Instructions to use ashawkey/controlnet_depth_aware_inpaint_v11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ashawkey/controlnet_depth_aware_inpaint_v11 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ashawkey/controlnet_depth_aware_inpaint_v11", 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:
- 49bebeb2d91d862be9535aa0756075bd2a98e0260508c424afe2e92a3c79732a
- Size of remote file:
- 723 MB
- SHA256:
- 0e9d08822c54034f7056ec7b4e437f1058e8086fc04425e1b1957e4cca638584
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