Omegance: A Single Parameter for Various Granularities in
  Diffusion-Based Synthesis
Authors: name: Xinyu Hou, name: Zongsheng Yue, name: Xiaoming Li, name: Chen Change Loy

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.