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lorien-danger commited on Sep 16, 2025

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 <!DOCTYPE html>
 <html lang="en">
 <head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>I-JEPA Patch Correspondence Analyzer</title>
-    <style>
-        body {
-            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-            margin: 0;
-            padding: 20px;
-            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-            min-height: 100vh;
-            color: #333;
-        }
-```
-    .container {
-        max-width: 1400px;
-        margin: 0 auto;
-        background: rgba(255, 255, 255, 0.95);
-        border-radius: 20px;
-        padding: 30px;
-        box-shadow: 0 20px 40px rgba(0, 0, 0, 0.1);
-    }
-    h1 {
-        text-align: center;
-        color: #4a5568;
-        margin-bottom: 10px;
-        font-size: 2.5em;
-        font-weight: 300;
-    }
-    .subtitle {
-        text-align: center;
-        color: #718096;
-        margin-bottom: 30px;
-        font-size: 1.1em;
-    }
-    .upload-section {
-        display: grid;
-        grid-template-columns: 1fr 1fr;
-        gap: 30px;
-        margin-bottom: 30px;
-    }
-    .upload-box {
-        border: 3px dashed #cbd5e0;
-        border-radius: 15px;
-        padding: 40px;
-        text-align: center;
-        transition: all 0.3s ease;
-        background: #f7fafc;
-        position: relative;
-        overflow: hidden;
-    }
-    .upload-box:hover {
-        border-color: #667eea;
-        background: #edf2f7;
-        transform: translateY(-2px);
-    }
-    .upload-box.has-image {
-        border-color: #48bb78;
-        background: #f0fff4;
-    }
-    .upload-input {
-        position: absolute;
-        top: 0;
-        left: 0;
-        width: 100%;
-        height: 100%;
-        opacity: 0;
-        cursor: pointer;
-    }
-    .upload-content {
-        pointer-events: none;
-    }
-    .upload-icon {
-        font-size: 3em;
-        margin-bottom: 15px;
-        color: #a0aec0;
-    }
-    .upload-text {
-        font-size: 1.1em;
-        color: #4a5568;
-        margin-bottom: 10px;
-    }
-    .upload-hint {
-        font-size: 0.9em;
-        color: #718096;
-    }
-    .preview-image {
-        max-width: 100%;
-        max-height: 200px;
-        border-radius: 10px;
-        margin-top: 15px;
-        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
-    }
-    .controls {
-        display: flex;
-        justify-content: center;
-        gap: 20px;
-        margin-bottom: 30px;
-        flex-wrap: wrap;
-    }
-    .btn {
-        padding: 12px 30px;
-        border: none;
-        border-radius: 25px;
-        cursor: pointer;
-        font-size: 1em;
-        font-weight: 600;
-        transition: all 0.3s ease;
-        text-transform: uppercase;
-        letter-spacing: 1px;
-    }
-    .btn-primary {
-        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-        color: white;
-    }
-    .btn-primary:hover {
-        transform: translateY(-2px);
-        box-shadow: 0 8px 20px rgba(102, 126, 234, 0.4);
-    }
-    .btn-secondary {
-        background: #e2e8f0;
-        color: #4a5568;
-    }
-    .btn-secondary:hover {
-        background: #cbd5e0;
-        transform: translateY(-2px);
-    }
-    .btn:disabled {
-        background: #e2e8f0;
-        color: #a0aec0;
-        cursor: not-allowed;
-        transform: none;
-    }
-    .loading {
-        text-align: center;
-        padding: 40px;
-        display: none;
-    }
-    .spinner {
-        width: 50px;
-        height: 50px;
-        border: 4px solid #e2e8f0;
-        border-top: 4px solid #667eea;
-        border-radius: 50%;
-        animation: spin 1s linear infinite;
-        margin: 0 auto 20px;
-    }
-    @keyframes spin {
-        0% { transform: rotate(0deg); }
-        100% { transform: rotate(360deg); }
-    }
-    .results {
-        display: none;
-    }
-    .visualization {
-        background: white;
-        border-radius: 15px;
-        padding: 20px;
-        margin-bottom: 20px;
-        box-shadow: 0 4px 12px rgba(0, 0, 0, 0.05);
-    }
-    .images-container {
-        display: grid;
-        grid-template-columns: 1fr 1fr;
-        gap: 30px;
-        margin-bottom: 30px;
-    }
-    .image-analysis {
-        text-align: center;
-    }
-    .analysis-image {
-        max-width: 100%;
-        height: 300px;
-        object-fit: contain;
-        border-radius: 10px;
-        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
-    }
-    .patch-grid {
-        display: inline-block;
-        position: relative;
-        margin-top: 15px;
-    }
-    .patch {
-        position: absolute;
-        border: 2px solid transparent;
-        cursor: pointer;
-        transition: all 0.3s ease;
-    }
-    .patch:hover {
-        border-color: #667eea;
-        background: rgba(102, 126, 234, 0.2);
-        z-index: 10;
-    }
-    .patch.highlighted {
-        border-color: #e53e3e;
-        background: rgba(229, 62, 62, 0.3);
-        z-index: 20;
-    }
