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 tokenizer.json from xing0916/DDB_Edit: direct link, hf CLI and curl.
- Browser
- Download file 11.3 MB
-
https://huggingface.co/xing0916/DDB_Edit/resolve/main/tokenizer.json
- Command line
-
hf download hf://xing0916/DDB_Edit/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/xing0916/DDB_Edit/resolve/main/tokenizer.json
11.3 MB
- Xet hash:
- 8dd75860c1f47b6660792eb2b2f751649e6aa2b5713cfec4296677eb2326877f
- Size of remote file:
- 11.3 MB
- SHA256:
- d1e30dd1588d25682e78c74a57bc775173cc34f85f991385602812a2d498fc7d
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