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Check out the documentation for more information.
Stable Diffusion Extraction Utilities
Research code for controlled, authorized memorization and training-data extraction experiments on publicly released Stable Diffusion checkpoints and datasets.
The repository contains:
extract.py: baseline multi-seed prompt extraction and clique filtering.pia_extract.py: rho/PIA-guided DDIM extraction with optional branching.reference_lpips.py: ranking generated images against paired reference images.prepare_naruto_blip.py: converts a Hugging Face image-caption dataset into prompt and reference-image files.sd_pipeline.py,components.py,stable_attack.py: PIA/rho sampler implementation.
No model weights, datasets, generated images, credentials, or caches are included.
Basic extraction
python extract.py \
--model /path/to/diffusers-model \
--prompts prompts.json \
--output output \
--num_images 500 \
--batch_size 32 \
--steps 50
Use --start_prompt and --max_prompts to shard a run across GPUs.
PIA/rho extraction
Run a small diagnostic first and inspect guidance_diagnostics.jsonl before scaling up. Tune pia_scale, shift, and temperature from the observed rho and injection-to-epsilon ratio rather than assuming a universal value.
All experiments should use models and datasets for which the operator has permission to run the evaluation.