Image-to-Image
Diffusers
Safetensors
Core ML
StableDiffusionInpaintPipeline
clover-image
inpainting
stable-diffusion
Instructions to use neonforestmist/Clover-Image-Tiny-Inpaint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use neonforestmist/Clover-Image-Tiny-Inpaint 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("neonforestmist/Clover-Image-Tiny-Inpaint", 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:
- 491f92972d1e30f5806dfcd209d7de40ccc1f48d2195a053615ecc3414cc809e
- Size of remote file:
- 548 kB
- SHA256:
- 80711e22fa8cb4fadd1e34e3ac8f8559a16d4775fcc377b7d76f5752ff0d68ff
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.