Instructions to use TianyuLin/CARE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use TianyuLin/CARE with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("TianyuLin/CARE", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 55dd969459889ba07c76f22505ea9598e51bd6b8e7ceed75c88f1edd4c115d66
- Size of remote file:
- 335 MB
- SHA256:
- 486cfc9b0b23ccde844e3008ecb9a3e542c3d426af2133aab11dd324c656e7af
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