Inserting Anybody in Diffusion Models via Celeb Basis
Authors: Ge Yuan, Xiaodong Cun, Yong Zhang, Maomao Li, Chenyang Qi, Xintao Wang, Ying Shan, Huicheng Zheng
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on the self-collected 2K synthetic facial images generated by Style GAN [2]. |
| Researcher Affiliation | Collaboration | 1 School of Computer Science and Engineering, Sun Yat-sen University 2 Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China 3 Guangdong Key Laboratory of Information Security Technology 4 Tencent AI Lab 5 The Hong Kong University of Science and Technology |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | Project page is at: http://celeb-basis. github.io. Code is at: https://github.com/ygtxr1997/Celeb Basis. |
| Open Datasets | Yes | We conduct experiments on the self-collected 2K synthetic facial images generated by Style GAN [2]. |
| Dataset Splits | No | The paper describes a single-shot personalization method where coefficients are optimized from a single facial photo, rather than training on predefined dataset splits. The 2K synthetic images are used as inputs for evaluation. |
| Hardware Specification | Yes | We train the MLP with a learning rate of 0.005 and batch size of 2 on a single NVIDIA A100 GPU. |
| Software Dependencies | No | The paper mentions various models and tools used (e.g., Stable Diffusion, CLIP, Arc Face), but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | We train the MLP with a learning rate of 0.005 and batch size of 2 on a single NVIDIA A100 GPU. The training augmentation includes horizontal flip, color jitter, and random scaling ranging in 0.1 1.0. For single identity training, the optimization costs 400 steps, taking 3 minutes. For 10 identities joint training, we found that training 2,500 steps is enough, taking 18 minutes (averaged about 250 steps and 108 seconds for each identity). |