OpenSDI-Flux.1-SigLIP2
OpenSDI-Flux.1-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 the Flux.1 generative model, based on 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 Flux.1 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 this model.
Classification Report:
precision recall f1-score support
Real_Image 0.9108 0.9238 0.9172 10000
Flux.1_Generated 0.9227 0.9095 0.9160 10000
accuracy 0.9166 20000
macro avg 0.9167 0.9166 0.9166 20000
weighted avg 0.9167 0.9166 0.9166 20000
Label Space: 2 Classes
The model classifies an image as either:
Class 0: Real_Image
Class 1: Flux.1_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-Flux.1-SigLIP2" # Update if needed
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Label mapping
id2label = {
"0": "Real_Image",
"1": "Flux.1_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="Flux.1 Image Detection"),
title="OpenSDI-Flux.1-SigLIP2",
description="Upload an image to determine whether it is a real photograph or generated by Flux.1."
)
if __name__ == "__main__":
iface.launch()
Demo Inference
[!warning] Flux.1 Generated
| Image 1 | Image 2 |
|---|---|
 |  |
[!warning] Real Image
| Image 1 | Image 2 |
|---|---|
 |  |
Intended Use
OpenSDI-Flux.1-SigLIP2 is designed for tasks such as:
- Generative Model Evaluation – Distinguish Flux.1-generated images from real photos for benchmarking and validation.
- Dataset Auditing – Detect synthetic images in real-world datasets to maintain integrity.
- Misinformation Detection – Identify AI-generated visuals in online or news content.
- Media Authentication – Verify whether visual content originates from human-captured or model-generated sources.