Omegance: A Single Parameter for Various Granularities in Diffusion-Based Synthesis
Xinyu Hou, Zongsheng Yue, Xiaoming Li, Chen Change Loy
2024-11-28
Summary
This paper introduces Omegance, a new technique that uses a single parameter to control the level of detail in images generated by diffusion models, making it easier to create high-quality visuals.
What's the problem?
Generating images with the right amount of detail can be difficult because existing methods often require complex adjustments or retraining of models. This can lead to inefficiencies and limit the control artists have over the final output.
What's the solution?
Omegance simplifies this process by introducing a single parameter called omega, which adjusts how much detail is added during the image generation process without needing to change the model or retrain it. This allows users to easily control the granularity of details in specific regions of an image or at different times during the creation process. The method also utilizes prior knowledge from reference images to enhance its effectiveness.
Why it matters?
This research is important because it provides artists and creators with a straightforward way to fine-tune the details in their generated images. By making it easier to control image quality and detail, Omegance can improve workflows in fields like digital art, animation, and video game design, ultimately leading to better and more personalized visual content.
Abstract
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.