FastDiSS: Few-step Match Many-step Diffusion Language Model on Sequence-to-Sequence Generation
arXiv:2604.05551v1
Announce Type: cross
Abstract
Self-conditioning has been pivotal to the success of continuous diffusion language models, primarily due to its ability to rectify prior errors. However, this capability tends to diminish in scenarios where diffusion is most beneficial for application: specifically, in few-step sampling, which facilitates rapid inference. In our research, we demonstrate that when models are limited to a small number of denoising steps, inaccuracies in self-conditioning lead to a significant approximation gap. This error compounds across denoising steps, ultimately overshadowing the quality of the generated samples.
Introduction
In the fast-evolving landscape of natural language processing, the efficacy of models hinges on their ability to balance speed and quality. Continuous diffusion models have emerged as a promising strategy; however, their reliance on self-conditioning poses challenges in fast inference scenarios. The FastDiSS framework seeks to bridge this gap, providing a robust alternative to traditional methods.
Methodology
To address the limitations of existing models, we introduce a novel training framework that actively mitigates self-conditioning errors during the learning phase. This is achieved by perturbing the self-conditioning signal to align with the noise encountered during inference. Our approach enhances the model’s resilience to prior estimation inaccuracies.
Key Features
- Robust Self-Conditioning: By adjusting the self-conditioning signal during training, we minimize the risk of error accumulation throughout the denoising process.
- Token-Level Noise Awareness: This mechanism prevents saturation during training, leading to improved optimization and performance.
- Speed and Efficiency: FastDiSS achieves an impressive 400x reduction in inference speed while maintaining competitive performance against one-step diffusion frameworks.
Results
Our extensive experiments across various conditional generation benchmarks reveal that the FastDiSS framework consistently outperforms standard continuous diffusion models. The enhancements in robustness and speed not only elevate the quality of outputs but also position FastDiSS as a viable option for real-world applications.
Conclusion
The FastDiSS model represents a significant advancement in the realm of sequence-to-sequence generation. By effectively addressing the challenges associated with self-conditioning in few-step sampling, we pave the way for faster, more reliable natural language processing solutions. Future research will explore further optimizations and potential applications of this innovative framework.
For more detailed insights, refer to the full version of our study available on arXiv.