Instructions to use ghoskno/Color-Canny-Controlnet-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ghoskno/Color-Canny-Controlnet-model 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("ghoskno/Color-Canny-Controlnet-model", 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
Create README.md
Browse files
README.md
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---
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license: apache-2.0
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datasets:
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- laion/laion-art
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language:
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- en
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library_name: diffusers
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pipeline_tag: text-to-image
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---
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This is a Torch ControlNet model converted from Flax model, trained by Flax diffusers framework on 'laion-art' dataset.
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