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
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Download README.md from LiXiY/ReferenceAnomaly: direct link, hf CLI and curl.
- Browser
- Download file 561 Bytes
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https://huggingface.co/LiXiY/ReferenceAnomaly/resolve/main/README.md
- Command line
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hf download hf://LiXiY/ReferenceAnomaly/README.md
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curl -L -o README.md https://huggingface.co/LiXiY/ReferenceAnomaly/resolve/main/README.md
561 Bytes
| 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** |