-    .patch.corresponding {
-        border-color: #38a169;
-        background: rgba(56, 161, 105, 0.3);
-        z-index: 15;
-    }
-    .correspondences {
-        margin-top: 20px;
-    }
-    .correspondence-line {
-        position: absolute;
-        height: 2px;
-        background: linear-gradient(90deg, #e53e3e, #38a169);
-        z-index: 5;
-        opacity: 0.8;
-        transform-origin: left center;
-    }
-    .stats {
-        display: grid;
-        grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
-        gap: 15px;
-        margin-top: 20px;
-    }
-    .stat-card {
-        background: #f7fafc;
-        padding: 20px;
-        border-radius: 10px;
-        text-align: center;
-        border-left: 4px solid #667eea;
-    }
-    .stat-value {
-        font-size: 2em;
-        font-weight: bold;
-        color: #4a5568;
-    }
-    .stat-label {
-        color: #718096;
-        margin-top: 5px;
-    }
-    .similarity-threshold {
-        margin: 20px 0;
-        text-align: center;
-    }
-    .threshold-slider {
-        width: 300px;
-        margin: 0 10px;
-    }
-    .error {
-        background: #fed7d7;
-        color: #c53030;
-        padding: 15px;
-        border-radius: 10px;
-        margin: 20px 0;
-        text-align: center;
-        display: none;
-    }
-    @media (max-width: 768px) {
-        .upload-section {
-            grid-template-columns: 1fr;
-        }
-        .images-container {
-            grid-template-columns: 1fr;
-        }
-        .controls {
-            flex-direction: column;
-            align-items: center;
-        }
-    }
 </style>
-```
 </head>
 <body>
-    <div class="container">
-        <h1>I-JEPA Patch Correspondence Analyzer</h1>
-        <p class="subtitle">Upload two images to analyze patch-level correspondences using I-JEPA embeddings</p>
-```
-    <div class="upload-section">
-        <div class="upload-box" id="upload1">
-            <input type="file" class="upload-input" accept="image/*" id="file1">
-            <div class="upload-content">
-                <div class="upload-icon">📸</div>
-                <div class="upload-text">Upload Image 1</div>
-                <div class="upload-hint">Click or drag image here</div>
-            </div>
-        </div>
-        <div class="upload-box" id="upload2">
-            <input type="file" class="upload-input" accept="image/*" id="file2">
-            <div class="upload-content">
-                <div class="upload-icon">📸</div>
-                <div class="upload-text">Upload Image 2</div>
-                <div class="upload-hint">Click or drag image here</div>
-            </div>
-        </div>
     </div>
-    <div class="controls">
-        <button class="btn btn-primary" id="analyzeBtn" disabled>
-            🔍 Analyze Patch Correspondences
-        </button>
-        <button class="btn btn-secondary" id="clearBtn">
-            🗑️ Clear Images
-        </button>
-    </div>
-    <div class="error" id="errorMsg"></div>
-    <div class="loading" id="loading">
-        <div class="spinner"></div>
-        <p>Loading I-JEPA model and analyzing images...</p>
-        <p><small>This may take a moment on first load as the model downloads</small></p>
     </div>
-    <div class="results" id="results">
-        <div class="visualization">
-            <div class="similarity-threshold">
-                <label>Similarity Threshold: </label>
-                <input type="range" class="threshold-slider" id="thresholdSlider"
-                       min="0" max="1" step="0.01" value="0.7">
-                <span id="thresholdValue">0.70</span>
-            </div>
-            <div class="images-container">
-                <div class="image-analysis">
-                    <h3>Image 1</h3>
-                    <div class="patch-grid" id="grid1">
-                        <img class="analysis-image" id="img1" alt="Image 1">
-                    </div>
-                </div>
-                <div class="image-analysis">
-                    <h3>Image 2</h3>
-                    <div class="patch-grid" id="grid2">
-                        <img class="analysis-image" id="img2" alt="Image 2">
-                    </div>
-                </div>
-            </div>
-            <div class="stats">
-                <div class="stat-card">
-                    <div class="stat-value" id="totalPatches">0</div>
-                    <div class="stat-label">Total Patches per Image</div>
-                </div>
-                <div class="stat-card">
-                    <div class="stat-value" id="strongCorrespondences">0</div>
-                    <div class="stat-label">Strong Correspondences</div>
-                </div>
-                <div class="stat-card">
-                    <div class="stat-value" id="avgSimilarity">0.00</div>
-                    <div class="stat-label">Average Similarity</div>
-                </div>
-                <div class="stat-card">
-                    <div class="stat-value" id="maxSimilarity">0.00</div>
-                    <div class="stat-label">Max Similarity</div>
-                </div>
