OmniPrism: Learning Disentangled Visual Concept for Image Generation
Summary: arXiv:2412.12242v2 Announce Type: replace-cross
Abstract: Creative visual concept generation often draws inspiration from specific concepts in a reference image to produce relevant outcomes. However, existing methods are typically constrained to single-aspect concept generation or are easily disrupted by irrelevant concepts in multi-aspect concept scenarios, leading to concept confusion and hindering creative generation. To address this, we propose OmniPrism, a visual concept disentangling approach for creative image generation.
Introduction
The demand for advanced image generation techniques has surged in recent years, driven by the growing interest in artificial intelligence and machine learning. To create visually appealing and contextually relevant images, it is crucial to disentangle various concepts from reference images. Traditional methods often struggle with this task, especially in complex scenarios where multiple aspects coexist.
Methodology
OmniPrism introduces a novel approach to visual concept disentanglement, guided by natural language inputs. The main components of our methodology include:
- Concept Disentanglement: We utilize a multimodal extractor to achieve clear separations between different visual concepts present in an image.
- Paired Concept Disentangled Dataset (PCD-200K): This dataset consists of 200,000 pairs of images, where each pair shares the same concept, such as content, style, and composition. This resource is instrumental in training our model.
- Contrastive Orthogonal Disentangled (COD) Training Pipeline: Through this innovative training process, we learn to create distinct concept representations, which are critical for effective image generation.
- Diffusion Model Integration: Our method incorporates these disentangled representations into additional diffusion cross-attention layers, enhancing the generation process.
- Block Embeddings: We design specific block embeddings that adapt each block’s concept domain, ensuring that the generated images align with the desired concepts.
Results
Extensive experiments have demonstrated the effectiveness of OmniPrism in generating high-quality images while maintaining a clear distinction between various concepts. Our results indicate that:
- The generated images exhibit high fidelity to the original text prompts provided.
- Concept disentanglement significantly reduces the confusion typically observed in multi-aspect scenarios.
- Overall, the quality of generated images is superior compared to existing methods, showcasing enhanced creativity and relevance.
Conclusion
OmniPrism represents a significant advancement in the field of creative image generation. By effectively disentangling visual concepts and integrating them into a robust image generation framework, we enable artists and designers to explore new creative possibilities. Our approach not only addresses the limitations of existing methods but also opens the door for future research in the realm of AI-driven visual creativity.
Future Work
Looking ahead, further improvements can be made to enhance the efficiency and scalability of our method. We aim to explore additional datasets, refine our training techniques, and investigate the potential integration of OmniPrism with various creative applications.