Diffusion models have gained importance in image, video, and audio generation, but their sampling process is computationally expensive compared to training. Consistency models offer faster sampling but sacrifice image quality, with variants being consistency training (CT) and consistency distillation (CD). TRACT focuses on distillation, dividing the diffusion path into stages to improve performance. However, neither consistency nor TRACT models achieve comparable performance to standard diffusion models.
Previous work includes Consistency Models and TRACT. The first operates in multiple stages, simplifying modeling tasks and improving performance, while the second focuses on distillation, progressively reducing the stages to one or two for sampling. DDIM showed that deterministic samplers degrade more gracefully than stochastic ones with limited sampling steps. Other approaches include second-order Heun samplers, different SDE integrators, specialized architectures, and progressive distillation to reduce model evaluations and sampling steps.
Google Deepmind researchers have proposed a machine learning method that unifies consistency and TRACT models to reduce the performance gap between standard diffusion models and low-pass variants. Relaxes the single-step restriction, allowing evaluations of 4, 8, or 16 functions. Generalizations include adaptation of step schedule annealing and synchronized dropout from consistency modeling. Multi-step coherence models divide the diffusion process into segments, improving performance with fewer steps. A deterministic sampler called adjusted DDIM (aDDIM) corrects integration errors to obtain sharper samples.
Multi-step consistency models divide the diffusion process into equal segments to simplify modeling. It uses a consistency loss to approximate path integrals by minimizing pairwise discrepancies. The algorithm involves training this loss in z-space but re-parametrizing it in x-space for interpretability. Focusing on the loss v, its goal is to avoid the collapse of degenerate solutions, converging to diffusion models with increasing steps. The approach hypothesizes faster convergence through adjustments and offers a trade-off between sample quality and duration as steps increase.
Experiments demonstrate that multi-step consistency models achieve state-of-the-art FID scores on ImageNet64, outperforming progressive distillation (PD) at various step counts. Furthermore, on ImageNet128, multi-pass consistency models outperform PDs. Qualitatively, comparisons reveal minor differences in sampling details between multistep consistency models and standard diffusion models in text-to-image conversion tasks. These results highlight the effectiveness of multi-step consistency models in improving sample quality and efficiency compared to existing methods.
In conclusion, researchers introduce multi-step consistency models, fusing them with consistency models and TRACT to reduce the performance gap between standard diffusion and few-step sampling. It offers a direct balance between sample quality and speed, achieving performance superior to standard diffusion in just eight steps. This unification significantly improves sample quality and efficiency in generative modeling tasks.
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Asjad is an internal consultant at Marktechpost. He is pursuing B.tech in Mechanical Engineering at Indian Institute of technology, Kharagpur. Asjad is a machine learning and deep learning enthusiast who is always researching applications of machine learning in healthcare.
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