Instructions to use lavinal712/controlnet-segmentation-sam-llava with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lavinal712/controlnet-segmentation-sam-llava 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("lavinal712/controlnet-segmentation-sam-llava", 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
- Xet hash:
- e2ff897f0790306d66f2b71b0ce756b7ea803f153ebe079692489fa62a020061
- Size of remote file:
- 1.45 GB
- SHA256:
- 792c2bb38530d9b86c225e52447977fbcb6281ac304dd63e3ae084fd2caeefba
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