Instructions to use Adapter/t2iadapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Adapter/t2iadapter 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("Adapter/t2iadapter", 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 figs_SDXLV1.0/teaser.PNG from Adapter/t2iadapter: direct link, hf CLI and curl.
- Browser
- Download file 3.51 MB
-
https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/teaser.PNG
- Command line
-
hf download hf://Adapter/t2iadapter/figs_SDXLV1.0/teaser.PNG
-
curl -L -o teaser.PNG https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/teaser.PNG
3.51 MB
- Xet hash:
- afb8afff9bae327f7373422001a30704c5dc0cf1b9fde16a3f4596d320215139
- Size of remote file:
- 3.51 MB
- SHA256:
- f16852fab80e93eb01e216a8fc0f6e0e4cade00118fcab8722752a06748823a7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.