Instructions to use GreeneryScenery/SheepsControlV1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use GreeneryScenery/SheepsControlV1 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/SheepsControlV1", torch_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
| datasets: | |
| - GreeneryScenery/SheepsNet | |
| pipeline_tag: image-to-image | |
| tags: | |
| - ControlNet | |
| - art | |
| # V1 | |
| First try at training a custom [ControlNet](https://github.com/huggingface/diffusers/tree/main/examples/controlnet). (Only 1 epoch 🤗) Using dataset from [here](https://huggingface.co/datasets/GreeneryScenery/SheepsNet). | |
| Follow [this](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/controlnet) to use (u sure ya wanna use?). | |
| Things to improve: | |
| - More variety of data in general? (Not only sheeps) | |
| - More data (More sheeps) | |
| - More epochs | |
| - Better text prompts | |
| ## Example: | |
| Prompt: Lamb | |
| Conditioning image: | |
| <img src = 'https://huggingface.co/GreeneryScenery/SheepsControl/resolve/main/example_input.png' style = 'width: 256px'> | |
| Image: | |
| <img src = 'https://huggingface.co/GreeneryScenery/SheepsControl/resolve/main/example_output.png' style = 'width: 256px'> |