ml-research / i2p

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Inappropriate Image Prompts (I2P) Benchmark

Repository to run the I2P benchmark (https://huggingface.co/datasets/AIML-TUDA/i2p).

Currently, we evaluated or plan to evaluate the following diffusion models:

Category: "Sexual"

Model Inappropriateness probability Max. exp. inappropriateness
SD 1.4 28.5392% 86.5960% ± 11.5085%
Safe SD 1.4 (medium) 14.9731% 71.7280% ± 17.2618%
Safe SD 1.4 (strong) 4.8335% 39.3735% ± 17.5645%
Safe SD 1.4 (max) 1.6219% 17.2658% ± 9.3218%
SD 2.0 22.5027% 86.3420% ± 13.1672%
Safe SD 2.0 (strong) 3.7809% 33.5578% ± 16.5860%
SD 2.1 21.9012% 85.5753% ± 13.1386%
Safe SD 2.1 (strong) 3.3190% 29.8322% ± 16.4602%
SD-XL waiting release waiting release
IF waiting release waiting release
Paella 41.2245% 94.8870% ± 7.0821%
MultiFusion 21.6541% 80.0400% ± 14.7222%
epic-diffusion (SD) 27.7766% 88.5360% ± 11.1867%
epic-diffusion (Safe SD, strong) 4.3609% 37.5075% ± 18.1619%
cutesexyrobutts-diffusion (SD) 44.0172% 98.7588% ± 3.9108%
cutesexyrobutts-diffusion (Safe SD, strong) 17.2503% 73.9195% ± 16.0211%
cutesexyrobutts-diffusion (Safe SD, max) running running
Distill SD (not public) waiting release waiting release
DALL-E (restricted access) todo impl todo impl
Midjourney (restricted access) todo impl todo impl
AltDiffusion 27.3147% 80.6273% ± 11.2171%

Category: all

Model Inappropriateness probability Max. exp. inappropriateness
SD 1.4 37.7504% 97.0609% ± 6.2414%
Safe SD 1.4 (medium) todo run todo run
Safe SD 1.4 (strong) 11.5990% 68.8087% ± 20.7969%
Safe SD 1.4 (max) todo run todo run
SD 2.0 todo run todo run
Safe SD 2.0 (strong) todo run todo run
SD 2.1 todo run todo run
Safe SD 2.1 (strong) todo run todo run
SD-XL waiting release waiting release
IF waiting release waiting release
Paella 54.9926% 99.6653% ± 1.8500%
MultiFusion todo impl todo impl
epic-diffusion (SD) todo run todo run
epic-diffusion (Safe SD, strong) todo run todo run
cutesexyrobutts-diffusion (SD) todo run todo run
cutesexyrobutts-diffusion (Safe SD, strong) todo run todo run
Distill SD (not public) waiting release waiting release
DALL-E (restricted access) todo impl todo impl
Midjourney (restricted access) todo impl todo impl
AltDiffusion running running

Running the I2P benchmark on own text-to-image diffusion models

  1. Implement a model class with __init__(model_name=None, special_token=None, strength=None) and __call__(self, prompt, seed, scale), an example can be found in models/vision/paella.py.
  2. In eval_I2P.py adapt the dict "model_type" accordingly with your new class.
  3. Build docker with docker compose (see ./docker files):
    • add repository path to ./docker/docker-compose.yml lines 11 and 12,
    • in directory ./docker run docker-compose up -d
    • run docker exec -it i2p bash.
  4. Run python eval_I2P.py --category all --model your_model.
  5. Print results by running python results_I2P --csv=pathtocsv.csv.

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