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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width">
<title>OpenAI CLIP Image Search in JavaScript (Using ONNX Web Runtime)</title>
<script src="enable-threads.js"></script>
<script src="./vips/vips.js"></script>
</head>
<body>
<style>
body * {
font-family: monospace;
}
</style>
<script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/ort.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]/dist/tf.min.js"></script> <!-- NOTE: tfjs is currently only used for image preprocessing stuff. -->
<div>
<h1 style="font-size:1rem;">Sort/search images using OpenAI's CLIP in your browser</h1>
<p>This web app sorts/searches through images in a directory on your computer using OpenAI's CLIP model, and the new File System Access API. <a href="https://github.com/josephrocca/clip-image-sorter">Here's the Github repo</a> for this web app, and <a href="https://github.com/josephrocca/openai-clip-js">here's the Github repo</a> for the web-ported CLIP models. Feel free to open an issue if you have any questions about this demo, but note that I'm not actively adding any more features right now.</p>
<p>All processing happens in your browser, on your device - i.e. your images are <b>not</b> uploaded to a server for processing.</p>
<p id="browserCompatibilityWarning" style="padding:0.25rem; background:rgb(255, 227, 160); display:none;"><b>Heads Up</b>: Your browser is missing one or more features (File System Access API and/or credentialless COEP) that are needed for this demo to work. As of writing, it works in Chromium-based browsers like Chrome and Edge. Other browsers like Firefox and Safari are often a bit slower in implementing cutting-edge features.</p>
<script>
if(!self.crossOriginIsolated || !window.showDirectoryPicker) {
browserCompatibilityWarning.style.display = "";
}
</script>
<hr>
<div id="modelNameSelectCtn" style="padding:0.5rem; background:lightgrey; margin:0.5rem;">
<b>Step 1:</b> Choose model:
<select onchange="window.MODEL_NAME=this.value;">
<option value="clip_vit_32">CLIP ViT-B/32 (recommended)</option>
<option value="clip_vit_32_uint8">CLIP ViT-B/32 (quantized - inaccurate embeddings)</option>
<option value="lit_b16b">LiT B16B</option>
</select>
</div>
<div id="initCtnEl" style="padding:0.5rem; background:lightgrey; margin:0.5rem;">
<b>Step 2:</b> Download and initialize the models.
<br>
Download image model: <progress id="imageModelLoadingProgressBarEl" value="0"></progress> <span id="imageModelLoadingMbEl"></span>
<br>
Download text model: <progress id="textModelLoadingProgressBarEl" value="0"></progress> <span id="textModelLoadingMbEl"></span>
<br>
Initialize workers: <progress id="workerInitProgressBarEl" value="0"></progress>
<div style="display:none;"> <!-- more workers (dividing threads between them) doesn't seem to make things faster -->
Number of image embedding workers/threads: <input id="numThreadsEl" type="range" min="1" max="4" value="1" oninput="numThreadsDisplayEl.textContent=this.value"> <span id="numThreadsDisplayEl"></span> <script>numThreadsEl.max = navigator.hardwareConcurrency; numThreadsDisplayEl.textContent=numThreadsEl.value;</script>
</div>
<br>
<button id="initWorkersBtn" onclick="modelNameSelectCtn.style.pointerEvents='none'; modelNameSelectCtn.style.opacity=0.5; initializeWorkers()">initialize workers</button>
</div>
<div id="pickDirCtnEl" style="opacity:0.5; pointer-events:none; padding:0.5rem; background:lightgrey; margin:0.5rem;">
<b>Step 3:</b> Pick a directory of images (images in subdirectories will be included).
