Replay-Based Continual Learning with Dual-Layered Distillation and a Streamlined U-Net for Efficient Text-to-Image Generation (2505.06995v1)

Abstract: Recent advancements in text-to-image diffusion models are hindered by high computational demands, limiting accessibility and scalability. This paper introduces KDC-Diff, a novel stable diffusion framework that enhances efficiency while maintaining image quality. KDC-Diff features a streamlined U-Net architecture with nearly half the parameters of the original U-Net (482M), significantly reducing model complexity. We propose a dual-layered distillation strategy to ensure high-fidelity generation, transferring semantic and structural insights from a teacher to a compact student model while minimizing quality degradation. Additionally, replay-based continual learning is integrated to mitigate catastrophic forgetting, allowing the model to retain prior knowledge while adapting to new data. Despite operating under extremely low computational resources, KDC-Diff achieves state-of-the-art performance on the Oxford Flowers and Butterflies & Moths 100 Species datasets, demonstrating competitive metrics such as FID, CLIP, and LPIPS. Moreover, it significantly reduces inference time compared to existing models. These results establish KDC-Diff as a highly efficient and adaptable solution for text-to-image generation, particularly in computationally constrained environments.

An In-Depth Exploration of "Replay-Based Continual Learning with Dual-Layered Distillation and a Streamlined U-Net for Efficient Text-to-Image Generation"

The paper "Replay-Based Continual Learning with Dual-Layered Distillation and a Streamlined U-Net for Efficient Text-to-Image Generation" introduces significant advancements in text-to-image (T2I) generation by addressing the computational and resource constraints of existing diffusion models, specifically Stable Diffusion (SbDf). This work proposes a novel framework named KDC-Diff, which combines architectural optimization, knowledge distillation (KD), and continual learning (CL) to enhance the efficiency and applicability of these models in real-world, resource-constrained environments.

Key Contributions and Methodology

The paper delineates several critical contributions to T2I generation:

1. Streamlined U-Net Architecture: The paper's primary focus is developing an efficient U-Net architecture for diffusion models, reducing parameters from 859 million to 482 million while maintaining robust performance. This architecture significantly reduces computational complexity and inference time, which are pivotal for applications in limited-resource settings.
2. Knowledge Distillation (KD) Framework: KDC-Diff introduces a dual-layered KD strategy involving both soft and hard target distillation and feature-based distillation to ensure high-fidelity image generation. This strategy helps the student model learn intricate details from a more complex teacher model, thus bridging the gap between efficiency and accuracy.
3. Replay-Based Continual Learning (CL): To mitigate catastrophic forgetting, the paper implements replay-based CL, which utilizes previous class data during new class training. This ensures the model retains prior knowledge while adapting to new data, thereby enhancing robustness and performance over time.

Experimental Evaluation and Results

The model's efficacy is evaluated on Oxford 102 Flower and Butterfly & Moth 100 Species datasets. KDC-Diff exhibits superior performance in various metrics. It achieves a remarkable FID score of 177.3690 and a CLIP score of 28.733 on the Oxford Flowers dataset, outperforming multiple state-of-the-art stable diffusion models. Notably, it reduces inference time to 7.854 seconds per image—showcasing both higher efficiency and effectiveness.

On the Butterfly & Moth dataset, KDC-Diff further demonstrates its robustness, achieving an FID score of 297.66 and the highest CLIP score of 33.89 among evaluations. These results solidify KDC-Diff’s standing as a potent tool in T2I generation, especially within computationally constrained environments.

Implications and Future Directions

The advancements introduced by KDC-Diff not only enhance the accessibility of high-performance T2I models but also push the boundaries of what's achievable within the constraints of limited computational resources. The implications for future developments in T2I and generative AI are significant, as more efficient architectures could democratize AI accessibility across mobile and embedded devices.

The research opens avenues for further work in optimizing diffusion models. Future studies might explore progressive distillation methods, adaptive learning techniques, and advanced techniques that focus on parameter-efficient models without relying on large-scale architectures. Additionally, experimentation with more diverse and complex datasets would extend the model's applicability across different domains.

In conclusion, the paper provides a comprehensive approach to overcoming fundamental challenges in T2I models, making significant strides in efficiency without compromising performance. KDC-Diff represents a strategic advancement in the field of generative AI, showcasing the potential for innovation in both model architecture and teaching-learning frameworks like KD and CL.