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

- Xet hash:
- a047825fa0d55ef5fb3f5f6493fefed44fbe9eb840358280ff514ca33b580918
- Size of remote file:
- 3 MB
- SHA256:
- add230713d6b7c327b9076e2ec3ae54f8192320d3c46e1b0f9d3ad4a6b3875bf
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