Instructions to use LeonardoBenitez/demo-vision-unlearning-uce with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use LeonardoBenitez/demo-vision-unlearning-uce with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("LeonardoBenitez/demo-vision-unlearning-uce", torch_dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Evaluation results
- ForgetSet clip score of original model mean (~↑) on Forget setself-reported31.528
- ForgetSet clip score of original model std (~↓) on Forget setself-reported3.217
- ForgetSet clip score of unlearned model mean (↓) on Forget setself-reported25.646
- ForgetSet clip score of unlearned model std (~↓) on Forget setself-reported2.659
- ForgetSet clip score difference between original and unlearned mean (↑) on Forget setself-reported5.883
- ForgetSet clip score difference between original and unlearned std (~↓) on Forget setself-reported5.636
- RetainSet clip score of original model mean (~↑) on Forget setself-reported30.831
- RetainSet clip score of original model std (~↓) on Forget setself-reported2.130