Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Adaptive Discretization for Consistency Models
Authors: Jiayu Bai, Zhanbo Feng, Zhijie Deng, TianQi Hou, Robert Qiu, Zenan Ling
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 23,154 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,613 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that ADCMs significantly improve the training efficiency of CMs, achieving superior generative performance with minimal training overhead on both CIFAR-10 and Image Net. |
| Researcher Affiliation | Collaboration | Jiayu Bai1, Zhanbo Feng2, Zhijie Deng2, Tianqi Hou3, Robert C. Qiu1, Zenan Ling1 1School of EIC, Huazhong University of Science and Technology 2School of Computer Science, Shanghai Jiao Tong University 3Huawei |
| Pseudocode | Yes | Algorithm 1 Adaptive Discretization for Consistency Models |
| Open Source Code | Yes | Code is available at https://github.com/ rainstonee/ADCM. |
| Open Datasets | Yes | unconditional CIFAR-10 [16] and class-conditional Image Net 64 64 [3], respectively. |
| Dataset Splits | Yes | unconditional CIFAR-10 [16] and class-conditional Image Net 64 64 [3], respectively. We evaluate the sample quality using FID [8] |
| Hardware Specification | Yes | Table 6: Hyperparameter Settings ... GPU types RTX3090 RTX3090 A100 GPU memory 24G 24G 40G Number of GPUs 1 8 4 |
| Software Dependencies | No | No specific version numbers for software dependencies (e.g., Python, PyTorch, CUDA versions) are explicitly listed in the paper. |
| Experiment Setup | Yes | Appendix A.2 Hyperparameters: Batch Size and EMA. For unconditional CIFAR-10, we use a batch size of 128 with an EMA decay rate of 0.9999... Lagrange Multiplier λ. For unconditional CIFAR-10, we set λ = 0.01... Table 6: Hyperparameter Settings ... Dropout probability 30% 40% 50% Optimizer RAdam Adam Adam Learning rate schedule fixed square root square root Learning rate max 0.0001 0.001 0.0009 Pseudo-Huber c 0.03 0 0 |