DiLaDiff: Distilled Latent-Augmented Diffusion for Language Modeling
DiLaDiff combines latent continuous diffusion with discrete masking, achieving high-quality, fast language generation.
Key Findings
Methodology
This paper introduces DiLaDiff, a hybrid model integrating auto-encoder-based semantic latent spaces with diffusion in the latent domain. It involves: 1) fine-tuning a masked diffusion model to learn a semantic latent space; 2) training a continuous latent diffusion model to learn the prior over these latents; 3) distilling the latent diffusion trajectories into a few-step generative process via a consistency model. This approach maintains high generation quality while significantly accelerating inference. Experiments demonstrate that, without distillation, the latent-guided diffusion surpasses baseline masked diffusion in quality and speed; with distillation, the model generates in just 5 steps, matching performance of 200-step models, with negligible inference time.
Key Results
- On OpenWebText, DiLaDiff achieves a 7× inference speedup over baseline models, reduces perplexity by over 30%, and improves MAUVE scores by 0.1 at batch size 32, outperforming traditional masked diffusion models.
- Undistilled, the model outperforms the baseline in both quality and efficiency; distilled, it approaches the original model's performance with only 5 diffusion steps, drastically reducing computational cost.
- Regularization and multi-scale distillation enable the model to capture semantic nuances, producing diverse yet coherent texts, validated through semantic similarity metrics and ablation studies.
Significance
This work addresses a core challenge in language generation: balancing quality and speed. By integrating continuous latent diffusion with discrete decoding, it overcomes the limitations of existing models that either sacrifice coherence or efficiency. The proposed distillation technique further enables practical deployment, making high-quality, fast language models feasible. This advances the state-of-the-art in generative modeling, opening avenues for real-time applications like chatbots, content creation, and multi-modal systems. It also bridges the gap between continuous and discrete diffusion paradigms, enriching the theoretical landscape of generative AI.
Technical Contribution
The paper's key innovation lies in the hybrid architecture combining auto-encoder-learned semantic latent spaces with diffusion in the latent domain, enabling global dependency modeling. The introduction of a consistency model for distilling latent trajectories into few-step generators is novel, reducing inference complexity. The regularization strategies ensure the latent space encodes meaningful semantics, facilitating effective diffusion guidance. This approach differs from prior work by unifying continuous and discrete diffusion techniques, providing theoretical guarantees on speed-quality trade-offs, and demonstrating practical efficiency gains. The model's ability to perform high-quality generation with minimal steps marks a significant leap forward.
Novelty
This is the first work to successfully combine latent continuous diffusion with discrete token diffusion, employing a consistency-based distillation to produce a few-step generator. Unlike previous models limited to either token-wise or latent modeling, this approach leverages the semantic richness of latent spaces and the efficiency of distillation, achieving a unique balance of quality and speed. The integration of these components represents a new paradigm in language modeling, offering both theoretical novelty and practical advantages.
Limitations
- The model's performance depends heavily on the quality of the auto-encoder's semantic latent space; poor regularization can lead to semantic drift and incoherence.
- Training involves complex regularization and hyperparameter tuning, which may limit scalability and robustness across different datasets or languages.
- While distillation reduces inference steps significantly, the initial training cost remains high, and the model may struggle with highly ambiguous or out-of-distribution inputs.
Future Work
Future research will explore multi-modal extensions, integrating visual or auditory features into the latent space to enhance diversity and control. Developing more robust and scalable distillation algorithms, possibly leveraging reinforcement learning or meta-learning, could further improve efficiency. Additionally, applying this framework to multilingual and low-resource settings, as well as refining the theoretical understanding of latent diffusion in language, will be key directions.
AI Executive Summary
Recent advances in generative modeling have seen diffusion models excel in continuous data like images and audio, but their application to natural language remains challenging. Traditional discrete diffusion models, such as masked diffusion language models, struggle to capture the complex dependencies between words, leading to a trade-off: high quality often comes at the cost of slow inference. This paper introduces DiLaDiff, a novel hybrid approach that marries the strengths of continuous latent diffusion with discrete token decoding.
The core idea is to learn a semantic latent space via an auto-encoder fine-tuned from a masked diffusion model. This latent space captures high-level semantic information and allows the application of continuous diffusion techniques to model the prior distribution over these representations. During inference, the model samples from this prior using a continuous diffusion process, then decodes tokens in parallel, significantly speeding up generation.
A key innovation is the use of a consistency model to distill the latent diffusion trajectories into a few-step generative process. This distillation reduces the computational overhead of continuous diffusion, enabling the latent variable to be generated in negligible time compared to traditional discrete decoding. Experiments on OpenWebText demonstrate that, at batch size 32, DiLaDiff achieves a 7× speedup, with perplexity and semantic coherence metrics surpassing baseline models. The model maintains high diversity and semantic fidelity, validated through BERTScore and MAUVE scores.
This work marks a significant step toward practical, high-quality language generation, addressing the speed-quality bottleneck of existing diffusion models. By effectively combining latent continuous diffusion with discrete decoding, it opens new avenues for real-time natural language applications. Despite its success, challenges remain in optimizing the auto-encoder regularization and extending the approach to multilingual or multi-modal scenarios. Future work will focus on these directions, aiming to further enhance the scalability and robustness of the framework.
Deep Dive
Abstract
Diffusion language models intrinsically fail to capture correlations between decoded tokens, which leads to a harsh trade-off between sampling quality and throughput. To solve this issue, we propose DiLaDiff, a variant of masked diffusion language models with three components: (1) a continuous latent space with semantic capabilities, learned by an auto-encoder fine-tuned from an existing masked diffusion language model; (2) a latent diffusion model learning the prior over the encoder distribution; (3) a consistency model distilling the learned prior into a few-step latent generative model. We show that, even without distillation, our latent-guided diffusion model outperforms the masked diffusion baseline while significantly accelerating inference. Consistency distillation further lowers the computational overhead of continuous diffusion, such that the latent is generated in negligible time compared to discrete decoding.