<br>
<button id="pickDirectoryBtn" onclick="pickDirectory({source:'local'})">pick directory</button> or <button id="useRedditImagesBtn" onclick="pickDirectory({source:'reddit'})">use ~200k reddit images</button> (remove nsfw:<input id="removeRedditNsfwEl" type="checkbox" checked>)
<br>
<div id="redditLoadProgressCtn" style="display:none;">Download progress: <progress id="redditProgressBarEl" value="0"></progress> <span id="redditProgressMbEl"></span></div>
<div id="existingEmbeddingsProgressCtn" style="display:none;">Loading existing embeddings: <span id="existingEmbeddingsLoadedEl">none</span></div>
</div>
<div id="computeEmbeddingsCtnEl" style="opacity:0.5; pointer-events:none; padding:0.5rem; background:lightgrey; margin:0.5rem;">
<b>Step 4:</b> Compute image embeddings. <span style="opacity:0.5;">(they will be saved as <ModelName>_embeddings.tsv in the selected directory)</span>
<br>
<button id="computeEmbeddingsBtn" onclick="computeImageEmbeddings(); this.disabled=true;">compute image embeddings</button>
<br>
<span id="computeEmbeddingsProgressEl">0</span> images embedded (<span id="computeEmbeddingsSpeedEl">?</span> ms per image) <span id="preexistingEmbeddingsEl"></span>
</div>
<div id="existingEmbeddingsFoundCtnEl" style="display:none; padding:0.5rem; background:lightgrey; margin:0.5rem;">
<b>Step 5:</b> <b>Existing embeddings found.</b>
<br>
Only needed if you've added or changed images: <button onclick="existingEmbeddingsFoundCtnEl.style.display='none'; computeEmbeddingsCtnEl.style.display=''; disableCtn(searchCtnEl); computeEmbeddingsBtn.click()">(re)compute image embeddings</button>
<input id="onlyEmbedNewImagesCheckbox" type="checkbox" checked> Only new images?
</div>
<div id="searchCtnEl" style="opacity:0.5; pointer-events:none; padding:0.5rem; background:lightgrey; margin:0.5rem;">
<b>Step 6:</b> Enter a search term or <button id="searchWithImagesBtn" style="font-size:80%;margin-bottom: 0.25rem;" onclick="loadImagesAsQuery()">search with image(s)</button>
<br>
<input id="searchTextEl" style="width:500px;" value="" placeholder="Enter search text here..." onclick="clearImageQueryIfNeeded()" onkeyup="if(event.which==13) searchSort()">
<button id="searchBtn" onclick="searchSort()">search</button> max results: <input id="maxResultsEl" type="number" value="100" style="width:50px;"> skip first: <input title="skip over this many of the most relevant results" id="skipFirstResultsNumEl" type="number" value="0" style="width:50px;"> score-based visibility: <button onclick="scoreBasedVisibilityCtnEl.style.display=''; this.remove();">enable</button>
<div id="searchNoteEl" style="font-size:80%;"></div>
<div id="scoreBasedVisibilityCtnEl" style="display:none;">
<textarea id="scoreBasedVisibilityRulesInputEl" style="width:100%; height:180px; white-space:nowrap;" placeholder="mouse hover here to show examples of syntax - one filter rule per line" title="skip:>0.25:blue cat skip:<0.21:elephant"></textarea>
</div>
</div>
</div>
<hr>
<b>Results</b> <span id="skippedCountEl"></span> <span style="opacity:0.5;">(hover over images for cosine similarities)</span>
<div id="resultsEl" style="margin-top:1rem; min-height:100vh;"><span style="opacity:0.5;">Click the search button to compute the results.</span></div>
<script>
/////////////
// STEP 1 //
/////////////
window.MODEL_NAME = "clip_vit_32";
window.modelData = {
clip_vit_32: {
image: {
modelUrl: (quantized) => `https://huggingface.co/rocca/openai-clip-js/resolve/main/clip-image-vit-32-${quantized ? "uint8" : "float32"}.onnx`,
