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
Download generation_config.json from xing0916/DDB_Edit: direct link, hf CLI and curl.
- Browser
- Download file 121 Bytes
-
https://huggingface.co/xing0916/DDB_Edit/resolve/main/generation_config.json
- Command line
-
hf download hf://xing0916/DDB_Edit/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/xing0916/DDB_Edit/resolve/main/generation_config.json
121 Bytes
| { | |
| "_from_model_config": true, | |
| "bos_token_id": 126080, | |
| "eos_token_id": 126081, | |
| "transformers_version": "4.46.2" | |
| } | |