Instructions to use SidXXD/encoder_attack_1-eps-5000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SidXXD/encoder_attack_1-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_1-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:
- 8b0c363b1f113211362c886dfaa649ecf5eb7b7aaaea6a762ec5eb7a8906a20a
- Size of remote file:
- 102 MB
- SHA256:
- fcab7a815ca1b54db18ab1dd8b8d4dcbfa858c571c66b2dc5c3bed94b01296e6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.