embed: async function(blob, session) {
let mode = "bicubicResizeAndCenterCrop";
if(window.debugImageTransformMode) mode = window.debugImageTransformMode;
let rgbData = await getRgbData(blob, mode);
const feeds = {input: new ort.Tensor('float32', rgbData, [1,3,224,224])};
const results = await session.run(feeds);
const embedVec = results["output"].data; // Float32Array
return embedVec;
}
},
text: {
modelUrl: (quantized) => `https://huggingface.co/rocca/openai-clip-js/resolve/main/clip-text-vit-32-${quantized ? "uint8" : "float32-int32"}.onnx`,
embed: async function(text, session) {
if(!window.textTokenizerClip) {
let Tokenizer = (await import("https://deno.land/x/[email protected]/mod.js")).default;
window.textTokenizerClip = new Tokenizer();
}
let textTokens = window.textTokenizerClip.encodeForCLIP(text);
textTokens = Int32Array.from(textTokens);
const feeds = {input: new ort.Tensor('int32', textTokens, [1, 77])};
const results = await session.run(feeds);
return [...results["output"].data];
},
}
},
lit_b16b: {
image: {
modelUrl: () => 'https://huggingface.co/rocca/lit-web/resolve/main/embed_images.onnx',
embed: async function(blob, session) {
// TODO: Maybe remove tf from this code so you can remove the whole tfjs dependency
blob = await bicubicResizeAndCenterCrop(blob);
let inputImg = new Image();
await new Promise(r => inputImg.onload=r, inputImg.src=URL.createObjectURL(blob));
let img = tf.browser.fromPixels(inputImg);
img = tf.sub(tf.div(tf.expandDims(img), 127.5), 1);
let float32RgbData = img.dataSync();
const feeds = {'images': new ort.Tensor('float32', float32RgbData, [1,224,224,3])};
const results = await session.run(feeds);
return results["Identity_1:0"].data;
},
},
text: {
modelUrl: () => 'https://huggingface.co/rocca/lit-web/resolve/main/embed_text_tokens.onnx',
embed: async function(text, session) {
if(!window.bertTextTokenizerLit) {
window.bertTextTokenizerLit = await import("./bert-text-tokenizer.js").then(m => new m.BertTokenizer());
await window.bertTextTokenizerLit.load();
}
let textTokens = window.bertTextTokenizerLit.tokenize(text);
textTokens.unshift(101); // manually put CLS token at the start
textTokens.length = 16;
textTokens = [...textTokens.slice(0, 16)].map(e => e == undefined ? 0 : e); // pad with zeros to length of 16
textTokens = Int32Array.from(textTokens);
const feeds = {'text_tokens': new ort.Tensor('int32', textTokens, [1,16])};
const results = await session.run(feeds);
return [...results["Identity_1:0"].data];
}
}
},
};
let imageWorkers = [];
let onnxImageSessions = [];
let onnxTextSession;
let textTokenizer;
async function initializeWorkers() {
initWorkersBtn.disabled = true;
numThreadsEl.disabled = true;
let useQuantizedModel = false;
if(MODEL_NAME.endsWith("_uint8")) {
MODEL_NAME = MODEL_NAME.replace(/_uint8$/g, "");
useQuantizedModel = true;
}
let imageOnnxBlobPromise = downloadBlobWithProgress(window.modelData[MODEL_NAME].image.modelUrl(useQuantizedModel), function(e) {
let ratio = e.loaded / e.total;
imageModelLoadingProgressBarEl.value = ratio;
imageModelLoadingMbEl.innerHTML = Math.round(ratio*e.total/1e6)+" MB";
});
let textOnnxBlobPromise = downloadBlobWithProgress(window.modelData[MODEL_NAME].text.modelUrl(useQuantizedModel), function(e) {
let ratio = e.loaded / e.total;
textModelLoadingProgressBarEl.value = ratio;
textModelLoadingMbEl.innerHTML = Math.round(ratio*e.total/1e6)+" MB";
});
let [imageOnnxBlob, textOnnxBlob] = await Promise.all([imageOnnxBlobPromise, textOnnxBlobPromise])
let imageModelUrl = window.URL.createObjectURL(imageOnnxBlob);
