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", 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 Settings
- Draw Things
Download example-6.png from HighCWu/Jojo_lora_4bit_training_v1: direct link, hf CLI and curl.
- Browser
- Download file 3.16 MB
-
https://huggingface.co/HighCWu/Jojo_lora_4bit_training_v1/resolve/main/example-6.png
- Command line
-
hf download hf://HighCWu/Jojo_lora_4bit_training_v1/example-6.png
-
curl -L -o example-6.png https://huggingface.co/HighCWu/Jojo_lora_4bit_training_v1/resolve/main/example-6.png
3.16 MB

- Xet hash:
- 11efa75023593e3a645e9ee7690181990723af4c74a44cb9cea32686205589db
- Size of remote file:
- 3.16 MB
- SHA256:
- edfd96fd78b2189c57e8e7be234ee9b1be8effb9754a679658cc9b6e5372fb33
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.