Instructions to use QWW/EditCLIP-IP2P with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use QWW/EditCLIP-IP2P 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("QWW/EditCLIP-IP2P", 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
- Xet hash:
- fcbc8dcba4375998862ea9f74f3e519c6057112f33b1767a932a607451c91b3d
- Size of remote file:
- 3.16 MB
- SHA256:
- cecd37525844730b0dcc0952dc8064317295ee30b5b607ea0714de0bc93c371f
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