Instructions to use xing0916/DDB_Edit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xing0916/DDB_Edit 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("xing0916/DDB_Edit", 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 metadata and links
#1
by nielsr HF Staff - opened
This PR adds the pipeline_tag and library_name metadata to make the model more discoverable on the Hub, and adds links to the paper and GitHub repository in the model card.
xing0916 changed pull request status to merged