ONNX Models for Vidupe.Net

This repository contains ONNX-exported models used by Vidupe.Net for visual similarity and perceptual comparison tasks.

Models

vidupe.net/models/clip_visual_vit_b32.onnx

CLIP visual encoder (ViT-B/32) exported to ONNX. This model encodes images into a 512-dimensional embedding space, enabling semantic image similarity comparisons.

• Source: openai/clip-vit-base-patch32
• Input: RGB image tensor [batch, 3, 224, 224], normalized
• Output: Image embeddings [batch, 512]

vidupe.net/models/lpips_alexnet.onnx

LPIPS (Learned Perceptual Image Patch Similarity) model with an AlexNet backbone exported to ONNX. Computes perceptual distance between two image patches.

• Source: richzhang/PerceptualSimilarity
• Input: Two normalized RGB image tensors [batch, 3, H, W]
• Output: Perceptual distance score [batch, 1, 1, 1]

Usage

import onnxruntime as ort
import numpy as np

# CLIP visual encoder
session = ort.InferenceSession("vidupe.net/models/clip_visual_vit_b32.onnx")
image = np.random.randn(1, 3, 224, 224).astype(np.float32)
embeddings = session.run(None, {"input": image})[0]

# LPIPS perceptual similarity
session = ort.InferenceSession("vidupe.net/models/lpips_alexnet.onnx")
img0 = np.random.randn(1, 3, 64, 64).astype(np.float32)
img1 = np.random.randn(1, 3, 64, 64).astype(np.float32)
distance = session.run(None, {"input0": img0, "input1": img1})[0]

Requirements

onnxruntime>=1.16.0
numpy