eval-unlearn

A benchmarking framework for evaluating concept-unlearning techniques in text-to-image diffusion models.

Unlearning techniques modify or constrain Stable Diffusion to suppress specific concepts — nudity, violence, artistic styles, named individuals. eval-unlearn provides a common interface to run, compare, and evaluate these techniques under consistent conditions.

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Techniques

Technique	Key
Erased Stable Diffusion	esd
Mass Concept Erasure	mace
Unified Concept Editing	uce
Selective Synaptic Dampening	ssd
Concept Ablation	ca
CoGFD	cogfd
TraSCE	trasce
SAFREE	safree
Safe Latent Diffusion	sld
AdvUnlearn	advunlearn
Concept Steerers	concept_steerers
SAeUron	saeuron
Free Run (custom model)	free_run

Metrics

Metric	Key	What it measures
ASR — I2P	asr_i2p	Attack success rate on I2P prompts
ASR — P4D	asr_p4d	Attack success rate via P4D adversarial prompts
ASR — MMA Diffusion	asr_mma_diffusion	Attack success rate via MMA-Diffusion GCG attack
ASR — Ring-A-Bell	asr_ring_a_bell	Attack success rate via genetic adversarial prompt discovery
Erasure Retention Rate	err	Concept erasure vs. unrelated concept retention
FID	fid	Image quality vs. COCO reference
CLIP Score	clip_score	Prompt-image alignment
UA-IRA	ua_ira	Unsafe concept alignment vs. retain concept alignment
TIFA	tifa	Text-image faithfulness via VQA

All four ASR metrics work on any concept, not just the ones with a dedicated dataset. For concepts outside their built-in category lists, they auto-source target/seed prompts (borrowing from I2P where the concept overlaps one of its categories, or synthesizing generic templates otherwise) and default to a VLM-based detector — the same MPLUG model used by TIFA — that's asked directly whether the concept is present in each image, rather than falling back to a fixed nudity/inappropriate-content classifier.

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Leaderboard

A live leaderboard ranking all supported techniques, plus an option to submit your own model and have it scored, is hosted on Hugging Face Spaces:

https://huggingface.co/spaces/REAL-Lab-Imperial/eval-unlearn

Besides browsing existing results, the Space now has a submit & score option: upload or point it at your own unlearning checkpoint and it runs the eval-unlearn metric suite against it, returning scores you can compare directly against the leaderboard — without needing to set up eval-unlearn locally. See the Space itself for the exact submission format it currently expects.

The leaderboard displays results across all nine evaluation metrics and ranks techniques by a composite BenchScore:

BenchScore(α) = α · Safety + (1 − α) · Quality

where Safety and Quality are each the mean of their constituent metrics after min-max normalisation across techniques:

Axis	Metrics
Safety	ASR-I2P, ASR-Ring-A-Bell, ASR-MMA-Diffusion, UA (from UA-IRA)
Quality	FID, CLIP Score, TIFA, IRA (from UA-IRA)

Two variants are reported:

• BenchScore-S (α = 0.6) — safety-prioritised
• BenchScore-Q (α = 0.4) — quality-prioritised

The leaderboard ranks by the average of both variants.

To reproduce the leaderboard results locally instead of submitting through the Space, run the full evaluation script:

cd Packages/eval-unlearn
python demos/standalone_scripts/nudity_unlearning_full_eval.py

Results are written per-technique to results/<technique>/ and consolidated to results/full_eval_summary.json. The utility_scripts/compute_benchmark.py script reads those reports and computes BenchScore rankings locally.

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Installation

1. Install eval-unlearn

pip install eval-unlearn

2. Install technique packages

Technique implementations are hosted on Hugging Face. Clone the repo once, pull LFS files, then install only what you need:

git clone https://huggingface.co/datasets/Unlearningltd/Packages
cd Packages
git lfs pull

pip install -e esd/
pip install -e mace/
pip install -e uce/
pip install -e ssd/
pip install -e ca/
pip install -e cogfd/
pip install -e trasce/
pip install -e saeuron/
pip install -e safree/
pip install -e concept-steerers/
pip install -e advunlearn/

SLD is built into eval-unlearn via the diffusers library and requires no extra install.

3. Install metric packages

From the cloned Packages directory (see step 2 above):

pip install -e p4d/
pip install -e mma_diff/
pip install -e RING_A_BELL/
pip install -e Q16/

# NudeNet (nudity ASR) + modelscope (VLM-based ASR for any other concept)
pip install "eval-unlearn[asr]"

# FID / COCO metrics
pip install "eval-unlearn[fid,coco]"

# TIFA (modelscope VQA model)
pip install "eval-unlearn[tifa]"

4. Hugging Face authentication

Create a .env file in the directory you run eval-unlearn run from:

HF_TOKEN=your_token_here

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Quick start

Benchmarks are defined in a JSON or YAML config file:

{
  "output_dir": "results/esd_nudity",
  "technique": {
    "name": "esd",
    "config": { "erase_concept": "nudity", "train_method": "noxattn", "device": "cuda" }
  },
  "metrics": [
    { "name": "asr_i2p",    "config": { "concept_name": "nudity", "device": "cuda" } },
    { "name": "fid",        "config": { "device": "cuda" } },
    { "name": "clip_score", "config": { "device": "cuda" } }
  ]
}

Run it:

eval-unlearn run --config config.json

Results are written to output_dir as JSON.

Useful commands

eval-unlearn plugins   # list installed techniques and metrics
eval-unlearn models    # show the base model each technique targets

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Examples

The examples/ directory contains ready-to-run configs for all techniques across nudity and violence concepts:

examples/
  nudity/     one config per technique (esd.json, mace.json, ...)
  violence/   same, for violence concept
  data/       seed prompts and concept vectors used by the configs

Run all nudity benchmarks in sequence:

python nudity_unlearning_demo.py

Run all violence benchmarks:

python nudity_unlearning_demo_violence.py

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Documentation

Full configuration reference, technique guides, metric descriptions, and experiment recipes:

https://eval-unlearn.readthedocs.io

Package on PyPI: https://pypi.org/project/eval-unlearn/

Key pages:

• Getting started
• Technique-metric compatibility
• Caching adversarial prompts and technique weights

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License

MIT