TweedieMix: Improving Multi-Concept Fusion for Diffusion-based Image/Video Generation (2410.05591v2)

Abstract: Despite significant advancements in customizing text-to-image and video generation models, generating images and videos that effectively integrate multiple personalized concepts remains a challenging task. To address this, we present TweedieMix, a novel method for composing customized diffusion models during the inference phase. By analyzing the properties of reverse diffusion sampling, our approach divides the sampling process into two stages. During the initial steps, we apply a multiple object-aware sampling technique to ensure the inclusion of the desired target objects. In the later steps, we blend the appearances of the custom concepts in the de-noised image space using Tweedie's formula. Our results demonstrate that TweedieMix can generate multiple personalized concepts with higher fidelity than existing methods. Moreover, our framework can be effortlessly extended to image-to-video diffusion models, enabling the generation of videos that feature multiple personalized concepts. Results and source code are in our anonymous project page.

• The paper introduces a two-stage diffusion process that integrates multiple distinct concepts using multi-object-aware sampling and CFG++.
• The method employs a novel resampling strategy and regional integration via Tweedie’s formula to achieve superior qualitative and quantitative results.
• The approach extends to video generation by ensuring frame consistency through feature injection, enabling high-fidelity multi-concept animations.

TweedieMix: Enhancing Multi-Concept Image and Video Generation with Diffusion Models

The paper introduces TweedieMix, a novel approach to effectively integrate multiple personalized concepts into image and video generation via diffusion models. In recent years, diffusion models have substantially advanced text-to-image (T2I) and video generation, unlocking new creative potentials. However, the simultaneous generation of multiple distinct concepts remains a technical challenge. TweedieMix addresses this gap by revolutionizing diffusion-based generation methods through a two-stage inference process.

The central innovation of the proposed method lies in the meticulous division of the reverse diffusion process into distinct stages. Initially, a multi-object-aware sampling strategy ensures the incorporation of all specified concepts. Utilizing Classifier-free guidance (CFG++) alongside a resampling strategy during this phase, TweedieMix enhances image quality and maintains the distinctive attributes of each concept.

Key Methods and Innovations:

1. Multi-Object-Aware Sampling: By employing unconditioned score sampling and multi-object prompts, the initial steps ensure broad concept representation. This addresses typical deficiencies in existing methods, where semantically similar concepts blend undesirably.
2. Resampling Strategy: The introduction of replacing single-concept samples with combined multi-concept samples fortifies the text-conditioned diffusion process, highlighted with improved qualitative and quantitative scores in text and image fidelity.
3. Tweedie's Formula and Regional Integration: Unlike traditional methods that integrate concepts within cross-attention layers, TweedieMix utilizes a denoised image space for regional integration of concepts, leveraging Tweedie’s formula. This methodically ensures that custom concepts are blended accurately without mutual interference.
4. Extension to Videos: A significant contribution is the extension of these techniques to video generation. The method incorporates feature injection techniques that maintain consistency between frames, allowing the seamless translation of multi-concept images into animations without loss of detail or contextual appearance.

Results and Implications:

Experimentally, the authors demonstrate marked improvements over existing methods, both in image quality and the preservation of distinct concept attributes. The model excels in generating high CLIP scores and favorable user paper results, substantiating its capacity to handle advanced prompt complexities.

This research offers practical improvements applicable in content generation industries, particularly where detailed customization and high fidelity are required. Moreover, the theoretical advancements, such as precise regional guidance in the denoised space and improved resampling techniques, provide a foundation for further exploration in diffusion model-based generative tasks.

Conclusion and Future Directions:

By eliminating the need for additional optimization and offering a flexible, efficient framework for multi-concept generation, TweedieMix represents a significant step forward in the T2I and video domains. Future research could explore optimizing computational efficiency further and extending the application to additional modalities such as 3D scene generation. The potential for integrating more complex, context-aware AI systems with TweedieMix methodologies could redefine how personalized content is generated and consumed.