Temporal Dynamic Quantization for Diffusion Models
Authors: Junhyuk So, Jungwon Lee, Daehyun Ahn, Hyungjun Kim, Eunhyeok Park
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |