Instructions to use dekes1/cindycfr9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dekes1/cindycfr9 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/cindycfr9") prompt = "A futuristic cyberpunk cyborg cat lounging on a neon-lit skyscraper ledge overlooking a rainy Tokyo cityscape, cindycfr9, hyper-realistic digital art." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 83625fbad85e1c6e97c5ce1897c5efa7738ac3adc290a44bcebebe68c7b3622b
- Size of remote file:
- 195 MB
- SHA256:
- 6276a2b1759b0f6aa26951373f003acb24de872b8736ae68381995b21f1234cb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.