Instructions to use GreeneryScenery/SheepsControlV2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use GreeneryScenery/SheepsControlV2 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/SheepsControlV2", 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
metadata
datasets:
- GreeneryScenery/SheepsNet
pipeline_tag: image-to-image
tags:
- art
- ControlNet
V2
3 epochs 🤗.
Much room for improvement.
Examples:
Conditional image:

Images:
A bull:
A chicken:
A cow with background removed, 8k:
A donkey:
A goat:
A realistic horse on a field with background removed, 8k:
A realistic horse on ice with background removed, 8k:
A realistic horse with background removed, 8k:
A realistic sheep on ice with background removed, 8k:
A sheep facing left:
A tiger:
