When StyleGAN Meets Stable Diffusion: a $\mathscr{W}_+$ Adapter for Personalized Image Generation (2311.17461v1)

Abstract: Text-to-image diffusion models have remarkably excelled in producing diverse, high-quality, and photo-realistic images. This advancement has spurred a growing interest in incorporating specific identities into generated content. Most current methods employ an inversion approach to embed a target visual concept into the text embedding space using a single reference image. However, the newly synthesized faces either closely resemble the reference image in terms of facial attributes, such as expression, or exhibit a reduced capacity for identity preservation. Text descriptions intended to guide the facial attributes of the synthesized face may fall short, owing to the intricate entanglement of identity information with identity-irrelevant facial attributes derived from the reference image. To address these issues, we present the novel use of the extended StyleGAN embedding space $\mathcal{W}_+$, to achieve enhanced identity preservation and disentanglement for diffusion models. By aligning this semantically meaningful human face latent space with text-to-image diffusion models, we succeed in maintaining high fidelity in identity preservation, coupled with the capacity for semantic editing. Additionally, we propose new training objectives to balance the influences of both prompt and identity conditions, ensuring that the identity-irrelevant background remains unaffected during facial attribute modifications. Extensive experiments reveal that our method adeptly generates personalized text-to-image outputs that are not only compatible with prompt descriptions but also amenable to common StyleGAN editing directions in diverse settings. Our source code will be available at \url{https://github.com/csxmli2016/w-plus-adapter}.

A Technical Overview of the Image Generation Method Using StyleGAN and Stable Diffusion Integration

The paper "When StyleGAN Meets Stable Diffusion: a $\mathcal{W}_+$ Adapter for Personalized Image Generation" introduces a sophisticated approach to enhancing personalized text-to-image (T2I) generation. This is achieved by aligning StyleGAN's extended $\mathcal{W}_+$ latent space with Stable Diffusion (SD) models to improve identity preservation and semantic editability in image synthesis. The core idea seeks to address existing challenges in balancing identity maintenance and attribute variability in T2I models, particularly in facial image generation.

Technical Insights

1. Latent Space Alignment: The approach leverages the semantically rich $\mathcal{W}_+$ space from StyleGAN to achieve better identity preservation and expression editing capabilities in diffusion models. This method is pioneering in its alignment of the $\mathcal{W}_+$ space with SD, facilitated through a mapping network and residual cross-attention modules.
2. Training Procedure: The method involves a two-stage training process:
   • Stage I: Focuses on aligning the $\mathcal{W}_+$ space with SD models. A mapping network projects $w_+$ vectors—latent vectors from StyleGAN's encoder—into a dimensional space compatible with SD. This phase ensures the model can map identities accurately into generated images.
   • Stage II: Enhances the adaptability of the aligned model for in-the-wild scenarios. This phase optimizes the cross-attention components to preserve identity features while allowing for diverse attribute modifications in broader contexts without altering identity-irrelevant aspects during synthesis.
3. Key Components and Innovations:
   • Mapping Network: Trains to project StyleGAN's $w_+$ embeddings into a format that SD models can interpret effectively.
   • Residual Cross-Attention: Offers a mechanism to conditionally integrate identity information into the SD denoising process, balancing text and identity influences.
4. Training Data Diversity: The paper employs extensive datasets, including FFHQ with both real and synthetic images, ensuring a robust training process that generalizes well to real-world scenarios.
5. Attribute Editability: The approach supports fine-grained editing of facial attributes by manipulating $w_+$ vectors along predefined semantic trajectories ($\Delta w$), which are informed by the interpretable directions in StyleGAN's latent space, such as expressions or age.

Empirical Validation

Extensive experiments conducted reveal that the newly introduced method outperforms existing approaches in terms of identity preservation and accuracy in reflecting text conditions. Notably, the system demonstrates flexibility across multiple Stable Diffusion models. Quantitative assessments using metrics such as CLIP Score and identity distance further underline the system's effectiveness.

Implications and Future Directions

• Practical Applications: The fusion between StyleGAN’s semantically rich space and SD models opens new avenues for personalized content creation, potentially enhancing applications in custom avatar creation, movie storyboarding, and advanced virtual reality environments.
• Scope for Enhancements: While the method shows promise, the paper acknowledges limitations, such as challenges in preserving subtle identity features due to potential losses in detail during StyleGAN inversion. Exploring alternative inversion techniques or enhancing $\mathcal{W}_+$ projection accuracy might mitigate these issues.
• Broader Applicability: Beyond facial images, the incorporation of other domains with distinct latent spaces into T2I models could revolutionize how personalized content is approached in fields like digital art and design.

In conclusion, this paper successfully introduces a framework that blends the potent image editing capabilities of StyleGAN with the flexibility of SD models, creating a harmonious approach that advances state-of-the-art personalized image synthesis. The methodology and results suggest a promising trajectory for future research in AI-driven image generation and customization.