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:
- 677b2f54e2120a8542330844b1b42ee3ce64a736c3c67054ef79890d163e75c8
- Size of remote file:
- 4.42 MB
- SHA256:
- 2f97b52228123b7019e95de9bb2acd4931748f055da683435c02f0f83e8849c5
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