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Editing Implicit Assumptions in Text-to-Image Diffusion Models

Hadas Orgad*, Bahjat Kawar*, and Yonatan Belinkov, Technion. * Equal Contribution.

We introduce TIME (Text-to-Image Model Editing), a method for editing implicit assumptions in text-to-image diffusion models.

New: Check out the Gradio demo and edit text-to-image models from your browser!

Dependencies Setup

This repo was tested with PyTorch 1.13.1, CUDA 11.6.2, Numpy 1.23.4, and Diffusers 0.9.0.

An example environment is given in environment.yml.

Running the Experiments

The general command to apply TIME and see results:

python apply_time.py {--with_to_k} {--with_augs} --train_func {TRAIN_FUNC} --lamb {LAMBDA} --save_dir {SAVE_DIR} --dataset {DATASET} --begin_idx {BEGIN} --end_idx {END} --num_seeds {SEEDS}

where the following are options

--with_to_k whether to edit the key projection matrix along with the value projection matrix.
--with_augs whether to apply textual augmentations for editing.
TRAIN_FUNC the name of the editing function to use (train_closed_form or baseline).
LAMBDA the regularization hyperparameter to be used in train_closed_form (default: 0.1).
SAVE_DIR the directory name to save into.
DATASET the dataset csv file name (default: TIMED_test_set_filtered_SD14.csv).
BEGIN the index to begin from in the dataset (inclusive).
END the index to end on in the dataset (exclusive).
SEEDS the number of seeds to generate images for in each prompt.

For example, for applying the main experiment on TIMED presented in the paper:

python apply_time.py --with_to_k --with_augs --train_func train_closed_form --lamb 0.1 --save_dir results --begin_idx 0 --end_idx 104 --num_seeds 24

References and Acknowledgements

@article{orgad2023editing,
    title={Editing Implicit Assumptions in Text-to-Image Diffusion Models},
    author={Orgad, Hadas and Kawar, Bahjat and Belinkov, Yonatan},
    journal={arXiv:2303.08084},
    year={2023}
}

This implementation is inspired by: