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-1.png from HighCWu/Jojo_lora_4bit_training_v1: direct link, hf CLI and curl.
- Browser
- Download file 4.28 MB
-
https://huggingface.co/HighCWu/Jojo_lora_4bit_training_v1/resolve/main/example-1.png
- Command line
-
hf download hf://HighCWu/Jojo_lora_4bit_training_v1/example-1.png
-
curl -L -o example-1.png https://huggingface.co/HighCWu/Jojo_lora_4bit_training_v1/resolve/main/example-1.png
4.28 MB

- Xet hash:
- 48843e7210f189224a6e3aac15c78ae62138943d35b3600609ffe285703b049c
- Size of remote file:
- 4.28 MB
- SHA256:
- 001a4fb255beda59ba546f9b98f2fc5ad4e4765861bef299efe7fa55c81be566
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