1. Introduction
Diffusion models have revolutionized image generation, producing remarkably high-fidelity and diverse samples; however, their computational demands, especially for fine-grained details, remain a significant challenge. Current Diffusion Transformer (DiT) architectures, while powerful, often require extensive resources for training and inference, limiting their practical application. This work addresses the need for efficient generation of fine-grained images without compromising quality. Models used in this article include EFDiT, and comparisons are made against established Diffusion Transformer (DiT) architectures and denoising diffusion probabilistic models (DDPMs).
2. Related Work
Previous research in generative models includes GANs, VAEs, and more recently, diffusion models, which have shown superior image synthesis capabilities. Early diffusion models like DDPMs laid the groundwork, while Diffusion Transformers (DiT) integrated the scalability of transformers, demonstrating impressive results on high-resolution image generation. However, existing methods often struggle with balancing computational efficiency and the generation of intricate, fine-grained details, particularly when scaling to larger datasets or higher resolutions. Our work builds upon these foundations by specifically targeting efficiency in fine-grained synthesis.
3. Methodology
EFDiT introduces a modified transformer block that incorporates a multi-scale feature fusion mechanism and a sparsely connected attention module to enhance fine-grained detail capture while reducing computational load. The model utilizes a hierarchical architecture where lower-resolution features guide the generation of higher-resolution details, enabling efficient processing without sacrificing perceptual quality. Furthermore, we employ a novel training strategy that prioritizes the refinement of subtle visual attributes, allowing the model to learn fine details more effectively. The sampling process is also optimized with an adaptive step schedule for faster inference.
4. Experimental Results
Experiments conducted on various fine-grained image datasets, such as Flowers and Birds, demonstrate EFDiT's superior performance across key metrics compared to existing Diffusion Transformers. The model consistently achieves lower FID scores and higher Inception Scores, indicating both higher perceptual quality and better diversity in generated images, while showing significant reductions in inference time. For instance, EFDiT reduces the average generation time by 30% while maintaining competitive FID scores. The table below summarizes the core performance metrics comparing EFDiT against baseline models, highlighting improvements in both efficiency and quality.
| Model | FID Score (↓) | Inception Score (↑) | Inference Time (s/image) (↓) |
|---|---|---|---|
| DiT-L/2 | 6.85 | 110.2 | 2.5 |
| DiT-B/8 | 8.12 | 98.5 | 1.8 |
| EFDiT (Ours) | 5.92 | 115.7 | 1.2 |
5. Discussion
The results confirm that EFDiT successfully addresses the trade-off between computational efficiency and fine-grained image generation quality. The observed improvements in FID and Inception Scores, coupled with reduced inference times, suggest that our architectural and training innovations are effective. This model's ability to generate highly detailed images efficiently has significant implications for applications requiring real-time synthesis or deployment on edge devices. Future work will explore extending EFDiT to conditional generation tasks and incorporating even more advanced efficiency techniques.