Skip to content

Latest commit

 

History

History
79 lines (57 loc) · 4.06 KB

README.md

File metadata and controls

79 lines (57 loc) · 4.06 KB
Raw

Saliency-Unlearning for SD

This is the official repository for Saliency Unlearning for stable diffusion. The code structure of this project is adapted from the ESD codebase.

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 SD/models/ldm/
  • [Only for training] To convert your trained models to diffusers download the diffusers Unet config from here

Unlearned Weights

The unlearned weights for NSFW and object forgetting are available here.

Forgetting Training with Saliency-Unlearning

  1. First, we need to generate saliency map for unlearning.

     python train-scripts/generate_mask.py --ckpt_path 'models/ldm/stable-diffusion-v1/sd-v1-4-full-ema.ckpt' --classes {label} --device '0'

    This will save saliency map in SD/mask/{label}.

  2. Forgetting training with Saliency-Unlearning

     python train-scripts/random_label.py --train_method full --alpha 0.5 --lr 1e-5 --epochs 5  --class_to_forget {label} --mask_path 'mask/{label}/with_0.5.pt' --device '0'

    This should create another folder in SD/model.

    You can experiment with forgetting different class labels using the --class_to_forget flag, but we will consider forgetting the 0 (tench) class here.

  3. Forgetting training with ESD

    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'

Generating Images

  1. To use eval-scripts/generate-images.py you would need a csv file with columns prompt, evaluation_seed and case_number. (Sample data in data/)
  2. To generate multiple images per prompt use the argument num_samples. It is default to 10.
  3. The path to model can be customised in the script.
  4. It is to be noted that the current version requires the model to be in saved in SD/model/compvis-<based on hyperparameters>/diffusers-<based on hyperparameters>.pt 
    python eval-scripts/generate-images.py --prompts_path 'prompts/imagenette.csv' --save_path 'evaluation_folder/ --model_name {model} --device 'cuda:0'

Evaluation

  1. FID

    • First,we need to select some images from Imagenette as real images.
    • Then, we can compute FID between real images and generated images. 
      python eval-scripts/compute-fid.py --folder_path {images_path}

  2. Accuracy

    python eval-scripts/imageclassify.py --prompts_path 'prompts/imagenette.csv' --folder_path {images_path}

NSFW-concept removal with Saliency-Unlearning

  1. To remove NSFW-concept, we initially utilize SD V1.4 to generate 800 images as Df with the prompt "a photo of a nude person" and store them in "SD/data/nsfw". Additionally, we generate another 800 images designated as Dr using the prompt "a photo of a person wearing clothes" and store them in "SD/data/not-nsfw".

  2. Next, we need to generate saliency map for NSFW-concept.

    python train-scripts/generate_mask.py --ckpt_path 'models/ldm/stable-diffusion-v1/sd-v1-4-full-ema.ckpt' --nsfw True --device '0'

    This will save saliency map in SD/mask.

  3. Forgetting training with Saliency-Unlearning

    python train-scripts/nsfw_removal.py --train_method 'full' --mask_path 'mask/nude_0.5.pt' --device '0'