-            </div>
-        </div>
     </div>
-</div>
-<script type="module">
-    import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@huggingface/transformers@3.7.3';
-    // Configure environment
-    env.allowRemoteModels = true;
-    env.allowLocalModels = false;
-    let model = null;
-    let processor = null;
-    let image1Data = null;
-    let image2Data = null;
-    let patchEmbeddings1 = null;
-    let patchEmbeddings2 = null;
-    let similarities = null;
-    // Model configuration - using smallest I-JEPA model
-    const MODEL_ID = "facebook/ijepa_vith14_1k";
-    const PATCH_SIZE = 14;
-    const IMAGE_SIZE = 224;
-    // DOM elements
-    const file1Input = document.getElementById('file1');
-    const file2Input = document.getElementById('file2');
-    const upload1 = document.getElementById('upload1');
-    const upload2 = document.getElementById('upload2');
-    const analyzeBtn = document.getElementById('analyzeBtn');
-    const clearBtn = document.getElementById('clearBtn');
-    const loading = document.getElementById('loading');
-    const results = document.getElementById('results');
-    const errorMsg = document.getElementById('errorMsg');
-    const thresholdSlider = document.getElementById('thresholdSlider');
-    const thresholdValue = document.getElementById('thresholdValue');
-    // Utility functions
-    function showError(message) {
-        errorMsg.textContent = message;
-        errorMsg.style.display = 'block';
-        setTimeout(() => {
-            errorMsg.style.display = 'none';
-        }, 5000);
-    }
-    function showLoading(show) {
-        loading.style.display = show ? 'block' : 'none';
-        analyzeBtn.disabled = show;
-    }
-    function showResults(show) {
-        results.style.display = show ? 'block' : 'none';
-    }
-    function updateAnalyzeButton() {
-        analyzeBtn.disabled = !image1Data || !image2Data;
-    }
-    // File handling
-    function handleFileUpload(fileInput, uploadBox, imageData, imageNum) {
-        const file = fileInput.files[0];
-        if (!file) return;
-        const reader = new FileReader();
-        reader.onload = function(e) {
-            const img = new Image();
-            img.onload = function() {
-                // Store image data
-                if (imageNum === 1) {
-                    image1Data = {
-                        url: e.target.result,
-                        width: img.width,
-                        height: img.height
-                    };
-                } else {
-                    image2Data = {
-                        url: e.target.result,
-                        width: img.width,
-                        height: img.height
-                    };
-                }
-                // Update UI
-                uploadBox.classList.add('has-image');
-                const content = uploadBox.querySelector('.upload-content');
-                content.innerHTML = `
-                    <img src="${e.target.result}" class="preview-image" alt="Preview ${imageNum}">
-                    <div style="margin-top: 10px; color: #48bb78; font-weight: 600;">✓ Image ${imageNum} loaded</div>
-                `;
-                updateAnalyzeButton();
-            };
-            img.src = e.target.result;
-        };
-        reader.readAsDataURL(file);
-    }
-    // Event listeners
-    file1Input.addEventListener('change', () => handleFileUpload(file1Input, upload1, image1Data, 1));
-    file2Input.addEventListener('change', () => handleFileUpload(file2Input, upload2, image2Data, 2));
-    clearBtn.addEventListener('click', () => {
-        // Reset all data
-        image1Data = null;
-        image2Data = null;
-        patchEmbeddings1 = null;
-        patchEmbeddings2 = null;
-        similarities = null;
-        // Reset UI
-        file1Input.value = '';
-        file2Input.value = '';
-        upload1.classList.remove('has-image');
-        upload2.classList.remove('has-image');
-        upload1.querySelector('.upload-content').innerHTML = `
-            <div class="upload-icon">📸</div>
-            <div class="upload-text">Upload Image 1</div>
-            <div class="upload-hint">Click or drag image here</div>
-        `;
-        upload2.querySelector('.upload-content').innerHTML = `
-            <div class="upload-icon">📸</div>
-            <div class="upload-text">Upload Image 2</div>
-            <div class="upload-hint">Click or drag image here</div>
-        `;
-        showResults(false);
-        updateAnalyzeButton();
-    });
-    thresholdSlider.addEventListener('input', () => {
-        const threshold = parseFloat(thresholdSlider.value);
-        thresholdValue.textContent = threshold.toFixed(2);
-        if (similarities) {
-            updateVisualization();
-        }
-    });
-    // Model loading and processing
-    async function loadModel() {
-        try {
-            if (!model) {
-                model = await pipeline('feature-extraction', MODEL_ID);
-            }
-            return true;
-        } catch (error) {
-            console.error('Error loading model:', error);
-            showError('Failed to load I-JEPA model. Please check your connection and try again.');
-            return false;
-        }
-    }
-    // Cosine similarity calculation
-    function cosineSimilarity(a, b) {
-        let dotProduct = 0;
-        let normA = 0;
-        let normB = 0;
-        for (let i = 0; i < a.length; i++) {
-            dotProduct += a[i] * b[i];
-            normA += a[i] * a[i];
-            normB += b[i] * b[i];
-        }
-        return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));
-    }
-    // Extract patch embeddings from I-JEPA
-    async function extractPatchEmbeddings(imageUrl) {
-        try {
-            // Create image element
-            const img = new Image();
-            img.crossOrigin = 'anonymous';
-            return new Promise((resolve, reject) => {
-                img.onload = async () => {
-                    try {
-                        // Extract features using the model
-                        const features = await model(img);
-                        // The features tensor should be [1, num_patches + 1, hidden_size]
-                        // We skip the first token (CLS token) to get just patch tokens
-                        const patchTokens = features.slice(null, [1, null], null).squeeze(0);
-                        // Convert to regular arrays for easier processing
-                        const embeddings = [];
-                        for (let i = 0; i < patchTokens.dims[0]; i++) {
-                            const embedding = [];
-                            for (let j = 0; j < patchTokens.dims[1]; j++) {
-                                embedding.push(patchTokens.data[i * patchTokens.dims[1] + j]);
-                            }
-                            embeddings.push(embedding);
-                        }
-                        resolve(embeddings);
-                    } catch (error) {
-                        reject(error);
-                    }
-                };
-                img.onerror = reject;
-                img.src = imageUrl;
-            });
-        } catch (error) {
-            console.error('Error extracting embeddings:', error);
-            throw error;
-        }
-    }
-    // Calculate patch correspondences
-    function calculateCorrespondences() {
-        const numPatches1 = patchEmbeddings1.length;
-        const numPatches2 = patchEmbeddings2.length;
-        similarities = [];
-        for (let i = 0; i < numPatches1; i++) {
-            similarities[i] = [];
-            for (let j = 0; j < numPatches2; j++) {
-                const sim = cosineSimilarity(patchEmbeddings1[i], patchEmbeddings2[j]);
-                similarities[i][j] = sim;
-            }
-        }
-    }
-    // Create patch grid visualization
-    function createPatchGrid(container, imageUrl, imageNum) {
-        container.innerHTML = '';
-        const img = document.createElement('img');
-        img.className = 'analysis-image';
-        img.src = imageUrl;
-        img.alt = `Image ${imageNum}`;
-        img.onload = () => {
-            const imgRect = img.getBoundingClientRect();
-            const patchesPerSide = Math.floor(IMAGE_SIZE / PATCH_SIZE); // 16 patches per side for 224/14
-            const patchWidth = imgRect.width / patchesPerSide;
-            const patchHeight = imgRect.height / patchesPerSide;
-            // Create patch overlay divs
-            for (let row = 0; row < patchesPerSide; row++) {
-                for (let col = 0; col < patchesPerSide; col++) {
-                    const patchIndex = row * patchesPerSide + col;
-                    const patch = document.createElement('div');
-                    patch.className = 'patch';
-                    patch.style.left = `${col * patchWidth}px`;
-                    patch.style.top = `${row * patchHeight}px`;
-                    patch.style.width = `${patchWidth}px`;
-                    patch.style.height = `${patchHeight}px`;
-                    patch.dataset.patchIndex = patchIndex;
-                    patch.dataset.imageNum = imageNum;
-                    patch.addEventListener('mouseenter', () => highlightCorrespondingPatches(patchIndex, imageNum));
-                    patch.addEventListener('mouseleave', () => clearHighlights());
-                    container.appendChild(patch);
-                }
-            }
-        };
-        container.appendChild(img);
-    }
-    // Highlight corresponding patches
-    function highlightCorrespondingPatches(patchIndex, imageNum) {
-        clearHighlights();
-        const threshold = parseFloat(thresholdSlider.value);
-        // Highlight the hovered patch
-        const hoveredPatch = document.querySelector(`[data-patch-index="${patchIndex}"][data-image-num="${imageNum}"]`);
-        if (hoveredPatch) {
-            hoveredPatch.classList.add('highlighted');
-        }
-        // Find and highlight corresponding patches in the other image
-        const otherImageNum = imageNum === 1 ? 2 : 1;
-        if (similarities) {
-            const sims = imageNum === 1 ? similarities[patchIndex] : similarities.map(row => row[patchIndex]);
