Text-to-Image
Diffusers
Safetensors
English
StableDiffusionPipeline
diffusion
concept-erasure
stable-diffusion
uce
cassette_player
Instructions to use DiffusionConceptErasure/uce_cassette_player with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use DiffusionConceptErasure/uce_cassette_player with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("DiffusionConceptErasure/uce_cassette_player", 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
- Draw Things
- DiffusionBee
uce_cassette_player
This is a concept-erased Stable Diffusion model using the Unlearning via Concept Erasure (UCE) method to remove the concept "Cassette Player".
Method
Unlearning via Concept Erasure (UCE) modifies the model's internal representations to remove specific concept knowledge.
Usage
from diffusers import StableDiffusionPipeline
import torch
pipe = StableDiffusionPipeline.from_pretrained("ErasureResearch/uce_cassette_player", torch_dtype=torch.float16).to("cuda")
prompt = "a photo of a cassette_player"
image = pipe(prompt).images[0]
image.save("erased_cassette_player.png")
Citation
If you use this model in your research, please cite:
@article{concept_erasure_2024,
title={Concept Erasure in Diffusion Models},
author={ErasureResearch Team},
journal={Proceedings of...},
year={2024}
}
- Downloads last month
- 1