
paper_authors.type: user
paper_authors.user: cavanloy
paper_title: Omegance: A Single Parameter for Various Granularities in
    Diffusion-Based Synthesis
paper_summary: In this work, we introduce a single parameter omega, to effectively
  control granularity in diffusion-based synthesis. This parameter is
  incorporated during the denoising steps of the diffusion model's reverse
  process. Our approach does not require model retraining, architectural
  modifications, or additional computational overhead during inference, yet
  enables precise control over the level of details in the generated outputs.
  Moreover, spatial masks or denoising schedules with varying omega values can
  be applied to achieve region-specific or timestep-specific granularity control.
  Prior knowledge of image composition from control signals or reference images
  further facilitates the creation of precise omega masks for granularity
  control on specific objects. To highlight the parameter's role in controlling
  subtle detail variations, the technique is named Omegance, combining "omega"
  and "nuance". Our method demonstrates impressive performance across various
  image and video synthesis tasks and is adaptable to advanced diffusion models.
  The code is available at https://github.com/itsmag11/Omegance.
paper_ai_keywords: diffusion-based synthesis denoising steps diffusion model's reverse process spatial masks denoising schedules granularity control region-specific granularity control timestep-specific granularity control spatial masks denoising schedules Omegance