Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
PTQD: Accurate Post-Training Quantization for Diffusion Models
Authors: Yefei He, Luping Liu, Jing Liu, Weijia Wu, Hong Zhou, Bohan Zhuang
NeurIPS 2023 | Venue PDF | 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) |