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 model_index.json from xing0916/DDB_Edit: direct link, hf CLI and curl.
- Browser
- Download file 203 Bytes
-
https://huggingface.co/xing0916/DDB_Edit/resolve/main/model_index.json
- Command line
-
hf download hf://xing0916/DDB_Edit/model_index.json
-
curl -L -o model_index.json https://huggingface.co/xing0916/DDB_Edit/resolve/main/model_index.json
203 Bytes
| { | |
| "_class_name": "LuminaDiMOOPipeline", | |
| "_diffusers_version": "0.36.0.dev0", | |
| "tokenizer": [ | |
| "transformers", | |
| "PreTrainedTokenizerFast" | |
| ], | |
| "vqvae": [ | |
| "diffusers", | |
| "VQModel" | |
| ] | |
| } |