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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.

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

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.

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.

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

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Label Space: 2 Classes

The model classifies an image as either:

Class 0: Real_Image
Class 1: SD3_Generated

Install Dependencies

pip install -q transformers torch pillow gradio hf_xet

Inference Code

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.

Base model