Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection
Authors: Lichen Bai, Shitong Shao, zikai zhou, Zipeng Qi, Zhiqiang Xu, Haoyi Xiong, Zeke Xie
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Third, our extensive experiments demonstrate that Z-Sampling can generally and significantly enhance generation quality across various benchmark datasets, diffusion models, and performance evaluation metrics. For example, Dream Shaper with Z-Sampling can self-improve with the HPSv2 winning rate up to 94% over the original results. Moreover, Z-Sampling can further enhance existing diffusion models combined with other orthogonal methods, including Diffusion-DPO. The code is publicly available at github.com/xie-lab-ml/Zigzag-Diffusion-Sampling. |
| Researcher Affiliation | Collaboration | Lichen Bai1 Shitong Shao1 Zikai Zhou1 Zipeng Qi2 Zhiqiang Xu3 Haoyi Xiong4 Zeke Xie1 1x Lea F Lab, The Hong Kong University of Science and Technology (Guangzhou) 2Beihang University 3Mohamed bin Zayed University of Artificial Intelligence 4Baidu Inc |
| Pseudocode | Yes | Algorithm 1 Z-Sampling |
| Open Source Code | Yes | The code is publicly available at github.com/xie-lab-ml/Zigzag-Diffusion-Sampling. |
| Open Datasets | Yes | Datasets Pick-a-Pic (Kirstain et al., 2023), Draw Bench dataset (Saharia et al., 2022), and Gen Eval (Ghosh et al., 2024). We leave more details in Appendix A.1. |
| Dataset Splits | Yes | Here we use only the first 100 prompts as the test set, which is sufficient to reflect the model s capabilities. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types, memory amounts) used for running its experiments. It mentions computational costs but not the hardware specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch versions) needed to replicate the experiment. |
| Experiment Setup | Yes | For SD2.1 (Rombach et al., 2022), SDXL (Podell et al., 2023), and Hunyuan-Di T (Li et al., 2024), we perform 50 denoising steps. For Dream Shaper-xl-v2-turbo, which achieves efficient and high-quality generation by fine-tuning SDXL Turbo (Sauer et al., 2023), we set denoising step T only to 4. And we set γ1 = 5.5 in SDXL/SD2.1, γ1 = 6.0 in Hunyuan-Di T, and γ1 = 3.5 in Dream Shaper-xl-v2-turbo, all to the recommended default values. We set the zigzag operation to be executed throughout the entire path (λ = T 1) and inversion guidance scale γ2 as zero, unless we specify them otherwisely. |