Instructions to use JasonYANG170/sd15-inpainting-onnx-fp32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JasonYANG170/sd15-inpainting-onnx-fp32 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("JasonYANG170/sd15-inpainting-onnx-fp32", 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
Stable Diffusion 1.5 inpainting ONNX FP32
ONNX FP32 export for browser/local inference. Exported from
stable-diffusion-v1-5/stable-diffusion-inpainting at immutable revision 8a4288a76071f7280aedbdb3253bdb9e9d5d84bb.
- Task:
inpainting - Resolution:
512x512 - ONNX opset:
17 - Safety checker: not included
- Provenance and per-file hashes:
export-manifest.jsonandSHA256SUMS
The model is intended to be loaded as a Diffusers ONNX pipeline. Review the upstream model card and CreativeML Open RAIL-M license before distribution or use.
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