Forget All That Should Be Forgotten: Separable, Recoverable, and Sustainable Multi-Concept Erasure from Diffusion Models

Issues:

• Concept restoration: The agreement breakdown between concept owners and DM owners may be temporary, and DM owners need to recover these forgotten concepts after regaining their copyrights.
• Multi-concept erasure: Current erasure procedures are confined to single-concept elimination and pose challenges when extending them to multi-concept erasure. Multi-concept erasure can take two forms: simultaneous erasure of multiple concepts and iterative concept erasure . The former encounters memory overload, while both forms involve interactions between fine-tuned weights for erasing various concepts.
• Model performance preservation: Prior efforts focus on concept erasure, leading to a considerable performance degradation in the overall generative capability of DMs. Particularly, they may destroy model watermarks, i.e., watermarks triggered by pre-defined prompts for text-guided DMs.

We propose

• Weight Decoupling;
• Concept-irrelevant Unlearning;
• Optimization Decoulping.

In this project, we propose a novel Separable, Recoverable, and Sustainable Multi-concept Eraser (SRS-ME), enabling diffusion models to forget all concepts that they should forget without necessitating retraining from scratch. Specifically, through theoretical analysis, we introduce the paradigm of weight decoupling for constructing separable weight shifts, which can decouple interactions among weight shifts targeting diverse concepts. This approach also provides flexibility in both erasing and recovering arbitrary concepts while preserving model watermarks. To effectively erase inappropriate concepts and preserve model performance on regular concepts, we design an innovative concept-irrelevant unlearning optimization process. By defining concept representations, this process introduces the concept correlation loss and the momentum statistic-based stopping condition. Besides, to reduce memory usage, we demonstrate the feasibility of optimization decoupling for separated weight shifts. Benchmarked against prior work, extensive experiments demonstrate

• the flexibility of our SRS-ME in concept manipulation,
• as well as its efficacy in preserving model performance
• and reducing memory consumption.

Fine-tuned Weights

We do not offer the fine-tuned weights for download, as the training process is faster.

Installation Guide

• To get started clone the following repository of Original Stable Diffusion Link
• Then replace the files from our repository (ldm-replace.txt) to stable-diffusion main directory of stable diffusion.
• Download the weights from here and move them to stable-diffusion/models/ldm/
• [Only for training] To convert your trained models to diffusers download the diffusers Unet config from here

Training Guide

After installation, follow these instructions to train a machine unlearning model:

1. Generate data and then put these samples to ./data/train/{0,1,2,3,4,5,6,7,8,9} or ./data/eval/{0,1,2,3,4,5,6,7,8,9}

• python eval-scripts/generate-data.py --prompt 'Van Gogh' --model_name '0' --save_path './data/' --num_samples 1 --device 'cuda:0'

--prompt should be Van Gogh, Picasso, Cezanne, Jackson Pollock, Caravaggio, Keith Haring, Kelly McKernan, Tyler Edlin, and Kilian Eng for style unlearning.

2. Train classification model

   • python train-scripts/artist-cls-train.py --device 'cuda:0'

3. Train comparative experiments: FMN, Abconcept, Esd

   • python train-scripts/FMN.py --erase_cat 'style' --erased_index 0 --lr 1e-5 --iter_break 50 --devices '0,1'
   • python train-scripts/Abconcept.py --erase_cat 'style' --erased_index 0 --lr 1e-5 --iter_break 75 --devices '0,1'
   • python train-scripts/Esd.py --iter_break 1000 --train_method 'xattn' --erase_cat 'style' --erased_index 0 --devices '0,1'

4. Train our SRS-ME

Calculate particular solutions:

• python train-scripts/SRSME_Solution.py --erase_cat 'style' --erased_index 0 --em_indexes '1,2' --scenei 1 --timei 0 --device '0'

SRS-ME optimization:

• python train-scripts/SRS_ME.py --lr 1e-1 --scale_factor 1e-4 --threshold 1e-4 --reg_beta 1e-6 --erased_index 0 --scenei 1 --timei 0 --max_base 691 --erase_cat 'style' --devices '0,1'

Generating Images

To generate images from one of the custom models use the following instructions:

• To use eval-scripts/generate-images-merge.py you would need a csv file with columns prompt, evaluation_seed and case_number. (Sample data in data/)
• evaluate.csv: We randomly select 50 seeds for each style and generate 5 images (num_samples) using trained DMs per seed.

For instance, select 0, 1, 2 model weights in generate-images-merge.py, and yield 012-merge

• python eval-scripts/generate-images-merge.py --prompts_path './data/style_classify.csv' --save_path 'evaluation_folder' --num_samples 5 --device 'cuda:0'

To obtain the quantitative results:

Rename the dir_name of generated images to eval, cate-cls-test.py for object, artist-cls-test.py for artist style

• python train-scripts/move_image.py
• python train-scripts/cate-cls-test.py

The default contains the images generated by the original diffusion model; The eval contains the images generated by the unlearned diffusion model

• python eval-scripts/lpips_eval.py --path1 './evaluation_folder/ori-merge' --path2 './evaluation_folder/012-merg'

  Citing our work

  If you find our work valuable, please consider citing the preprint using the following format: