WMAdapter: Adding WaterMark Control to Latent Diffusion Models
Authors: Hai Ci, Yiren Song, Pei Yang, Jinheng Xie, Mike Zheng Shou
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that WMAdapter provides strong flexibility, superior image quality, and competitive watermark robustness. Code: https://github.com/ showlab/WMAdapter |
| Researcher Affiliation | Academia | 1Show Lab, National University of Singapore, Singapore. Correspondence to: Mike Zheng Shou <EMAIL>. |
| Pseudocode | No | The paper describes the methodology using text and architectural diagrams (Figure 2, Figure 3) but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code: https://github.com/ showlab/WMAdapter |
| Open Datasets | Yes | ALL training and finetuning steps are performed on MS-COCO 2017 (Lin et al., 2014) training set. |
| Dataset Splits | Yes | ALL training and finetuning steps are performed on MS-COCO 2017 (Lin et al., 2014) training set. Validation is performed on COCO 2017 validation set. |
| Hardware Specification | Yes | For the first stage training, we adopt 8 NVIDIA A5000 GPUs of 24 GB memory, with per-GPU batchsize of 2, Adam W optimizer (Loshchilov & Hutter, 2017), a learning rate of 5e-4. We train the model for 2 epochs, taking about 5 hours. For the second stage finetuning, we use a single A5000 GPU. |
| Software Dependencies | No | The paper mentions 'Adam W optimizer' but does not specify particular software libraries or frameworks with version numbers that would be required to replicate the experiment. |
| Experiment Setup | Yes | For the first stage training, we adopt 8 NVIDIA A5000 GPUs of 24 GB memory, with per-GPU batchsize of 2, Adam W optimizer (Loshchilov & Hutter, 2017), a learning rate of 5e-4. We train the model for 2 epochs, taking about 5 hours. For the second stage finetuning, we use a single A5000 GPU. We set the mini-batch to 2. We also use the Adam W optimizer and a start learning rate of 5e-4. However, we adopt a per-step cosine learning rate decay with 20 warm-up steps. Unless otherwise specified, the total fine-tuning process defaults to 2,000 steps, lasting for about 50 minutes. |