PuLID: Pure and Lightning ID Customization via Contrastive Alignment
Authors: Zinan Guo, Yanze Wu, Chen Zhuowei, Lang chen, Peng Zhang, Qian HE
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that Pu LID achieves superior performance in both ID fidelity and editability. |
| Researcher Affiliation | Industry | Zinan Guo Yanze Wu Zhuowei Chen Lang Chen Peng Zhang Qian He Byte Dance Inc. guozinan.1@bytedance.com wuyanze123@gmail.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes and models are available at https://github.com/To The Beginning/Pu LID. |
| Open Datasets | Yes | As a supplementary resource, we also used a recent open-source test set, Unsplash-50 [8], which comprises 50 portrait images uploaded to the Unsplash website between February and March 2024. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for its main training dataset. |
| Hardware Specification | Yes | Training is performed with Py Torch and diffusers on 8 NVIDIA A100 GPUs in an internal cluster. |
| Software Dependencies | No | The paper mentions "Py Torch and diffusers" but does not provide specific version numbers for these or any other ancillary software components. |
| Experiment Setup | Yes | We set the λalign-sem to 0.6, λalign-layout to 0.1, and λid to 1.0. In the Lightning T2I training branch, we set the resolution of the generated image to 768 × 768 to conserve memory. |