---
license: apache-2.0
datasets:
- nebula/OpenSDI_test
- madebyollin/megalith-10m
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- OpenSDI
- Spotting Diffusion-Generated Images in the Open World
- OpenSDI
- SD3
- AI-vs-Real
- SigLIP2
---

![3.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/6YXDxA-BKpXAPHMQYLv4V.png)

# OpenSDI-SD3-SigLIP2

> OpenSDI-SD3-SigLIP2 is a vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for binary image classification. It is trained to detect whether an image is a real photograph or generated using Stable Diffusion 3 (SD3), using the SiglipForImageClassification architecture.

> [!note]
*SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features* https://arxiv.org/pdf/2502.14786

> [!note]
*OpenSDI: Spotting Diffusion-Generated Images in the Open World* https://arxiv.org/pdf/2503.19653, OpenSDI SD3 SigLIP2 works best with crisp and high-quality images. Noisy images are not recommended for validation.

> [!warning]
If the task is based on image content moderation or AI-generated image vs. real image classification, it is recommended to use the OpenSDI-Flux.1-SigLIP2 model.

```py
Classification Report:
               precision    recall  f1-score   support

   Real_Image     0.8526    0.8916    0.8716     10000
SD3_Generated     0.8864    0.8458    0.8656     10000

     accuracy                         0.8687     20000
    macro avg     0.8695    0.8687    0.8686     20000
 weighted avg     0.8695    0.8687    0.8686     20000
```

![download (1).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/MKSJZ_cv6QI5FVEtkX5R6.png)

---

## Label Space: 2 Classes

The model classifies an image as either:

```
Class 0: Real_Image
Class 1: SD3_Generated
```

---

## Install Dependencies

```bash
pip install -q transformers torch pillow gradio hf_xet
```

---

## Inference Code

```python
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/OpenSDI-SD3-SigLIP2"  # Update with the correct model path
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# Label mapping
id2label = {
    "0": "Real_Image",
    "1": "SD3_Generated"
}

def classify_image(image):
    image = Image.fromarray(image).convert("RGB")
    inputs = processor(images=image, return_tensors="pt")

    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()

    prediction = {
        id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
    }

    return prediction

# Gradio Interface
iface = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Label(num_top_classes=2, label="SD3 Image Detection"),
    title="OpenSDI-SD3-SigLIP2",
    description="Upload an image to determine whether it is a real photograph or generated by Stable Diffusion 3 (SD3)."
)

if __name__ == "__main__":
    iface.launch()
```

---

## Intended Use

OpenSDI-SD3-SigLIP2 is designed for tasks such as:

* Generative Image Analysis – Identify SD3-generated images for benchmarking and quality inspection.
* Dataset Validation – Ensure training or evaluation datasets are free from unintended generative artifacts.
* Content Authenticity – Verify whether visual media originates from real-world photography or AI generation.
* Digital Forensics – Aid in determining the origin of visual content in investigative scenarios.