Instructions to use GreeneryScenery/SheepsControlV9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use GreeneryScenery/SheepsControlV9 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("GreeneryScenery/SheepsControlV9", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download checkpoint-36000/scheduler.bin from GreeneryScenery/SheepsControlV9: direct link, hf CLI and curl.
- Browser
- Download file 563 Bytes
-
https://huggingface.co/GreeneryScenery/SheepsControlV9/resolve/main/checkpoint-36000/scheduler.bin
- Command line
-
hf download hf://GreeneryScenery/SheepsControlV9/checkpoint-36000/scheduler.bin
-
curl -L -o scheduler.bin https://huggingface.co/GreeneryScenery/SheepsControlV9/resolve/main/checkpoint-36000/scheduler.bin
563 Bytes
- Xet hash:
- aaadc707b1706137fbd35a830b03d6b4989a744fed9857504664de84213c33b1
- Size of remote file:
- 563 Bytes
- SHA256:
- 78be0278ba019b0cde11e6eacc9212d400de983c2b3e2dae8bb03b8f7fab4395
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.