DreamMix: Decoupling Object Attributes for Enhanced Editability in Customized Image Inpainting (2411.17223v1)

Abstract: Subject-driven image inpainting has emerged as a popular task in image editing alongside recent advancements in diffusion models. Previous methods primarily focus on identity preservation but struggle to maintain the editability of inserted objects. In response, this paper introduces DreamMix, a diffusion-based generative model adept at inserting target objects into given scenes at user-specified locations while concurrently enabling arbitrary text-driven modifications to their attributes. In particular, we leverage advanced foundational inpainting models and introduce a disentangled local-global inpainting framework to balance precise local object insertion with effective global visual coherence. Additionally, we propose an Attribute Decoupling Mechanism (ADM) and a Textual Attribute Substitution (TAS) module to improve the diversity and discriminative capability of the text-based attribute guidance, respectively. Extensive experiments demonstrate that DreamMix effectively balances identity preservation and attribute editability across various application scenarios, including object insertion, attribute editing, and small object inpainting. Our code is publicly available at https://github.com/mycfhs/DreamMix.

• The paper introduces DreamMix, a diffusion model that decouples local and global features to enable precise, customizable image inpainting.
• It employs an Attribute Decoupling Mechanism and a Textual Attribute Substitution module to enhance control over object attributes.
• Experimental results show superior identity preservation and flexible attribute modifications compared to prior methods.

DreamMix: Decoupling Object Attributes for Enhanced Editability in Customized Image Inpainting

This paper introduces DreamMix, a generative model designed to enhance the flexibility and precision of subject-driven image inpainting processes. The model addresses the limitations of existing methods, which typically either focus heavily on preserving the identity of inserted objects, consequently losing editability, or fail to incorporate a specific object into a given scene effectively. DreamMix offers a novel solution by employing a diffusion-based generative model capable of object customization and inpainting while allowing text-based modifications to the object's attributes.

Model Architecture and Innovations

The DreamMix model builds on the strengths of diffusion models for image generation, introducing a unique inpainting framework that disentangles local and global features to improve both object insertion and scene coherence. Key components of this approach include:

1. Disentangled Local-Global Inpainting Framework: The model separates inpainting tasks into local content generation and global context harmonization stages. This allows DreamMix to integrate target objects with high precision locally while retaining the overall visual harmony of the entire scene.
2. Attribute Decoupling Mechanism (ADM): DreamMix improves the control over object attributes by decoupling them during the training phase. This mechanism allows for variability in textual attribute descriptions, thus enhancing the diversity and specificity of modifications based on user inputs.
3. Textual Attribute Substitution (TAS) Module: This component is designed to enhance text-driven attribute editability further. By utilizing an orthogonal decomposition strategy, it separates interfering information from textual guidance, thus amplifying the model's ability to adapt and modify attributes according to new inputs.

Experimental Results

The authors conducted extensive experiments to validate DreamMix's capability in balancing identity preservation with attribute editability across diverse scenarios. Quantitative metrics such as CLIP similarity scores and FID indicated superior performance over preceding techniques in both maintaining object identity and achieving desired attribute modifications. Qualitative assessments further confirmed the model's capability in handling various customization tasks, such as identity preservation, attribute editing, and small-scale object inpainting.

Implications for Future Research

This research expands the boundary of what is feasible in the field of image inpainting and image generation. DreamMix potentially opens doors to more sophisticated applications such as detailed virtual environment creation, personalized content design, and beyond. The introduction of mechanisms to ensure both precision in object placement and flexibility in object customization reflects an advancement in generating contextually aware and editable imagery.

Potential Future Directions

DreamMix lays a foundation upon which future research can build. Possible avenues include expanding multi-object dynamics within scenes, incorporating additional contextual information such as depth and pose for more nuanced inpainting results, or exploring real-time applications in augmented reality and interactive design tools. Further refinement of attribute decoupling and substitution techniques could also open pathways to even more nuanced control in generative tasks, potentially leading to more intricate and application-specific models.

In summary, DreamMix represents a significant step forward in enabling high-fidelity, user-guided image customization. It effectively addresses the twin challenges of maintaining the integrity of injected features while simultaneously offering substantial flexibility in object editability.