Instructions to use Owen777/Kontext-Style-Loras with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Owen777/Kontext-Style-Loras 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("Owen777/Kontext-Style-Loras", 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
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This repository provides a collection of style LoRA adapters for the FLUX.1 Kontext Model, enabling a wide range of artistic and cartoon styles for high-quality image-to-image generation.
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These LoRAs are trained on high-quality paired data generated by GPT-4o.
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The data is from [Omniconsistency](https://huggingface.co/datasets/showlab/OmniConsistency).
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Contributor: Tian YE&Song FEI, HKUST Guangzhou.
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This repository provides a collection of style LoRA adapters for the FLUX.1 Kontext Model, enabling a wide range of artistic and cartoon styles for high-quality image-to-image generation.
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These LoRAs are trained on high-quality paired data generated by GPT-4o.
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The data is from [Omniconsistency](https://huggingface.co/datasets/showlab/OmniConsistency).
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Contributor: Tian YE&Song FEI, HKUST Guangzhou.
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