let textModelUrl = window.URL.createObjectURL(textOnnxBlob);
let numImageWorkers = Number(numThreadsEl.value);
// Inference latency is about 5x faster with wasm threads, but this requires these headers: https://web.dev/coop-coep/ I'm using this as a hack (in enable-threads.js) since Github pages doesn't allow setting headers: https://github.com/gzuidhof/coi-serviceworker
if(self.crossOriginIsolated) {
ort.env.wasm.numThreads = Math.ceil(navigator.hardwareConcurrency / numImageWorkers) / 2; // divide by two to utilise only half the CPU's threads because trying to use all the cpu's threads actually makes it slower
}
workerInitProgressBarEl.max = numImageWorkers + 2; // +2 because of text model and bpe library
let imageModelExecutionProviders = ["wasm"]; // webgl is not compatible with this model (need to tweak conversion data/op types)
for(let i = 0; i < numImageWorkers; i++) {
let session = await ort.InferenceSession.create(imageModelUrl, { executionProviders: imageModelExecutionProviders });
onnxImageSessions.push(session);
imageWorkers.push({
session,
busy: false,
});
workerInitProgressBarEl.value = Number(workerInitProgressBarEl.value) + 1;
}
console.log("Image model loaded.");
onnxTextSession = await ort.InferenceSession.create(textModelUrl, { executionProviders: ["wasm"] }); // webgl is not compatible with this model (need to tweak conversion data/op types)
console.log("Text model loaded.");
workerInitProgressBarEl.value = Number(workerInitProgressBarEl.value) + 1;
window.URL.revokeObjectURL(imageModelUrl);
window.URL.revokeObjectURL(textModelUrl);
window.vips = await Vips(); // for bicubicly resizing images (since that's what CLIP expects)
window.vips.EMBIND_AUTOMATIC_DELETELATER = false;
workerInitProgressBarEl.value = Number(workerInitProgressBarEl.value) + 1;
disableCtn(initCtnEl);
enableCtn(pickDirCtnEl);
}
/////////////
// STEP 2 //
/////////////
let directoryHandle;
let embeddingsFileHandle;
let embeddings;
let dataSource;
async function pickDirectory(opts={}) {
dataSource = opts.source;
if(dataSource === "local") {
if(!window.showDirectoryPicker) return alert("Your browser does not support some modern features (specifically, File System Access API) required to use this web app. Please try again with a Chromium-based browser, like Chrome or Edge.");
directoryHandle = await window.showDirectoryPicker({mode: 'readwrite'});
embeddingsFileHandle = await directoryHandle.getFileHandle(`${window.MODEL_NAME}_embeddings.tsv`, {create:true});
pickDirectoryBtn.disabled = true;
useRedditImagesBtn.disabled = true;
pickDirectoryBtn.textContent = "Loading...";
}
let redditEmbeddingsBlob;
if(dataSource === "reddit") {
if(window.MODEL_NAME !== "clip_vit_32") return alert("Sorry, there are only pre-computed Reddit image embeddings for the CLIP ViT-B/32 model at the moment.");
if(!removeRedditNsfwEl.checked && !confirm("Are you sure you'd like to see NSFW Reddit images?")) return;
if(removeRedditNsfwEl.checked) alert("Note that NSFW images are filtered from Reddit using CLIP, and CLIP can make mistakes, so some NSFW images may still be shown.");
pickDirectoryBtn.disabled = true;
useRedditImagesBtn.disabled = true;
useRedditImagesBtn.textContent = "Loading...";
redditLoadProgressCtn.style.display = "";
redditEmbeddingsBlob = await downloadBlobWithProgress("https://huggingface.co/datasets/rocca/top-reddit-posts/resolve/main/clip_embeddings_top_50_images_per_subreddit.tsv.gz", function(e) {
let ratio = e.loaded / e.total;
redditProgressBarEl.value = ratio;
redditProgressMbEl.innerHTML = Math.round(ratio*213)+" MB";
