Infinite-ID: Identity-preserved Personalization via ID-semantics Decoupling Paradigm (2403.11781v1)

Abstract: Drawing on recent advancements in diffusion models for text-to-image generation, identity-preserved personalization has made significant progress in accurately capturing specific identities with just a single reference image. However, existing methods primarily integrate reference images within the text embedding space, leading to a complex entanglement of image and text information, which poses challenges for preserving both identity fidelity and semantic consistency. To tackle this challenge, we propose Infinite-ID, an ID-semantics decoupling paradigm for identity-preserved personalization. Specifically, we introduce identity-enhanced training, incorporating an additional image cross-attention module to capture sufficient ID information while deactivating the original text cross-attention module of the diffusion model. This ensures that the image stream faithfully represents the identity provided by the reference image while mitigating interference from textual input. Additionally, we introduce a feature interaction mechanism that combines a mixed attention module with an AdaIN-mean operation to seamlessly merge the two streams. This mechanism not only enhances the fidelity of identity and semantic consistency but also enables convenient control over the styles of the generated images. Extensive experimental results on both raw photo generation and style image generation demonstrate the superior performance of our proposed method.

Infinite-ID: Advancing Personalized Text-to-image Generation with ID-semantics Decoupling

Overview

In the field of personalized text-to-image generation, the aspiration to perfectly preserve individual identities while adhering to the text semantic context has long been a challenging pursuit. The paper "Infinite-ID: Identity-preserved Personalization via ID-semantics Decoupling Paradigm" rises to this challenge by proposing a novel method that decouples identity and text semantics to remarkably maintain identity fidelity alongside semantic consistency. Through a unique approach of identity-enhanced training and an ingenious feature interaction mechanism, Infinite-ID paves the way for generating highly personalized images adhering to both the nuances of individual identities and the stipulations of textual prompts.

Methodology

ID-semantics Decoupling Paradigm

The core innovation of Infinite-ID lies in its ID-semantics decoupling paradigm. Contrary to existing methods that often entangle identity and text semantics leading to compromised fidelity or semantic consistency, Infinite-ID successfully separates the representation of identity from textual semantics. This separation is achieved through identity-enhanced training, which involves the exclusive capture of identity information devoid of textual interference, thereby enhancing identity fidelity. This strategy not only improves the model's ability to retain the reference image's identity but also permits unhampered semantic interpretation of textual prompts.

Feature Interaction Mechanism

To effectively merge identity and text semantics, Infinite-ID introduces a sophisticated feature interaction mechanism constituted by a mixed attention module and an Adaptive Instance Normalization (AdaIN)-mean operation. This mechanism adeptly combines identity and semantic information, facilitating the generation of images that not only bear a strong resemblance to the provided identity but are also semantically coherent with the text prompt. Furthermore, the AdaIN-mean operation offers fine control over the stylistic elements of the generated images, enhancing the model's versatility in producing diverse stylistic renditions.

Experimental Results

Extensive experiments conducted on both raw photo generation and style image generation attest to the superior performance of Infinite-ID against contemporary state-of-the-art methods. Through a rigorous quantitative and qualitative analysis, Infinite-ID demonstrates its remarkable ability to produce images with high identity fidelity and semantic consistency across various styles and scenes. This performance is attributed to the effective decoupling of image and text information and the adept fusion of these elements during the generation process.

Implications and Future Directions

The implications of such a methodology are vast, spanning from personalized AI portraits to virtual try-on applications. By mastering the art of preserving identity while accommodating a wide range of text-directed semantics and styles, Infinite-ID has the potential to significantly enhance personalized content creation. Furthermore, the paradigm of ID-semantics decoupling opens new avenues for future research in personalized text-to-image generation. It encourages the exploration of more sophisticated mechanisms for identity preservation and semantic interpretation, possibly extending beyond human faces to other entities requiring personalized representation.

Conclusion

In summary, Infinite-ID marks a significant advancement in the domain of personalized text-to-image generation. By successfully decoupling and reintegrating identity and semantic information, it addresses a crucial trade-off faced by preceding methods. The developed identity-preserved personalization framework not only sets a new benchmark in generating semantically consistent and identity-faithful images but also provides a promising direction for future exploration in generative AI. However, the journey to perfect identity-preserving personalization is far from over. Infinite-ID, while powerful, encounters limitations in multi-object personalization and may exhibit artifacts under certain conditions, delineating the path for ongoing research and development in this fascinating field of paper.