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
Improve model card: Add pipeline tag, library name, GitHub link, and descriptive tags
#1
by nielsr HF Staff - opened
This PR enhances the model card for FLUX-Text by:
- Adding the
pipeline_tag: image-to-imageto improve discoverability for scene text editing models on the Hugging Face Hub. - Specifying
library_name: diffusersto enable the "Use in Diffusers" widget and provide a standard way to interact with the model within the Hugging Face ecosystem. - Including relevant
tagssuch astext-editing,multilingual,diffusion-transformer, anddiffusion-modelfor more detailed categorization. - Adding a direct link to the GitHub repository as a badge for easier access to the code.
- Updating the main title in the markdown content to the full paper title for better clarity.
The existing ArXiv link to the paper is preserved.
rrui changed pull request status to merged