});
}
try {
existingEmbeddingsProgressCtn.style.display = "";
embeddings = {};
let file, opts;
if(dataSource === "local") {
file = await embeddingsFileHandle.getFile();
opts = {};
}
if(dataSource === "reddit") {
file = redditEmbeddingsBlob;
opts = {decompress:"gzip"};
}
let i = 0;
for await (let line of makeTextFileLineIterator(file, opts)) {
if(!line || !line.trim()) continue; // <-- to skip final new line (not sure if this is needed)
let [filePath, embeddingVec] = line.split("\t");
embeddings[filePath] = JSON.parse(embeddingVec);
i++;
if(i % 1000 === 0) {
existingEmbeddingsLoadedEl.innerHTML = i;
await sleep(10);
}
}
} catch(e) {
embeddings = undefined;
console.log("No existing embedding found, or the embeddings file was corrupted:", e);
existingEmbeddingsProgressCtn.style.display = "none";
}
pickDirectoryBtn.textContent = "Done.";
useRedditImagesBtn.textContent = "Done.";
disableCtn(pickDirCtnEl);
enableCtn(computeEmbeddingsCtnEl);
enableCtn(searchCtnEl);
if(embeddings && Object.keys(embeddings).length > 0) {
computeEmbeddingsCtnEl.style.display = "none";
existingEmbeddingsFoundCtnEl.style.display = "";
}
if(dataSource === "reddit") {
disableCtn(existingEmbeddingsFoundCtnEl);
}
}
/////////////
// STEP 3 //
/////////////
let totalEmbeddingsCount = 0;
let imagesEmbedded;
let recentEmbeddingTimes = []; // how long each embed took in ms, newest at end
let recomputeAllEmbeddings;
let imagesBeingProcessedNow = 0;
let needToSaveEmbeddings = false;
async function computeImageEmbeddings() {
imagesEmbedded = 0;
totalEmbeddingsCount = Object.keys(embeddings).length;
recomputeAllEmbeddings = !onlyEmbedNewImagesCheckbox.checked;
let gotSomeExistingEmbeddings = totalEmbeddingsCount > 0;
if(onlyEmbedNewImagesCheckbox.checked && gotSomeExistingEmbeddings) {
preexistingEmbeddingsEl.innerHTML = `(loaded ${Object.keys(embeddings).length} existing embeddings)`;
}
if(recomputeAllEmbeddings || !gotSomeExistingEmbeddings) {
embeddings = {}; // <-- maps file path (relative to top/selected directory) to embedding
}
try {
await recursivelyProcessImagesInDir(directoryHandle);
await saveEmbeddings();
} catch(e) {
console.error(e);
alert(e.message);
}
disableCtn(computeEmbeddingsCtnEl);
enableCtn(searchCtnEl);
}
async function recursivelyProcessImagesInDir(dirHandle, currentPath="") {
for await (let [name, handle] of dirHandle) {
const {kind} = handle;
let path = `${currentPath}/${name}`;
if (handle.kind === 'directory') {
await recursivelyProcessImagesInDir(handle, path);
} else {
let isImage = /\.(png|jpg|jpeg|webp)$/.test(path);
if(!isImage) continue;
let alreadyGotEmbedding = !!embeddings[path];
if(alreadyGotEmbedding && !recomputeAllEmbeddings) continue;
if(needToSaveEmbeddings) {
await saveEmbeddings();
needToSaveEmbeddings = false;
}
while(imageWorkers.filter(w => !w.busy).length === 0) await sleep(1);
let worker = imageWorkers.filter(w => !w.busy)[0];
worker.busy = true;
imagesBeingProcessedNow++;
(async function() {
let startTime = Date.now();
let blob = await handle.getFile();
const embedVec = await modelData[MODEL_NAME].image.embed(blob, worker.session);
embeddings[path] = [...embedVec];
worker.busy = false;
imagesEmbedded++;
totalEmbeddingsCount++;
computeEmbeddingsProgressEl.innerHTML = imagesEmbedded;
let saveInterval = totalEmbeddingsCount > 50_000 ? 10_000 : 1000; // since saves take longer if there are lots of embeddings
if(imagesEmbedded % saveInterval === 0) {
needToSaveEmbeddings = true;
}
recentEmbeddingTimes.push(Date.now()-startTime);
if(recentEmbeddingTimes.length > 100) recentEmbeddingTimes = recentEmbeddingTimes.slice(-50);
if(recentEmbeddingTimes.length > 10) computeEmbeddingsSpeedEl.innerHTML = Math.round(recentEmbeddingTimes.slice(-20).reduce((a,v) => a+v, 0)/20);
imagesBeingProcessedNow--;
})();
}
}
while(imagesBeingProcessedNow > 0) await sleep(10);
}
/////////////
// STEP 4 //
/////////////
async function searchSort() {
searchBtn.disabled = true;
if(dataSource === "local") {
for(let imgEl of [...document.querySelectorAll("img")]) {
URL.revokeObjectURL(imgEl.src);
}
}
resultsEl.innerHTML = "Loading...";
await sleep(50);
searchNoteEl.innerHTMl = "";
skippedCountEl.innerHTML = "";
let searchTexts = [searchTextEl.value];
let extraLabels = [];
if(searchTexts[0].includes(";;;")) {
let parts = searchTexts[0].split(";;;").map(s => s.trim());
searchTexts[0] = parts[0];
extraLabels = parts.slice(1);
searchNoteEl.innerHTML = `<b>Note</b>: Your search text contained the special <b>;;;</b> delimiter, so the first part is used as the search ranking text, and the rest will be used as extra labels. Hover over an image to see the extra label scores - they don't affect ranking.`;
} else if(searchTexts[0].includes("|||")) {
searchTexts = searchTexts[0].split("|||").map(s => s.trim());
searchNoteEl.innerHTML = `<b>Note</b>: Your search text contained the special <b>|||</b> delimiter, so it will be split up into multiple searches and the centroid/mean embedding of all the searches will be used for the scoring.`;
}
let searchEmbedding;
if(imageQueryVectors) {
searchEmbedding = meanVector(imageQueryVectors);
} else {
let embeddings = [];
for(let text of searchTexts) {
embeddings.push(await modelData[MODEL_NAME].text.embed(text, onnxTextSession));
}
searchEmbedding = meanVector(embeddings);
}
let similarities = {}; // maps path to similarity score
console.log("searchEmbedding:", searchEmbedding);
let extraLabelsEmbeddings = await Promise.all(extraLabels.map(label => modelData[MODEL_NAME].text.embed(label, onnxTextSession)));
let extraLabelsSimilarities = {}; // maps path to object with label and similarity score
for(let [path, embedding] of Object.entries(embeddings)) {
similarities[path] = cosineSimilarity(searchEmbedding, embedding);
for(let i = 0; i < extraLabels.length; i++) {
let label = extraLabels[i];
if(!extraLabelsSimilarities[path]) extraLabelsSimilarities[path] = [];
extraLabelsSimilarities[path].push({
label,
similarity: cosineSimilarity(extraLabelsEmbeddings[i], embedding),
});
}
}
let similarityEntries = Object.entries(similarities).sort((a,b) => b[1]-a[1]).slice(0, 500000);
if(dataSource === "reddit" && removeRedditNsfwEl.checked) {
let nsfwTextEmbedding = await modelData[MODEL_NAME].text.embed(atob('cG9ybiBuYWtlZCBwZW5pcyB2YWdpbmEgbnVkZSBzZXggZGljayBwdXNzeSBzZXh1YWwgcG9ybm9ncmFwaGljIGFzcyBib29icw=='), onnxTextSession); // nsfw words (hidden with `btoa`)
let nsfwSimilarities = {};
for(let [path, similarity] of similarityEntries) {
let embedding = embeddings[path];
nsfwSimilarities[path] = cosineSimilarity(nsfwTextEmbedding, embedding);
}
similarityEntries = similarityEntries.filter(e => nsfwSimilarities[e[0]] < 0.2093);
}
let scoreBasedVisibilityRules = scoreBasedVisibilityRulesInputEl.value.trim().split("\n").map(s => s.trim()).filter(s => s);
scoreBasedVisibilityRules = scoreBasedVisibilityRules.map(r => {
let [type, threshold, ...text] = r.split(":").map(s => s.trim());
text = text.join(":").trim();
if(threshold[0] !== "<" && threshold[0] !== ">") {
alert("Invalid threshold in score-based visibility rule. There must be a < or > before the threshold value.")
