Omegance: A Single Parameter for Various Granularities in Diffusion-Based Synthesis (2411.17769v1)

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.

• The paper presents Omegance, which integrates a single scaling parameter into diffusion models to precisely control output granularity.
• Experiments on image and video synthesis reveal enhanced detail control and effective artifact correction across various tasks.
• Its architecture-agnostic design and negligible computational overhead provide practical benefits for refining generative models in real-world applications.

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

The paper presents "Omegance," a novel methodology for controlling granularity in diffusion-based generative models using a single parameter, denoted as $\omega$. Unlike many existing techniques, Omegance avoids the conventional complexities of model retraining or architectural adjustments. Instead, it introduces a parameter that integrates seamlessly into existing denoising structures to facilitate precision in manipulating output granularity without incurring additional computational costs. This parameter can be applied both globally and locally, either through spatial omega masks or temporal omega schedules, providing substantial flexibility in applications involving both image and video synthesis.

Theoretical Considerations

In diffusion models, the synthesis process consists of a forward and reverse diffusion sequence wherein visual content is formed by iteratively removing noise from an initially corrupted signal. The introduction of $\omega$ as a scaling factor into the noise prediction modifies the effective signal-to-noise ratio (SNR) during the reverse diffusion process. By altering the magnitude of noise suppression, $\omega$ allows for a modulation of visual granularity: lower values of $\omega$ maintain more noise, resulting in richer, more complex textures, while higher values yield smoother, less intricate outputs. This simple integration mechanism demonstrates the theoretical basis for the parameter's efficacy without requiring significant modifications to existing diffusion models.

Experimental Results

The experimental evaluations of Omegance were wide-ranging, assessing its impact on various diffusion-based tasks. The authors explored text-to-image generation using models like Stable Diffusion XL (SDXL) and FLUX, and text-to-video tasks with models like AnimateDiff. Early-stage and late-stage omega schedules were used to demonstrate the temporal control over the generated content, effectively highlighting the nuanced modification of object shapes and textures in the output.

The experiments convincingly show that Omegance can rectify artifacts in lower-quality models and enhance details in visually over-smoothed outputs. User studies further validate Omegance's operational efficacy, achieving a high accuracy rate in granularity ranking and receiving positive feedback on output quality.

Applicability and Future Directions

Omegance holds significant implications for practitioners seeking efficient ways to fine-tune output details in generative models. Its architecture-agnostic nature and negligible computational overhead make it a versatile tool for integration across existing workflows. Moreover, practical applications in image inpainting, real-image editing, and localized adjustments suggest a wide array of potential use cases beyond purely aesthetic improvements.

The flexibility of Omegance, notably its capacity for spatial and temporal granularity control, represents a step forward in user-driven content synthesis. Future investigations might explore its combination with other generative techniques or its application in real-time scenarios, broadening the horizon for both research and commercial ventures within generative artificial intelligence. Integrating such a mechanism with reinforcement learning strategies from human feedback may yield even more refined control, aligning outputs more closely with human preferences and enhancing user satisfaction.

Overall, Omegance stands out for its subtle yet powerful approach, promising adaptable detail manipulation across diverse generative tasks without the need for exhaustive computational resources.