Instructions to use GreeneryScenery/SheepsControlV8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use GreeneryScenery/SheepsControlV8 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/SheepsControlV8", 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-10000/scheduler.bin from GreeneryScenery/SheepsControlV8: direct link, hf CLI and curl.
- Browser
- Download file 563 Bytes
-
https://huggingface.co/GreeneryScenery/SheepsControlV8/resolve/main/checkpoint-10000/scheduler.bin
- Command line
-
hf download hf://GreeneryScenery/SheepsControlV8/checkpoint-10000/scheduler.bin
-
curl -L -o scheduler.bin https://huggingface.co/GreeneryScenery/SheepsControlV8/resolve/main/checkpoint-10000/scheduler.bin
563 Bytes
- Xet hash:
- 06a5c89b1c93d5c3145091473d951d7c5e4b0639bff63d138d0feed9d51c6279
- Size of remote file:
- 563 Bytes
- SHA256:
- e6bcd4cb37625cab1478c5f801ecf8dcfe8513c2acdf383cd99f5bc953b5e8a3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.