return null;
}
if(type !== "skip") {
alert("Only the 'skip' type is supported. Your rule should start with 'skip:'.");
return null;
}
let direction = threshold[0] === "<" ? "lt" : "gt";
threshold = Number(threshold.slice(1));
return {type, direction, threshold, text};
});
scoreBasedVisibilityRules = scoreBasedVisibilityRules.filter(r => r !== null);
for(let rule of scoreBasedVisibilityRules) {
rule.embedding = await modelData[MODEL_NAME].text.embed(rule.text, onnxTextSession);
}
let resultHtml = "";
let numResults = 0;
let maxNumResults = Number(maxResultsEl.value);
let skipFirstResultsNum = Number(skipFirstResultsNumEl.value);
let skippedDueToVisibilityRulesCount = 0;
let skitFirstCount = 0;
for(let [path, score] of similarityEntries) {
let shouldSkip = false;
for(let rule of scoreBasedVisibilityRules) {
let similarity = cosineSimilarity(rule.embedding, embeddings[path]);
if(rule.type === "skip" && rule.direction === "gt" && similarity > rule.threshold) shouldSkip = true;
if(rule.type === "skip" && rule.direction === "lt" && similarity < rule.threshold) shouldSkip = true;
}
if(shouldSkip) {
skippedDueToVisibilityRulesCount++;
continue;
}
if(skitFirstCount < skipFirstResultsNum) {
skitFirstCount++;
continue;
}
if(dataSource === "local") {
let url;
try {
let handle = await getFileHandleByPath(path).catch(e => null);
url = URL.createObjectURL(await handle.getFile());
} catch(e) { console.warn("A file that we computed embeddings for has since been deleted."); }
if(!url) continue;
resultHtml += `<img src="${url}" style="max-height:400px;" title="${path}: ${score.toFixed(3)}
${(extraLabelsSimilarities[path] || []).map(o => `${o.label}: ${o.similarity.toFixed(3)}`).join("
")}" loading="lazy"/>`;
}
if(dataSource === "reddit") {
let imageUrl = `https://i.redd.it/${path.split("__")[1]}`;
let postUrl = `https://reddit.com/comments/${path.split("__")[0].split("/")[1]}`;
resultHtml += `<a href="${postUrl}" target="_blank"><img src="${imageUrl}" onload="this.style.height='';this.style.width='';this.style.border='';" style="max-height:400px; height:300px; width:300px; border:1px solid black;" title="${path}: ${score.toFixed(3)}
${(extraLabelsSimilarities[path] || []).map(o => `${o.label}: ${o.similarity.toFixed(3)}`).join("
")}" loading="lazy"/></a>`;
}
numResults++;
if(numResults >= maxNumResults) break;
}
if(skippedDueToVisibilityRulesCount > 0) {
skippedCountEl.innerHTML = `(skipped <b>${skippedDueToVisibilityRulesCount}</b> images due to visibility rules)`;
}
if(!resultHtml) {
resultsEl.innerHTML = "No results found after filtering NSFW.";
} else {
resultsEl.innerHTML = resultHtml;
}
searchBtn.disabled = false;
}
let imageQueryVectors = null;
async function clearImageQueryIfNeeded() {
if(imageQueryVectors !== null) {
imageQueryVectors = null;
searchTextEl.value = "";
searchWithImagesBtn.innerHTML = `search with image(s)`;
}
}
async function loadImagesAsQuery() {
let fileHandles = await window.showOpenFilePicker({multiple:true});
searchWithImagesBtn.innerHTML = "loading...";
imageQueryVectors = [];
for(let handle of fileHandles) {
let blob = await handle.getFile();
let embedVec = await modelData[MODEL_NAME].image.embed(blob, imageWorkers[0].session);
imageQueryVectors.push(embedVec);
}
searchWithImagesBtn.innerHTML = `using ${imageQueryVectors.length} images`;
searchTextEl.value = "<using image query, click here to use text instead>";
}
function addImagePathsToIgnoreList() {
}
/////////////////////////////
// FUNCTIONS / UTILITIES //
/////////////////////////////
function meanVector(vectors) {
const vectorLength = vectors[0].length;
const mean = Array(vectorLength).fill(0);
vectors.forEach(vector => {
vector.forEach((value, index) => {
mean[index] += value;
});
});
return mean.map(val => val / vectors.length);
};
async function getFileHandleByPath(path) {
let handle = directoryHandle;
