Instructions to use ucfzl/ControlNet_Pose_CPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ucfzl/ControlNet_Pose_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_Pose_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:
- 2a57f4c3349361e4a48eef6e393d42ec77870025ab20af0dd4a79a25b2afd1ab
- Size of remote file:
- 1.45 GB
- SHA256:
- 7a1b05c3629ea10da95fa8cbde25dcf2fcc2e58310c179df28ba4db2263266c1
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