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 | Venue 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 EMAIL Henry Li Yale University EMAIL Zhun Wang University of California, Berkeley EMAIL Jonathan Patsenker Yale University EMAIL Jiachen T. Wang Princeton University EMAIL Qinbin Li University of California, Berkeley EMAIL Ming Jin Virginia Tech EMAIL Dawn Song University of California, Berkeley EMAIL Ruoxi Jia Virginia Tech EMAIL |
| 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. |