Instructions to use ckpt/controlavideo-hed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ckpt/controlavideo-hed with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ckpt/controlavideo-hed", 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:
- 708be0125b94651809eeb0e531c96c88824f70b8949b65ad7f111fcf06c1137e
- Size of remote file:
- 1.32 GB
- SHA256:
- a089692bb820852baf8990107e1fb6893530f0f54750d395a93ceb866d0658bb
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