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README.md

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+---
+license: creativeml-openrail-m
+base_model: runwayml/stable-diffusion-v1-5
+tags:
+- stable-diffusion
+- diffusion-models
+- machine-unlearning
+- unlearncanvas
+---
+
+# FADE UnlearnCanvas -- SalUn unlearned checkpoints
+
+Stable Diffusion checkpoints after applying **Saliency Unlearning (SalUn)**
+([SalUn reference implementation](https://github.com/OPTML-Group/Unlearn-Saliency)) to unlearn a
+single artistic style from the UnlearnCanvas full (50-style) checkpoint. One
+checkpoint per forgotten style.
+
+## Files
+
+| File | Description |
+|---|---|
+| `salun_monet.safetensors` | Monet forgotten |
+| `salun_picasso.safetensors` | Picasso forgotten |
+| `salun_van_gogh.safetensors` | Van Gogh forgotten |
+
+## Evaluation
+
+These checkpoints were evaluated with FADE against the retain-only oracle checkpoints
+(see `nike7788/fade-unlearncanvas-retain-seed{0,1,2}`) and with UnlearnCanvas's own
+accuracy/FID metrics. See the project repo for numbers and evaluation scripts.
+
+
+## About FADE
+
+FADE (Forget-Ability via Divergence Estimate) is a metric for evaluating machine
+unlearning in diffusion models: it generates images from an unlearned model and a
+retain-only oracle, then measures a variational upper bound on the KL divergence
+between the two models' output distributions on those images. See the project repo
+for the full method and evaluation code: the project's public GitHub repo (link forthcoming -- not yet published as of this checkpoint's upload)
+
+This checkpoint is a fine-tune of Stable Diffusion v1.5, built on the
+[UnlearnCanvas](https://github.com/OPTML-Group/UnlearnCanvas) dataset/benchmark
+(Zhang et al., 2024). It inherits Stable Diffusion's CreativeML OpenRAIL-M license --
+see the license file for the specific use-based restrictions that apply.
+
+## Loading
+
+```python
+import torch
+from safetensors.torch import load_file
+
+state_dict = load_file("<downloaded_file>.safetensors")
+# merge into a CompVis-format LDM model, e.g. via UnlearnCanvas's
+# ldm.util.instantiate_from_config, then:
+# model.load_state_dict(state_dict, strict=False)
+```