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README.md

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+---
+pipeline_tag: text-to-image
+---
+
+# Taming Generative Synthetic Data for X-ray Prohibited Item Detection
+
+This repository contains the Xsyn model, a one-stage X-ray security image synthesis pipeline based on text-to-image generation. Proposed in the paper [Taming Generative Synthetic Data for X-ray Prohibited Item Detection](https://huggingface.co/papers/2511.15299), Xsyn addresses data insufficiency for prohibited item detection by incorporating two effective strategies: Cross-Attention Refinement (CAR) for refining bounding box annotations and Background Occlusion Modeling (BOM) for enhancing imaging complexity. It aims to achieve high-quality X-ray security image synthesis without incurring additional labor-intensive foreground preparation.
+
+Code repository: [https://github.com/pILLOW-1/Xsyn/](https://github.com/pILLOW-1/Xsyn/)
+
+<figure style="display:block; text-align:center; margin:0 auto;">
+  <img src="https://github.com/pILLOW-1/Xsyn/raw/main/figures/analysis.jpg"
+       alt="Analysis of existing X-ray image synthesis methods"
+       style="width:90%; max-width:600px; margin:0 auto; display:block;">
+</figure>
+
+## Download Xsyn models
+
+Checkpoints for different datasets are available. All models here are based on GLIGEN.
+| Dataset       | Mode       | Download                                                                                                       |
+|------------|----------------|----------------------------------------------------------------------------------------------------------------|
+| PIDray | text-grounded inpainting       | [HF Hub](https://huggingface.co/Pillow-1/Xsyn)       |
+| OPIXray | text-grounded inpainting | [HF Hub](https://huggingface.co/Pillow-1/Xsyn) |
+| HiXray | text-grounded inpainting       | [HF Hub](https://huggingface.co/Pillow-1/Xsyn)       |
+
+## Inference
+
+We provide one script to generate x-ray security images and construct their annotations. First download models and put them in `--ckpt_path`. Then run:
+
+```bash
+python gligen_inference.py
+```
+
+Details of some important args:
+
+- `--output_path`: the path to save your generated x-ray security images
+- `--annotation_path`: the path to save the refined annotation (stored in txt format)
+- `--vis_path`: the path to save visualization compared with gt
+- `--ca_vis_path`: the path to save cross-attention maps
+- `--image_path`: the path to load images you want to inpaint
+- `--ckpt_path`: the generation model checkpoint path
+- `--gligen_caption_pt`: the file to prepare your training/test data in [GLIGEN](https://github.com/gligen/GLIGEN) format
+- `--gen_method`: set to 1 for Xsyn-M and 3 for Xsyn-A
+- `--refine_anno`: set to True for `CAR`
+- `--latent_redist`: set to True for `BOM`
+
+After inference, you can use `downstream_test.sh` to test the performance of our synthetic data. Our downstream detection environment is [mmdetection](https://github.com/open-mmlab/mmdetection).