Erasing Concepts from Diffusion Models
Motivated by recent advancements in text-to-image diffusion, we study erasure of specific concepts from the model's weights. While Stable Diffusion has shown promise in producing explicit or realistic artwork, it has raised concerns regarding its potential for misuse. We propose a fine-tuning method that can erase a visual concept from a pre-trained diffusion model, given only the name of the style and using negative guidance as a teacher. We benchmark our method against previous approaches that remove sexually explicit content and demonstrate its effectiveness, performing on par with Safe Latent Diffusion and censored training.
To evaluate artistic style removal, we conduct experiments erasing five modern artists from the network and conduct a user study to assess the human perception of the removed styles. Unlike previous methods, our approach can remove concepts from a diffusion model permanently rather than modifying the output at the inference time, so it cannot be circumvented even if a user has access to model weights
Given only a short text description of an undesired visual concept and no additional data, our method fine-tunes model weights to erase the targeted concept. Our method can avoid NSFW content, stop imitation of a specific artist's style, or even erase a whole object class from model output, while preserving the model's behavior and capabilities on other topics.
Fine-tuned Weights
The finetuned weights for both NSFW and art style erasures are available on our
project page.
Running Gradio Demo Locally
To run the gradio interactive demo locally, clone the files from
demo repository
- Create an environment using the packages included in the requirements.txt file
- Run
python app.py
- Open the application in browser at
http://127.0.0.1:7860/
- Train, evaluate, and save models using our method
Installation Guide
- To get started clone the following repository of Original Stable Diffusion Link
- Then download the files from our repository to
stable-diffusion main directory of stable diffusion. This would replace the ldm folder of the original repo with our custom ldm directory
- Download the weights from here and move them to
stable-diffusion/models/ldm/ (This will be ckpt_path variable in train-scripts/train-esd.py)
- [Only for training] To convert your trained models to diffusers download the diffusers Unet config from here (This will be
diffusers_config_path variable in train-scripts/train-esd.py)
Training Guide
After installation, follow these instructions to train a custom ESD model:
cd stable-diffusion to the main repository of stable-diffusion- [IMPORTANT] Edit
train-script/train-esd.py and change the default argparser values according to your convenience (especially the config paths)
- To choose train_method, pick from following
'xattn','noxattn', 'selfattn', 'full'
python train-scripts/train-esd.py --prompt 'your prompt' --train_method 'your choice of training' --devices '0,1'
Note that the default argparser values must be changed!
The optimization process for erasing undesired visual concepts from pre-trained diffusion model weights involves using a short text description of the concept as guidance. The ESD model is fine-tuned with the conditioned and unconditioned scores obtained from frozen SD model to guide the output away from the concept being erased. The model learns from it's own knowledge to steer the diffusion process away from the undesired concept.
