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:
- 24d9a0e5791382a212686854fb0f839fee3d148fc90edc367c89ee014c39ca48
- Size of remote file:
- 427 kB
- SHA256:
- 94fd932e5fe902898f26773d60bf4ce582e2eeb2efe2893c305a6b3d6fc11b5c
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