Instructions to use Kontext-Style/Pixel_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Kontext-Style/Pixel_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/Pixel_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
- Xet hash:
- dec4ff7cc3d53cefa7c6a70e067b4ec4a7dcbcd5d042fffbced3dc4dd4f7b18a
- Size of remote file:
- 3.72 MB
- SHA256:
- 10121e34986c86953cfda9daafdc8a8e7024056dfa19a04d84ba853850e72c73
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.