Omegance: A Single Parameter for Various Granularities in
  Diffusion-Based Synthesis
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.
67480fd786d0329c44dcdfa0
Xinyu Hou
admin_assigned
2024-11-28T11:36:46.015Z
64a7780d87cbd4dc73fd1889
/avatars/d680cdc58d69ef0f5091cbc122f3383b.svg
HOU XINYU
user
itsmag11
67480fd786d0329c44dcdfa1
Zongsheng Yue
admin_assigned
2024-11-28T11:37:04.861Z
630ad0dd2ff113e0fb31c6b0
https://cdn-avatars.huggingface.co/v1/production/uploads/1671174653229-630ad0dd2ff113e0fb31c6b0.jpeg
Zongsheng Yue
user
OAOA
67480fd786d0329c44dcdfa2
Xiaoming Li
67480fd786d0329c44dcdfa3
Chen Change Loy
admin_assigned
2024-11-28T11:36:53.340Z
67459d997a49660f7f62452f
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/X25yTdxC_hcLVxpx4voZo.png
Chen Change Loy
user
cavanloy
Omegance: A Single Parameter for Various Granularities in
  Diffusion-Based Synthesis
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.
A single parameter in diffusion-based synthesis provides precise control over detail granularity without requiring model retraining or increased computation.
diffusion-based synthesis denoising steps diffusion model's reverse process granularity control spatial masks denoising schedules Omegance image synthesis video synthesis advanced diffusion models