SVDiff: Compact Parameter Space for Diffusion Fine-Tuning (2303.11305v4)

Abstract: Diffusion models have achieved remarkable success in text-to-image generation, enabling the creation of high-quality images from text prompts or other modalities. However, existing methods for customizing these models are limited by handling multiple personalized subjects and the risk of overfitting. Moreover, their large number of parameters is inefficient for model storage. In this paper, we propose a novel approach to address these limitations in existing text-to-image diffusion models for personalization. Our method involves fine-tuning the singular values of the weight matrices, leading to a compact and efficient parameter space that reduces the risk of overfitting and language drifting. We also propose a Cut-Mix-Unmix data-augmentation technique to enhance the quality of multi-subject image generation and a simple text-based image editing framework. Our proposed SVDiff method has a significantly smaller model size compared to existing methods (approximately 2,200 times fewer parameters compared with vanilla DreamBooth), making it more practical for real-world applications.

• The paper presents SVDiff, a novel method that fine-tunes diffusion models by optimizing only the singular values of weight matrices.
• It uses singular value decomposition and a Cut-Mix-Unmix strategy to reduce overfitting and enhance multi-subject image generation.
• The approach achieves quality comparable to full fine-tuning methods like DreamBooth while significantly lowering computational and storage demands.

SVDiff: Compact Parameter Space for Diffusion Fine-Tuning

The paper presents SVDiff, a novel approach tailored for efficient fine-tuning of text-to-image diffusion models. Modern diffusion models have demonstrated proficiency in generating high-quality images from text prompts. However, their customization remains problematic due to the risk of overfitting and impracticality resulting from their massive parameter sizes. SVDiff addresses these issues by fine-tuning only the singular values of the diffusion model's weight matrices, thereby creating a substantially compact parameter space that reduces overfitting and conserves model storage space.

Methodology

The proposed framework, titled SVDiff, leverages a compact parameter space for model personalization. By applying Singular Value Decomposition (SVD) to the weight matrices, the model is fine-tuned exclusively on the singular values. This approach minimizes the risk of overfitting to individual styles or concepts and reduces the parameter count of the model substantially compared to full model fine-tuning techniques like DreamBooth. Notably, SVDiff manages to operate with approximately 2,200 times fewer parameters than the vanilla DreamBooth, showcasing its practical potential for widespread deployment.

Additionally, to enhance the model's ability to learn multiple concepts simultaneously, SVDiff introduces a Cut-Mix-Unmix data-augmentation strategy. This novel technique involves constructing image samples with mixed regions from different concepts and training the model to separate these concepts via attention-based unmixing. This is particularly useful for handling semantically similar categories, which traditional fine-tuning can struggle with.

Key Results

SVDiff's efficacy was demonstrated across several tasks:

• Single-Subject Generation: Results from experiments indicate that SVDiff achieves performance close to that of full weight fine-tuning (e.g., DreamBooth) while maintaining a much smaller footprint.
• Multi-Subject Generation: The Cut-Mix-Unmix augmentation significantly improved the generation of images containing multiple personalized subjects. Human evaluations further endorse the efficacy of SVDiff over traditional methods in managing these images.
• Single Image Editing: SVDiff facilitates flexible image editing, preventing overfitting and maintaining the generation capability of the model without substantial drift from the input styles.

Implications and Future Research Directions

SVDiff introduces a promising direction for diffusion model fine-tuning with substantial implications for AI model efficiency and versatility. By reducing the parameter space size, SVDiff not only facilitates efficient deployment but also opens opportunities for faster adaptation and integration into lightweight devices. Future research might explore combinations of SVDiff with other efficient fine-tuning techniques such as LoRA, potentially offering even greater gains in efficiency.

The compact parameter design espoused by SVDiff could also inspire similar adaptations in other domains where large parameter models are prevalent. Additionally, unexplored avenues such as training-free approaches to customization using SVDiff's parameter space could be a fertile ground for research, leading to immediate fine-tuning possibilities without computational overhead.

Conclusion

Through its efficient use of spectral shifts, SVDiff presents a significant enhancement over existing diffusion fine-tuning models. Its ability to effectively maintain a model's adaptability while conserving computational resources sets a foundation for further innovations in model personalization and adaptation. The methodological contributions of SVDiff underscore the importance of exploring alternative parameters in generative model fine-tuning, motivating further examination of parameter efficiency in AI models at large.