Omegance：扩散基合成中不同粒度的单一参数

Omegance: A Single Parameter for Various Granularities in Diffusion-Based Synthesis

November 26, 2024

作者: Xinyu Hou, Zongsheng Yue, Xiaoming Li, Chen Change Loy

cs.AI

摘要

在这项工作中，我们引入了一个名为omega的单一参数，用于有效地控制基于扩散的合成中的粒度。该参数被整合到扩散模型反向过程的去噪步骤中。我们的方法在推理过程中不需要模型重新训练、架构修改或额外的计算开销，但能够精确控制生成输出中的细节级别。此外，可以应用具有不同omega值的空间掩模或去噪时间表，实现区域特定或时间步特定的粒度控制。通过从控制信号或参考图像中获取的图像组成的先验知识，进一步促进了为特定对象上的粒度控制创建精确omega掩模。为突出该参数在控制微妙细节变化中的作用，该技术被命名为Omegance，结合了"omega"和"nuance"。我们的方法在各种图像和视频合成任务中展现出令人印象深刻的性能，并可适应先进的扩散模型。代码可在https://github.com/itsmag11/Omegance 上找到。

English

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.