Instructions to use ashawkey/control_v11e_sd15_depth_aware_inpaint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ashawkey/control_v11e_sd15_depth_aware_inpaint 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/control_v11e_sd15_depth_aware_inpaint", 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:
- b81e4436ff51c66cf25f1e5899b08d7598c063a6bad3ef869cf84792f5c7dd2d
- Size of remote file:
- 723 MB
- SHA256:
- 41b37d953d5fce061cac425d70299fecd488d0e00d23b39303865aca4cf67794
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.