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 t_sketch.PNG from Adapter/t2iadapter: direct link, hf CLI and curl.
- Browser
- Download file 2.86 MB
-
https://huggingface.co/Adapter/t2iadapter/resolve/main/t_sketch.PNG
- Command line
-
hf download hf://Adapter/t2iadapter/t_sketch.PNG
-
curl -L -o t_sketch.PNG https://huggingface.co/Adapter/t2iadapter/resolve/main/t_sketch.PNG
2.86 MB
- Xet hash:
- f2b18b05f98fa3af0cf5fb50044bc7ce5e668929920bc0e7a97b21a16f9d241a
- Size of remote file:
- 2.86 MB
- SHA256:
- 581af6f63e7325860be3e1c77adda8bad45416b1d3f13c7a14a09294d0c0410d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.