Instructions to use dekes1/cindycfr2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dekes1/cindycfr2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("krea/Krea-2-Raw", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("dekes1/cindycfr2") prompt = "A high-fashion editorial shot of a woman cindycfr2 wearing a neon holographic gown, standing amidst a futuristic cyberpunk Tokyo street with rain-slicked pavement and glowing signs." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 30d21ddd69423a17cf31430ef9a78698c4587f4c8b129afdd1b1244a356eec94
- Size of remote file:
- 195 MB
- SHA256:
- 65d5f16567dc4d31ef510694f4c8a871e8bb9eb4f92bb2944a6508d4e03170f6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.