-            sims.forEach((sim, otherPatchIndex) => {
-                if (sim >= threshold) {
-                    const correspondingPatch = document.querySelector(`[data-patch-index="${otherPatchIndex}"][data-image-num="${otherImageNum}"]`);
-                    if (correspondingPatch) {
-                        correspondingPatch.classList.add('corresponding');
-                    }
-                }
-            });
-        }
-    }
-    // Clear all highlights
-    function clearHighlights() {
-        document.querySelectorAll('.patch.highlighted, .patch.corresponding').forEach(patch => {
-            patch.classList.remove('highlighted', 'corresponding');
-        });
-    }
-    // Update statistics
-    function updateStatistics() {
-        if (!similarities) return;
-        const threshold = parseFloat(thresholdSlider.value);
-        const totalPatches = similarities.length;
-        let strongCorrespondences = 0;
-        let totalSimilarity = 0;
-        let maxSim = 0;
-        let count = 0;
-        for (let i = 0; i < similarities.length; i++) {
-            for (let j = 0; j < similarities[i].length; j++) {
-                const sim = similarities[i][j];
-                totalSimilarity += sim;
-                maxSim = Math.max(maxSim, sim);
-                count++;
-                if (sim >= threshold) {
-                    strongCorrespondences++;
-                }
-            }
-        }
-        document.getElementById('totalPatches').textContent = totalPatches;
-        document.getElementById('strongCorrespondences').textContent = strongCorrespondences;
-        document.getElementById('avgSimilarity').textContent = (totalSimilarity / count).toFixed(3);
-        document.getElementById('maxSimilarity').textContent = maxSim.toFixed(3);
-    }
-    // Update visualization based on current threshold
-    function updateVisualization() {
-        updateStatistics();
-        // Patch highlighting is handled by mouse events
-    }
-    // Main analysis function
-    analyzeBtn.addEventListener('click', async () => {
-        if (!image1Data || !image2Data) return;
-        showLoading(true);
-        showResults(false);
-        try {
-            // Load model if not already loaded
-            const modelLoaded = await loadModel();
-            if (!modelLoaded) {
-                showLoading(false);
-                return;
-            }
-            // Extract patch embeddings for both images
-            patchEmbeddings1 = await extractPatchEmbeddings(image1Data.url);
-            patchEmbeddings2 = await extractPatchEmbeddings(image2Data.url);
-            // Calculate correspondences
-            calculateCorrespondences();
-            // Create visualizations
-            const grid1 = document.getElementById('grid1');
-            const grid2 = document.getElementById('grid2');
-            createPatchGrid(grid1, image1Data.url, 1);
-            createPatchGrid(grid2, image2Data.url, 2);
-            // Update statistics
-            updateStatistics();
-            // Show results
-            showResults(true);
-            showLoading(false);
-        } catch (error) {
-            console.error('Analysis error:', error);
-            showError('Failed to analyze images. Please try again with different images.');
-            showLoading(false);
-        }
-    });
-    // Drag and drop support
-    ['upload1', 'upload2'].forEach((id, index) => {
-        const uploadBox = document.getElementById(id);
-        const fileInput = document.getElementById(`file${index + 1}`);
-        uploadBox.addEventListener('dragover', (e) => {
-            e.preventDefault();
-            uploadBox.style.borderColor = '#667eea';
-            uploadBox.style.background = '#edf2f7';
-        });
-        uploadBox.addEventListener('dragleave', (e) => {
-            e.preventDefault();
-            uploadBox.style.borderColor = '#cbd5e0';
-            uploadBox.style.background = '#f7fafc';
-        });
-        uploadBox.addEventListener('drop', (e) => {
-            e.preventDefault();
-            uploadBox.style.borderColor = '#cbd5e0';
-            uploadBox.style.background = '#f7fafc';
-            const files = e.dataTransfer.files;
-            if (files.length > 0 && files[0].type.startsWith('image/')) {
-                fileInput.files = files;
-                handleFileUpload(fileInput, uploadBox, null, index + 1);
-            }
-        });
-    });
-    // Initial setup
-    console.log('I-JEPA Patch Correspondence Analyzer initialized');
-    console.log(`Using model: ${MODEL_ID}`);
-    console.log(`Patch size: ${PATCH_SIZE}x${PATCH_SIZE}, Image size: ${IMAGE_SIZE}x${IMAGE_SIZE}`);
 </script>
-```
 </body>
 </html>

 <!DOCTYPE html>
 <html lang="en">
 <head>
+<meta charset="utf-8" />
+<meta name="viewport" content="width=device-width,initial-scale=1" />
+<title>I-JEPA Patch Matching (Browser, ONNX)</title>
+<style>
+  :root { --w: 256; --gap: 40; --bg: #0b0d10; --fg: #e8f0f2; --muted:#92a2aa; --accent:#7bdcff; }
+  html,body { height:100%; margin:0; background:var(--bg); color:var(--fg); font-family: ui-sans-serif, system-ui, -apple-system, Segoe UI, Roboto, Helvetica, Arial; }
+  header { padding:16px 18px; border-bottom:1px solid #1b2228; display:flex; gap:16px; align-items:center; flex-wrap:wrap;}
+  header h1 { font-size:16px; margin:0; font-weight:600; letter-spacing:.2px;}
+  header .pill { padding:6px 10px; border:1px solid #24313a; border-radius:999px; color:#cfe7ff; }
+  main { display:grid; grid-template-columns: 320px 1fr; gap:16px; height:calc(100% - 66px); }
+  aside { border-right:1px solid #1b2228; padding:16px; overflow:auto; }
+  section { padding:16px; overflow:auto;}
+  fieldset { border:1px solid #24313a; border-radius:10px; padding:12px; margin:0 0 12px 0;}
+  legend { padding:0 6px; color:#c0d1da; font-size:12px; }
+  label { display:block; font-size:12px; color:#a9bac4; margin:8px 0 4px;}
+  input[type="file"] { width:100%; }
+  .row { display:flex; gap:8px; align-items:center; flex-wrap:wrap;}
+  .row > * { flex:1; min-width:0; }
+  input[type="range"] { width:100%; }
+  button { background:#0f1418; color:var(--fg); border:1px solid #2a3945; padding:10px 12px; border-radius:8px; cursor:pointer;}
+  button:disabled { opacity:.6; cursor:not-allowed;}
+  small.muted { color:var(--muted); }
+  .canv-wrap { display:flex; align-items:center; justify-content:center; }
+  canvas { background:#0a0c0f; border:1px solid #1f2830; border-radius:10px; }
+  .status { font-family: ui-monospace, SFMono-Regular, Menlo, monospace; font-size:12px; color:#c5e3ff; white-space:pre-wrap; background:#0a0f13; border:1px solid #23313b; padding:8px; border-radius:8px; min-height:2.5em;}
+  .gridlabel { font-size:11px; color:#7e909b; }
+  .foot { color:#7a8b95; font-size:12px; }
 </style>
 </head>
 <body>
+<header>
+  <h1>I-JEPA Patch Matching (Transformers.js + ONNX)</h1>
+  <span class="pill">Model: onnx-community/ijepa_vith14_22k · dtype=q8</span>
+</header>
+<main>
+  <aside>
+    <fieldset>
+      <legend>Inputs</legend>
+      <label>Image A</label>
+      <input id="fileA" type="file" accept="image/*" />
+      <label>Image B</label>
+      <input id="fileB" type="file" accept="image/*" />
+      <div class="row" style="margin-top:10px;">
+        <button id="runBtn" disabled>Run patch matching</button>
+        <button id="clearBtn">Clear</button>
+      </div>
+      <small class="muted">Images are resized to 224×224 internally to match the model.</small>
+    </fieldset>
+    <fieldset>
+      <legend>Matching</legend>
+      <label>Top-K lines <span id="kVal" class="gridlabel"></span></label>
+      <input id="k" type="range" min="8" max="256" step="8" value="64" />
+      <label>Min similarity (cosine) <span id="thrVal" class="gridlabel"></span></label>
+      <input id="thr" type="range" min="0" max="100" step="1" value="40" />
+      <div class="row">
+        <label class="row" style="gap:6px;align-items:center;">
+          <input id="mutual" type="checkbox" checked />
+          Mutual nearest neighbors only
+        </label>
+      </div>
+      <div class="row">
+        <label class="row" style="gap:6px;align-items:center;">
+          <input id="showGrid" type="checkbox" />
+          Show 16×16 patch grid overlay
+        </label>
+      </div>
+    </fieldset>
+    <fieldset>
+      <legend>Runtime</legend>
+      <div class="row">
+        <label class="row" style="gap:6px;align-items:center;">
+          <input id="preferGPU" type="checkbox" />
+          Try WebGPU (if available)
+        </label>
+      </div>
+      <label>Quantization</label>
+      <select id="dtype">
+        <option value="q8" selected>q8 (smallest, default)</option>
+        <option value="fp32">fp32</option>
+      </select>
+      <label>Model repo</label>
+      <input id="modelId" type="text" value="onnx-community/ijepa_vith14_22k" />
+      <small class="muted">Patch size is 14; tokens map to a 16×16 grid. CLS token is dropped if present.</small>
+    </fieldset>
+    <fieldset>
+      <legend>Status</legend>
+      <div id="status" class="status">Idle.</div>
+    </fieldset>
+    <div class="foot">
+      Preprocess (from model card): resize 224, rescale 1/255, normalize mean=std=0.5. Patch size=14.
+      Outputs are per-patch hidden states; we build a full cosine similarity matrix.
     </div>
+  </aside>
+  <section>
+    <div class="canv-wrap">
+      <canvas id="viz" width="544" height="240" aria-label="Patch correspondence visualizer"></canvas>
     </div>
+    <div class="row" style="margin-top:8px;">
+      <small class="muted">Left = Image A (224×224). Right = Image B (224×224). Lines connect matched patch centers.</small>
     </div>
+  </section>
+</main>
+<!-- Transformers.js UMD (exposes window.transformers) -->
+<script src="https://cdn.jsdelivr.net/npm/@huggingface/transformers@3.7.3/dist/transformers.min.js"></script>
+<script>
+(async () => {
+  const status = (msg) => document.getElementById('status').textContent = msg;
+  // UI elements
+  const fileA = document.getElementById('fileA');
+  const fileB = document.getElementById('fileB');
+  const runBtn = document.getElementById('runBtn');
+  const clearBtn = document.getElementById('clearBtn');
+  const preferGPU = document.getElementById('preferGPU');
+  const dtypeSel = document.getElementById('dtype');
+  const modelIdInput = document.getElementById('modelId');
+  const kSlider = document.getElementById('k');
+  const thrSlider = document.getElementById('thr');
+  const kVal = document.getElementById('kVal');
+  const thrVal = document.getElementById('thrVal');
+  const mutualChk = document.getElementById('mutual');
+  const gridChk = document.getElementById('showGrid');
+  const W = 224, H = 224, PATCH = 14; // per config
+  const GRID = W / PATCH;             // 16
+  const GAP = 96;                      // gap between images on the canvas
+  const cvs = document.getElementById('viz');
+  const ctx = cvs.getContext('2d');
+  const setSliders = () => {
+    kVal.textContent = `(${kSlider.value})`;
+    thrVal.textContent = `(${(thrSlider.value/100).toFixed(2)})`;
+  };
+  setSliders();
+  kSlider.addEventListener('input', setSliders);
+  thrSlider.addEventListener('input', setSliders);
+  // Enable buttons when both files chosen
+  const updateReady = () => runBtn.disabled = !(fileA.files?.[0] && fileB.files?.[0]);
+  fileA.addEventListener('change', updateReady);
+  fileB.addEventListener('change', updateReady);
+  clearBtn.onclick = () => {
+    fileA.value = ''; fileB.value = '';
+    runBtn.disabled = true;
+    ctx.clearRect(0,0,cvs.width,cvs.height);
+    status('Cleared.');
+  };
+  // Load Transformers.js and configure runtime
+  const { pipeline, env } = window.transformers;
+  // Configure ONNX Runtime Web assets and caching
+  env.backends.onnx.wasm.wasmPaths = "https://cdn.jsdelivr.net/npm/onnxruntime-web@1.19.2/dist/";
+  env.useBrowserCache = true;
+  env.allowRemoteModels = true;
+  // WebGPU hint (Transformers.js chooses the best available)
+  preferGPU.addEventListener('change', () => {
+    // Hint: runtime picks WebGPU automatically if available; keeping as a user toggle placeholder
+    status(preferGPU.checked ? 'Will try WebGPU where possible.' : 'Defaulting to WASM backend.');
+  });
+  // Helpers
+  const loadImageURL = (file) => new Promise((resolve, reject) => {
+    const url = URL.createObjectURL(file);
+    const img = new Image();
+    img.onload = () => resolve({ url, img });
+    img.onerror = reject;
+    img.crossOrigin = "anonymous";
+    img.src = url;
+  });
+  function drawSideBySide(imgA, imgB) {
+    // Clear
+    ctx.fillStyle = '#0a0c0f';
+    ctx.fillRect(0, 0, cvs.width, cvs.height);
+    // Draw A and B (padded)
+    const leftX = 8, topY = 8;
+    ctx.drawImage(imgA, leftX, topY, W, H);
+    const rightX = leftX + W + GAP;
+    ctx.drawImage(imgB, rightX, topY, W, H);
+    // Optional grid
+    if (gridChk.checked) {
+      ctx.strokeStyle = 'rgba(255,255,255,0.15)';
+      ctx.lineWidth = 1;
+      for (let i=1;i<GRID;i++) {
+        const xA = leftX + i*PATCH, xB = rightX + i*PATCH, y = topY + i*PATCH;
+        ctx.beginPath(); ctx.moveTo(xA, topY); ctx.lineTo(xA, topY+H); ctx.stroke();
+        ctx.beginPath(); ctx.moveTo(leftX, y); ctx.lineTo(leftX+W, y); ctx.stroke();
+        ctx.beginPath(); ctx.moveTo(xB, topY); ctx.lineTo(xB, topY+H); ctx.stroke();
+        ctx.beginPath(); ctx.moveTo(rightX, y); ctx.lineTo(rightX+W, y); ctx.stroke();
+      }
+    }
+    return { leftX, rightX, topY };
+  }
+  // Normalize a [L,D] flat Float32Array in-place to unit vectors per row
+  function rowNormalize(data, L, D) {
+    for (let i=0; i<L; i++) {
+      let sum=0.0, off = i*D;
+      for (let j=0; j<D; j++) { const v = data[off+j]; sum += v*v; }
+      const inv = 1.0 / Math.max(Math.sqrt(sum), 1e-12);
+      for (let j=0; j<D; j++) data[off+j] *= inv;
+    }
+  }
+  // Cosine sim matrix S (L1 x L2) = A_norm (L1 x D) * B_norm^T (D x L2)
+  function simMatrix(A, L1, B, L2, D) {
+    const S = new Float32Array(L1 * L2);
+    for (let i=0; i<L1; i++) {
+      const ai = i*D;
+      for (let j=0; j<L2; j++) {
+        const bj = j*D;
+        let acc = 0.0;
+        // unrolled loop could help but keep simple & correct
+        for (let d=0; d<D; d++) acc += A[ai+d] * B[bj+d];
+        S[i*L2 + j] = acc;
+      }
+    }
+    return S;
+  }
+  function argmaxPerRow(S, rows, cols) {
+    const idx = new Int32Array(rows);
+    const val = new Float32Array(rows);
+    for (let i=0; i<rows; i++) {
+      let bestV = -Infinity, bestJ = -1, off = i*cols;
+      for (let j=0; j<cols; j++) {
+        const v = S[off + j];
+        if (v > bestV) { bestV = v; bestJ = j; }
+      }
+      idx[i] = bestJ; val[i] = bestV;
+    }
+    return { idx, val };
+  }
+  function argmaxPerCol(S, rows, cols) {
+    const idx = new Int32Array(cols);
+    const val = new Float32Array(cols);
+    for (let j=0; j<cols; j++) {
+      let bestV = -Infinity, bestI = -1;
+      for (let i=0; i<rows; i++) {
+        const v = S[i*cols + j];
+        if (v > bestV) { bestV = v; bestI = i; }
+      }
+      idx[j] = bestI; val[j] = bestV;
+    }
+    return { idx, val };
+  }
+  function gridCenter(k) {
+    // k in [0, L). grid is row-major over 16x16.
