Boosting Alignment for Post-Unlearning Text-to-Image Generative Models
Authors: Myeongseob Ko, Henry Li, Zhun Wang, Jonathan Patsenker, Jiachen (Tianhao) Wang, Qinbin Li, Ming Jin, Dawn Song, Ruoxi Jia
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our evaluation demonstrates that our method effectively removes target classes from recent diffusion-based generative models and concepts from stable diffusion models while maintaining close alignment with the models original trained states, thus outperforming stateof-the-art baselines. |
| Researcher Affiliation | Academia | Myeongseob Ko Virginia Tech myeongseob@vt.edu Henry Li Yale University henry.li@yale.edu Zhun Wang University of California, Berkeley zhun.wang@berkeley.edu Jonathan Patsenker Yale University jonathan.patsenker@yale.edu Jiachen T. Wang Princeton University tianhaowang@princeton.edu Qinbin Li University of California, Berkeley liqinbin1998@gmail.com Ming Jin Virginia Tech jinming@vt.edu Dawn Song University of California, Berkeley dawnsong@berkeley.edu Ruoxi Jia Virginia Tech ruoxijia@vt.edu |
| Pseudocode | No | The paper does not contain any pseudocode blocks or clearly labeled algorithm sections. |
| Open Source Code | Yes | Our code will be made available at https://github.com/ reds-lab/Restricted_gradient_diversity_unlearning.git. |
| Open Datasets | Yes | For our CIFAR-10 experiments, we leverage the EDM framework [Karras et al., 2022]...For dataset construction, we used all samples in each class for the CIFAR-10 forgetting dataset and 800 samples for Stable Diffusion experiments. |
| Dataset Splits | Yes | We evaluate model performance on both training prompts (Dr,train) used during unlearning and a separate set of held-out test prompts (Dr,test). These two distinct sets are constructed by carefully splitting semantic dimensions (e.g., activities, environments, moods). Detailed construction procedures for both sets are provided in Appendix D. |
| Hardware Specification | Yes | All experiments were conducted using an NVIDIA H100 GPU. |
| Software Dependencies | No | The paper mentions using the 'EDM framework' and 'pre-trained Stable Diffusion version 1.4', but it does not specify concrete version numbers for ancillary software like Python, PyTorch, TensorFlow, or CUDA libraries. |
| Experiment Setup | Yes | Both implementations require two key hyperparameters: the weight λ of the gradient descent direction relative to the ascent direction, and the loss truncation value α... Detailed hyperparameter configurations are provided in Appendix C. ... For experiments on CIFAR-10, we implemented our method using hyperparameters α = 1 10 1 and λ = 5. Our EDM implementation used a batch size of 64, a duration parameter of 0.05, and a learning rate of 1e-5. |