Instructions to use Sanster/PowerPaint-V1-stable-diffusion-inpainting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Sanster/PowerPaint-V1-stable-diffusion-inpainting 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("Sanster/PowerPaint-V1-stable-diffusion-inpainting", 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
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Model from: https://huggingface.co/JunhaoZhuang/PowerPaint-v1
Based on runwayml/stable-diffusion-inpainting, the unet has been replaced with PowerPaint's unet,
and the token embedding (P_ctxt, P_shape, P_obj) newly added by PowerPaint has been integrated into the text_encoder.
Download python file at here, then run:
python3 demo.py
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