+    const r = Math.floor(k / GRID);
+    const c = k % GRID;
+    return { r, c, cx: c*PATCH + PATCH/2, cy: r*PATCH + PATCH/2 };
+  }
+  function drawMatches(base, matches, L2, topK, thr, mutualOnly) {
+    const { leftX, rightX, topY } = base;
+    // Sort by similarity desc
+    matches.sort((a,b) => b.sim - a.sim);
+    const K = Math.min(topK, matches.length);
+    // Draw lines
+    for (let n=0, drawn=0; n<matches.length && drawn<K; n++) {
+      const m = matches[n];
+      if (m.sim < thr) continue;
+      if (mutualOnly && !m.mutual) continue;
+      const A = gridCenter(m.i);
+      const B = gridCenter(m.j);
+      const x1 = leftX + A.cx, y1 = topY + A.cy;
+      const x2 = rightX + B.cx, y2 = topY + B.cy;
+      // color by similarity (blue→cyan)
+      const t = Math.min(1, Math.max(0, (m.sim - thr) / (1 - thr)));
+      const r = Math.floor(60 + 40*t);
+      const g = Math.floor(200 + 30*t);
+      const b = Math.floor(255);
+      ctx.strokeStyle = `rgba(${r},${g},${b},${0.85})`;
+      ctx.lineWidth = 1.25;
+      ctx.beginPath();
+      ctx.moveTo(x1, y1);
+      ctx.lineTo(x2, y2);
+      ctx.stroke();
+      drawn++;
+    }
+  }
+  // Extract per-patch tokens as a flat Float32Array [L,D]
+  function tokens2D(tensor) {
+    // Expect dims [B, L, D] OR [L, D]
+    const dims = tensor.dims;
+    let L, D, data = tensor.data;
+    if (dims.length === 3) {
+      L = dims[1]; D = dims[2];
+    } else if (dims.length === 2) {
+      L = dims[0]; D = dims[1];
+    } else {
+      throw new Error(`Unexpected tensor shape: [${dims.join(',')}]`);
+    }
+    // If CLS present, drop first token to get a perfect square (16x16)
+    const isSquare = (n) => Number.isInteger(Math.sqrt(n));
+    if (!isSquare(L) && isSquare(L - 1)) {
+      // Slice off the first token (CLS) → returns [L-1, D]
+      const out = new Float32Array((L - 1) * D);
+      let dst = 0, src = D; // skip first row
+      for (let i=1;i<L;i++, src += D) {
+        out.set(data.subarray(src, src + D), dst);
+        dst += D;
+      }
+      return { data: out, L: L - 1, D };
+    }
+    // Already square grid
+    return { data: Float32Array.from(data), L, D };
+  }
+  let extractor = null;
+  async function ensureExtractor() {
+    if (extractor) return extractor;
+    const modelId = modelIdInput.value.trim();
+    const dtype = dtypeSel.value; // "q8" or "fp32"
+    status(`Loading model: ${modelId} (${dtype}) ...`);
+    const t0 = performance.now();
+    extractor = await pipeline(
+      "image-feature-extraction",
+      modelId,
+      { dtype } // uses ONNX + wasm/webgpu under the hood
+    );
+    const t1 = performance.now();
+    status(`Model ready in ${(t1 - t0).toFixed(0)} ms. Awaiting images...`);
+    return extractor;
+  }
+  async function run() {
+    try {
+      runBtn.disabled = true;
+      const [{img: imgA}, {img: imgB}] = await Promise.all([
+        loadImageURL(fileA.files[0]),
+        loadImageURL(fileB.files[0]),
+      ]);
+      // Draw base images
+      const base = drawSideBySide(imgA, imgB);
+      // Load extractor (once)
+      await ensureExtractor();
+      status('Extracting per-patch features ...');
+      const t0 = performance.now();
+      // We can pass Blob URLs/HTMLImageElements; Transformers.js handles preprocessing
+      const out = await extractor([imgA, imgB]); // returns a Tensor of shape [2, L, D]
+      const dims = out.dims; // expect [B, L, D]
+      // Split the batch into two separate tensors (copying data slices)
+      if (!(dims.length === 3 && dims[0] === 2)) {
+        throw new Error(`Unexpected output dims: [${dims.join(',')}]`);
+      }
+      const B = dims[0], L = dims[1], D = dims[2];
+      // Slice batch 0 and 1
+      const stride = L * D;
+      const dataA = out.data.subarray(0, stride);
+      const dataB = out.data.subarray(stride, 2*stride);
+      // Convert to [L',D] and drop CLS if present (to get 16x16)
+      const Atd = tokens2D({ data: dataA, dims: [L, D] });
+      const Btd = tokens2D({ data: dataB, dims: [L, D] });
+      if (Atd.L !== GRID*GRID || Btd.L !== GRID*GRID) {
+        console.warn('Token count not 16x16; continuing anyway.', Atd.L, Btd.L);
+      }
+      // Normalize rows for cosine similarity
+      rowNormalize(Atd.data, Atd.L, Atd.D);
+      rowNormalize(Btd.data, Btd.L, Btd.D);
+      status('Computing similarity matrix ... (this is O(L^2·D))');
+      const S = simMatrix(Atd.data, Atd.L, Btd.data, Btd.L, Atd.D);
+      // Argmaxes for A→B and B→A
+      const A2B = argmaxPerRow(S, Atd.L, Btd.L);
+      const B2A = argmaxPerCol(S, Atd.L, Btd.L);
+      // Build match list
+      const thr = Number(thrSlider.value)/100.0;
+      const pairs = [];
+      for (let i=0; i<Atd.L; i++) {
+        const j = A2B.idx[i];
+        const sim = A2B.val[i];
+        const mutual = (B2A.idx[j] === i);
+        pairs.push({ i, j, sim, mutual });
+      }
+      // Redraw base (so grid toggle applies immediately)
+      drawSideBySide(imgA, imgB);
+      drawMatches(base, pairs, Btd.L, Number(kSlider.value), thr, mutualChk.checked);
+      const t1 = performance.now();
+      status(`Done. Tokens: ${Atd.L}×${Atd.D}. Max sim: ${Math.max(...pairs.map(p=>p.sim)).toFixed(3)}. Total ${(t1-t0).toFixed(0)} ms.`);
+    } catch (err) {
+      console.error(err);
+      status('Error: ' + (err && err.message ? err.message : String(err)));
+    } finally {
+      runBtn.disabled = false;
+    }
+  }
+  runBtn.onclick = run;
+  status('Ready. Load two images, then click “Run patch matching”.');
+})();
 </script>
 </body>
 </html>