Instructions to use HighCWu/Jojo_lora_4bit_training_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use HighCWu/Jojo_lora_4bit_training_v2 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_v2") 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 Settings
- Draw Things
Download example-5.png from HighCWu/Jojo_lora_4bit_training_v2: direct link, hf CLI and curl.
- Browser
- Download file 4.07 MB
-
https://huggingface.co/HighCWu/Jojo_lora_4bit_training_v2/resolve/main/example-5.png
- Command line
-
hf download hf://HighCWu/Jojo_lora_4bit_training_v2/example-5.png
-
curl -L -o example-5.png https://huggingface.co/HighCWu/Jojo_lora_4bit_training_v2/resolve/main/example-5.png
4.07 MB

- Xet hash:
- 04f65b71a5b4bce996433aaf5eae52c3aed2961f4eb93983ca0232bbac516804
- Size of remote file:
- 4.07 MB
- SHA256:
- a4b0cdaa5922cec4837bc9ed7cc8cedeeed63b86a38a6cddfa823d10b92291f6
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