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.
Temporal Dynamic Quantization for Diffusion Models
Authors: Junhyuk So, Jungwon Lee, Daehyun Ahn, Hyungjun Kim, Eunhyeok Park
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 17,361 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,844 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 | Our extensive experiments demonstrate substantial improvements in output quality with the quantized diffusion model across various datasets. |
| Researcher Affiliation | Collaboration | 1 Department of Computer Science and Engineering, POSTECH 2 Graduate School of Artificial and Intelligence, POSTECH 3 Squeeze Bits. Inc |
| Pseudocode | No | The paper describes methods and equations, such as the quantization function (Eq. 5), but does not contain any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | The source code is available at https: //github.com/ECo Lab-POSTECH/TDQ_Neur IPS2023. |
| Open Datasets | Yes | For the DDIM experiments, we utilized the CIFAR-10 dataset [45] (32x32), while for the LDM experiments, we employed the LSUN Churches dataset [46] (256x256). |
| Dataset Splits | No | The paper mentions selecting checkpoints with the 'lowest validation loss' in the context of QAT, which implies the use of a validation set. However, it does not provide specific details on the size or methodology of the training/validation/test dataset splits, beyond a 'calibration set of 5120 samples for PTQ'. |
| Hardware Specification | Yes | All of experiments were conducted on the high performance servers having 4x A100 GPUs and 8x RTX3090 GPUs with Py Torch [49] 2.0 framework. |
| Software Dependencies | Yes | All of experiments were conducted on the high performance servers having 4x A100 GPUs and 8x RTX3090 GPUs with Py Torch [49] 2.0 framework. |
| Experiment Setup | Yes | The models were trained for 200K iterations on CIFAR-10 and LSUN-churches, with respective batch sizes of 128 and 32. The learning rate schedule was consistent with the full precision model. |