Instructions to use GD-ML/FLUX-Text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use GD-ML/FLUX-Text 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("GD-ML/FLUX-Text", dtype=torch.bfloat16, device_map="cuda") 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

- Xet hash:
- ab20c857e2d03b49190630f801e98696280c3ccb09e961a32822c4a3359fa5e2
- Size of remote file:
- 2.68 MB
- SHA256:
- 4205b11706d91ed4824ebd11806232e5531be9f8b3ecb9208ce3e6ad019c8a81
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.