Instructions to use HighCWu/Jojo_lora_4bit_training_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use HighCWu/Jojo_lora_4bit_training_v1 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,HighCWu/FLUX.1-Kontext-dev-bnb-hqq-4bit", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("HighCWu/Jojo_lora_4bit_training_v1") 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
- Local Apps
- Draw Things

- Xet hash:
- 11efa75023593e3a645e9ee7690181990723af4c74a44cb9cea32686205589db
- Size of remote file:
- 3.16 MB
- SHA256:
- edfd96fd78b2189c57e8e7be234ee9b1be8effb9754a679658cc9b6e5372fb33
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