Instructions to use Akaneqwq/DualDiff with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Akaneqwq/DualDiff 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("Akaneqwq/DualDiff", 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.pkl from Akaneqwq/DualDiff: direct link, hf CLI and curl.
- Browser
- Download file 3.47 GB
-
https://huggingface.co/Akaneqwq/DualDiff/resolve/main/model.pkl
- Command line
-
hf download hf://Akaneqwq/DualDiff/model.pkl
-
curl -L -o model.pkl https://huggingface.co/Akaneqwq/DualDiff/resolve/main/model.pkl
3.47 GB
- Xet hash:
- 11bf7f99ca157e85ddce9246790f272863dfba7eedc610097fd07dc9577a8dd5
- Size of remote file:
- 3.47 GB
- SHA256:
- 485ce47f2c2f18bfb54ea1ffc1908fce12e7ef1d98cc4e0f7e39afe3d01cf248
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.