Instructions to use SidXXD/encoder_attack_2-eps-5000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SidXXD/encoder_attack_2-eps-5000 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SidXXD/encoder_attack_2-eps-5000", dtype=torch.bfloat16, device_map="cuda") prompt = "photo of a <v1*> cat" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- ee2c67910d8ad361ffaf633dcaf2264099a8798ebdb65b603667de3df1275638
- Size of remote file:
- 102 MB
- SHA256:
- 6e6730314b79a4787c9b22fa30c803724a635d2e2d7df56d69d0297ac00eb35c
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