Instructions to use dekes1/cindycfr14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dekes1/cindycfr14 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/cindycfr14") prompt = "A majestic snow leopard leaping across a jagged Himalayan cliffside during a swirling blizzard, captured in hyper-realistic detail, cindycfr11" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- e8859b47f925bc53b024c02c9272fdb566108de85a37c4d5a849893d68fbc966
- Size of remote file:
- 195 MB
- SHA256:
- 804ce154cb04218f75646a876a9ab00daf95e66398a64ce648ebfe8a604b4dad
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.