Instructions to use Boogu/Boogu-Image-0.1-Edit-Turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Boogu/Boogu-Image-0.1-Edit-Turbo 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("Boogu/Boogu-Image-0.1-Edit-Turbo", 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
Add pipeline tag to metadata
#5
by nielsr HF Staff - opened
This PR adds the pipeline_tag: image-to-image to the model card's YAML metadata. This ensures that the model is correctly categorized on the Hugging Face Hub under the Image-to-Image pipeline, making it easier for users to discover.
Boogu-AI changed pull request status to merged