Instructions to use ucfzl/ControlNet_Lineart_CPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ucfzl/ControlNet_Lineart_CPO with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ucfzl/ControlNet_Lineart_CPO", 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:
- 44dde9ff465b855482440b73c9e0669b18de09745d6efe33092a2085bbd71386
- Size of remote file:
- 1.45 GB
- SHA256:
- e1123c9e288834cf983737d3087121236d73e33a450e54a30eab594c195e144e
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