Theory of Consistency Diffusion Models: Distribution Estimation Meets Fast Sampling
Authors: Zehao Dou, Minshuo Chen, Mengdi Wang, Zhuoran Yang
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper contributes towards the first statistical theory for consistency models, formulating their training as a distribution discrepancy minimization problem. Our analysis yields statistical estimation rates based on the Wasserstein distance for consistency models, matching those of vanilla diffusion models. Additionally, our results encompass the training of consistency models through both distillation and isolation methods, demystifying their underlying advantage. |
| Researcher Affiliation | Academia | 1Department of Statistics and Data Science, Yale University, New Haven, US 2Electrical and Computer Engineering, Princeton University, Princeton, US. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements or links regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use or describe a specific dataset for experiments with public access information. It refers to 'the given dataset is {xj}j [n], which is assumed to be i.i.d sampled from pdata' but this is a theoretical notation, not a specific dataset that was used in experiments. |
| Dataset Splits | No | The paper is theoretical and does not report on experiments or data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers for reproducibility of experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |