Instructions to use cvnberk/cat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cvnberk/cat with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cvnberk/cat", dtype=torch.bfloat16, device_map="cuda") prompt = "photo of a <new1> cat" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 238e13af1fc9b9505b33441fe72fc8d91428c90318ce34cd56b8e9c1682a5866
- Size of remote file:
- 76.7 MB
- SHA256:
- a0229ab530c0c795af51a25914ee45fc26b38f27aeb2e68625e957302719af53
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