Instructions to use LiXiY/ReferenceAnomaly with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use LiXiY/ReferenceAnomaly 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("LiXiY/ReferenceAnomaly", 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
File size: 561 Bytes
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license: apache-2.0
base_model:
- stable-diffusion-v1-5/stable-diffusion-v1-5
- stable-diffusion-v1-5/stable-diffusion-inpainting
pipeline_tag: image-to-image
library_name: diffusers
tags:
- image-editing
- reference-image-generation
---
**Reference-based Anomaly Image Generation via Inpainting**
This model generates realistic anomaly images by transferring defect patterns from a reference anomaly image onto a normal (background) image within a user-defined inpainting mask region.
**Github: https://github.com/huan-yin/reference_anomaly_generation** |