| | --- |
| | license: apache-2.0 |
| | --- |
| | # 🖼️ DiffSeg30k -- A multi-turn diffusion-editing dataset for localized AIGC detection |
| |
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| | A dataset for **segmenting diffusion-based edits** — ideal for training and evaluating models that localize edited regions and identify the underlying diffusion model |
| |
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| | ## 📁 Dataset Usage |
| | - `xxxxxxxx.image.png`: Edited images. Each image may have undergone 1, 2, or 3 editing operations. |
| | - `xxxxxxxx.mask.png`: The corresponding mask indicating edited regions, where pixel values encode the diffusion model used. |
| |
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| | Load images and masks as follows: |
| |
|
| | ```python |
| | from datasets import load_dataset |
| | dataset = load_dataset("Chaos2629/Diffseg30k", split="train") |
| | image, mask = dataset[0]['image'], dataset[0]['mask'] |
| | ``` |
| |
|
| | ## 🧠 Mask Annotation |
| |
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| | Each mask is a grayscale image (PNG format), where pixel values correspond to a specific editing model. The mapping is as follows: |
| |
|
| | | Mask Value | Editing Model | |
| | |------------|------------------------------------------------------| |
| | | 0 | background | |
| | | 1 | stabilityai/stable-diffusion-2-inpainting | |
| | | 2 | kolors | |
| | | 3 | stabilityai/stable-diffusion-3.5-medium | |
| | | 4 | flux | |
| | | 5 | diffusers/stable-diffusion-xl-1.0-inpainting-0.1 | |
| | | 6 | glide | |
| | | 7 | Tencent-Hunyuan/HunyuanDiT-Diffusers | |
| | | 8 | kandinsky-community/kandinsky-2-2-decoder-inpaint | |
| |
|
| | ## 📌 Notes |
| |
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| | - Each edited image may be edited **multiple turns**, so the corresponding mask may contain several different **label values** ranging from 0 to 8. |
| |
|
| | ## 📄 License |
| |
|
| | Apache-2.0 |
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