Core Concepts
FineDiffusion introduces an efficient parameter-tuning approach to scale up diffusion models for fine-grained image generation with 10,000 classes. By fine-tuning key components and utilizing hierarchical label information, the method achieves superior performance while reducing training and storage overheads.
Abstract
FineDiffusion presents a novel strategy for large-scale fine-grained image generation by efficiently fine-tuning pre-trained diffusion models. The method focuses on tiered label embeddings, bias terms, and normalization layers to achieve state-of-the-art results. By leveraging superclass information and introducing a novel sampling method, FineDiffusion significantly improves image generation quality while reducing computational costs.
The content discusses the challenges of fine-grained image generation and the need for efficient methods to scale up diffusion models. It introduces FineDiffusion as a solution that accelerates training speed, reduces storage requirements, and outperforms existing parameter-efficient fine-tuning methods. The method is evaluated on datasets like iNaturalist 2021 mini and VegFru, showcasing its effectiveness in generating high-quality images across diverse categories.
Key points include:
- Introduction of FineDiffusion for large-scale fine-grained image generation.
- Efficient parameter tuning focusing on tiered label embeddings, bias terms, and normalization layers.
- Utilization of superclass information and novel sampling methods to enhance image quality.
- Comparison with existing methods like full fine-tuning, BitFit, and DiffFit on various datasets.
- Visualization of class embeddings using t-SNE technique to demonstrate the effectiveness of FineDiffusion.
Stats
Compared to full fine-tuning: 1.77% parameters tuned; 1.56× training speed-up.
FID scores: FineDiffusion - 9.776; Full Fine-tuning - 13.034; BitFit - 15.022; DiffFit - 15.068.
LPIPS scores: FineDiffusion - 0.721; Full Fine-tuning - 0.651; BitFit - 0.654; DiffFit - 0.653.
Quotes
"FineDiffusion significantly accelerates training and reduces storage overhead."
"Our method showcases an effective means of achieving efficient parameter fine-tuning."
"Extensive qualitative and quantitative experiments demonstrate the superiority of our method."