VCT: Training Consistency Models with Variational Noise Coupling
Authors: Gianluigi Silvestri, Luca Ambrogioni, Chieh-Hsin Lai, Yuhta Takida, Yuki Mitsufuji
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on multiple image datasets demonstrate significant improvements: our method surpasses baselines, achieves state-of-the-art FID among non-distillation CT approaches on CIFAR-10, and matches So TA performance on Image Net 64 64 with only two sampling steps. |
| Researcher Affiliation | Collaboration | 1One Planet Research Center, imec-the Netherlands, Wageningen, the Netherlands 2Donders Institute for Brain, Cognition and Behaviou, Nijmegen, the Netherlands 3Sony AI, Tokyo, Japan 4Sony Group Corporation. Correspondence to: Gianluigi Silvestri <EMAIL, EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Variational Consistency Training (VCT) ... Algorithm 2 Multistep Variational Consistency Sampling |
| Open Source Code | Yes | Code is available at https://github.com/sony/vct. |
| Open Datasets | Yes | We evaluate the models on the image datasets Fashion-MNIST (Xiao et al., 2017), CIFAR-10 (Krizhevsky et al., 2009), FFHQ 64 64 (Karras et al., 2019) and (classconditional) Image Net 64 64 (Deng et al., 2009). |
| Dataset Splits | No | The paper mentions standard datasets like Fashion-MNIST, CIFAR-10, FFHQ, and Image Net, but it does not explicitly state the training/test/validation splits (e.g., percentages or sample counts) used for these datasets in the main text or appendices. It refers to using existing baselines' settings but does not detail the splits within the paper. |
| Hardware Specification | Yes | GPU types H100 |
| Software Dependencies | No | The paper discusses model architectures like DDPM++ and EDM2-S but does not provide specific software dependencies with version numbers (e.g., PyTorch 1.9, CUDA 11.1). |
| Experiment Setup | Yes | We report the training details for our models in Tables 4 and 5. Note that the baselines are the ones from our reimplementation. The models have the same number of parameters and training hyperparameters regardless of the transition kernel used. ... Minibatch size, Iterations, Dropout probability, Optimizer, Learning rate, EMA rate |