Image-to-Image
Diffusers
StableDiffusionInstructPix2PixPipeline
stable-diffusion
stable-diffusion-diffusers
art
Instructions to use AnalogMutations/cartoonizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AnalogMutations/cartoonizer 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("AnalogMutations/cartoonizer", 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:
- 182d0f147e87f8383f3802f33165af9d9ec4993223cef79eeb37ada0d4ea15e5
- Size of remote file:
- 335 MB
- SHA256:
- 6723bacd3c60b11a2b4e6007338a54c6964c210116c3ccecb3bfc80e218afc8f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.