PTQD: Accurate Post-Training Quantization for Diffusion Models
Authors: Yefei He, Luping Liu, Jing Liu, Weijia Wu, Hong Zhou, Bohan Zhuang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our method outperforms previous post-training quantized diffusion models in generating high-quality samples, with only a 0.06 increase in FID score compared to full-precision LDM-4 on Image Net 256 256, while saving 19.9 bit operations. |
| Researcher Affiliation | Academia | 1Zhejiang University, China 2ZIP Lab, Monash University, Australia |
| Pseudocode | Yes | Algorithm 1: Quantization noise correction. |
| Open Source Code | Yes | Code is available at https://github.com/ziplab/PTQD. |
| Open Datasets | Yes | We conduct image synthesis experiments using latent diffusion models (LDM) [45] on three standard benchmarks: Image Net[9], LSUN-Bedrooms, and LSUNChurches [60], each with a resolution of 256 256. |
| Dataset Splits | No | The paper mentions using datasets but does not explicitly provide details on training, validation, or test splits (e.g., percentages or sample counts for each split). |
| Hardware Specification | Yes | We have measured the latency of matrix multiplication and convolution operations in quantized and full-precision diffusion models using an RTX3090 GPU, as shown in Table 4. |
| Software Dependencies | No | The paper mentions "CUTLASS [24]" but does not provide specific version numbers for software dependencies or libraries used in their experiments. |
| Experiment Setup | Yes | All experimental configurations, including the number of steps, variance schedule (denoted by eta in the following), and classifier-free guidance scale, follow the official implementation [45]. ... LDM-4 (steps = 20 eta = 0.0 scale = 3.0) ... LDM-4 (steps = 250 eta = 1.0 scale = 1.5) |