Instructions to use Kontext-Style/American_Cartoon_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Kontext-Style/American_Cartoon_lora 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("black-forest-labs/FLUX.1-Kontext-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Kontext-Style/American_Cartoon_lora") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
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This repository provides the **American Cartoon** style LoRA adapter for the [FLUX.1 Kontext Model](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev).
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This LoRA is part of a collection of 20+ style LoRAs trained on high-quality paired data generated by GPT-4o from the [OmniConsistency](https://huggingface.co/datasets/showlab/OmniConsistency) dataset.
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Contributor: Tian YE & Song FEI, HKUST Guangzhou.
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## Style Showcase
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This repository provides the **American Cartoon** style LoRA adapter for the [FLUX.1 Kontext Model](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev).
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This LoRA is part of a collection of 20+ style LoRAs trained on high-quality paired data generated by GPT-4o from the [OmniConsistency](https://huggingface.co/datasets/showlab/OmniConsistency) dataset.
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Contributor: Tian YE & Song FEI, HKUST Guangzhou.
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## Style Showcase
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