Latent Bias Alignment for High-Fidelity Diffusion Inversion in Real-World Image Reconstruction and Manipulation
arXiv:2603.23903v1 Announce Type: cross
Abstract
Recent research has demonstrated that text-to-image diffusion models have the capability to generate high-quality images guided by text prompts. However, a pertinent question arises: can these models also be utilized to generate or approximate real-world images from seed noise? This challenge is referred to as the diffusion inversion problem, which is essential for integrating diffusion models with real-world applications. Despite progress, existing diffusion inversion methods often encounter issues related to low reconstruction quality and insufficient robustness.
Challenges in Diffusion Inversion
Two primary challenges must be addressed to enhance the efficacy of diffusion inversion:
- Misalignment between Inversion and Generation Trajectories: During the diffusion process, there is often a disconnect between the paths taken during inversion and those used for generation.
- Mismatched Processes: The diffusion inversion process does not always align well with the VQ autoencoder (VQAE) reconstruction, leading to inefficiencies and inaccuracies.
Proposed Solutions
To tackle these challenges, we introduce two innovative strategies:
- Latent Bias Optimization (LBO): At each inversion step, a latent bias vector is incorporated and learned to minimize the misalignment between the inversion and generation trajectories. This optimization aims to enhance the overall coherence of the diffusion process.
- Image Latent Boosting (ILB): This technique involves approximate joint optimization of the diffusion inversion and VQAE reconstruction processes. By learning to adjust the image latent representation, this strategy establishes a robust connection between the two processes, thereby improving the quality of image reconstruction.
Experimental Results
Extensive experiments have been conducted to evaluate the effectiveness of the proposed methods. The results indicate a significant improvement in the image reconstruction quality of the diffusion model. Furthermore, the performance of downstream tasks, such as image editing and rare concept generation, has also shown considerable enhancement.
Conclusion
In summary, the introduction of Latent Bias Optimization and Image Latent Boosting presents a promising approach to overcoming the challenges associated with diffusion inversion. By addressing the misalignment and mismatch issues, these methods pave the way for improved real-world image reconstruction and manipulation capabilities within diffusion models. As research in this field progresses, it is expected that these innovations will contribute to the development of more robust and versatile image generation technologies.