let chunks = path.split("/").slice(1);
for(let i = 0; i < chunks.length; i++) {
let chunk = chunks[i];
if(i === chunks.length-1) {
handle = await handle.getFileHandle(chunk);
} else {
handle = await handle.getDirectoryHandle(chunk);
}
}
return handle;
}
async function getRgbData(blob, mode="bicubicResizeAndCenterCrop") {
// let blob = await fetch(imgUrl, {referrer:""}).then(r => r.blob());
let width = 224;
let height = 224;
let imageData;
if(mode === "defaultCanvasResizeAndCenterCrop") {
let img = await createImageBitmap(blob);
let canvas = new OffscreenCanvas(224, 224);
let ctx = canvas.getContext("2d");
// scale img to fit the shorter side to the canvas size
let scale = Math.max(canvas.width / img.width, canvas.height / img.height);
// compute new image dimensions that would maintain the original aspect ratio
let scaledW = img.width * scale;
let scaledH = img.height * scale;
// compute position to center the image
let posX = (canvas.width - scaledW) / 2;
let posY = (canvas.height - scaledH) / 2;
// draw the image centered and scaled on the canvas
ctx.drawImage(img, posX, posY, scaledW, scaledH);
imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
} else if(mode === "bicubicResizeAndCenterCrop") {
let resizedBlob = await bicubicResizeAndCenterCrop(blob);
let img = await createImageBitmap(resizedBlob);
let canvas;
if(window.document) {
canvas = document.createElement("canvas");
canvas.width = width;
canvas.height = height;
} else {
canvas = new OffscreenCanvas(width, height);
}
let ctx = canvas.getContext("2d");
ctx.drawImage(img, 0, 0);
// document.body.appendChild(canvas);
imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
} else if(mode === "defaultCanvasSquash") {
let img = await createImageBitmap(resizedBlob);
let canvas = new OffscreenCanvas(width, height);
let ctx = canvas.getContext("2d");
ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
} else {
throw new Error("Invalid resize/crop type");
}
let rgbData = [[], [], []]; // [r, g, b]
// remove alpha and put into correct shape:
let d = imageData.data;
for(let i = 0; i < d.length; i += 4) {
let x = (i/4) % width;
let y = Math.floor((i/4) / width)
if(!rgbData[0][y]) rgbData[0][y] = [];
if(!rgbData[1][y]) rgbData[1][y] = [];
if(!rgbData[2][y]) rgbData[2][y] = [];
rgbData[0][y][x] = d[i+0]/255;
rgbData[1][y][x] = d[i+1]/255;
rgbData[2][y][x] = d[i+2]/255;
// From CLIP repo: Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))
rgbData[0][y][x] = (rgbData[0][y][x] - 0.48145466) / 0.26862954;
rgbData[1][y][x] = (rgbData[1][y][x] - 0.4578275) / 0.26130258;
rgbData[2][y][x] = (rgbData[2][y][x] - 0.40821073) / 0.27577711;
}
rgbData = Float32Array.from(rgbData.flat().flat());
return rgbData;
}
async function bicubicResizeAndCenterCrop(blob) {
let im1 = vips.Image.newFromBuffer(await blob.arrayBuffer());
// Resize so smallest side is 224px:
const scale = 224 / Math.min(im1.height, im1.width);
let im2 = im1.resize(scale, { kernel: vips.Kernel.cubic });
// crop to 224x224:
let left = (im2.width - 224) / 2;
let top = (im2.height - 224) / 2;
let im3 = im2.crop(left, top, 224, 224)
let outBuffer = new Uint8Array(im3.writeToBuffer('.png'));
im1.delete(), im2.delete(), im3.delete();
return new Blob([outBuffer], { type: 'image/png' });
}
function downloadBlobWithProgress(url, onProgress) {
return new Promise((res, rej) => {
var blob;
var xhr = new XMLHttpRequest();
xhr.open('GET', url, true);
xhr.responseType = 'arraybuffer';
xhr.onload = function(e) {
blob = new Blob([this.response]);
};
xhr.onprogress = onProgress;
xhr.onloadend = function(e){
res(blob);
}
xhr.send();
});
}
async function saveEmbeddings(opts={}) {
let writable = await embeddingsFileHandle.createWritable();
let textBatch = "";
let i = 0;
for(let [filePath, embeddingVec] of Object.entries(embeddings)) {
let vecString = opts.compress ? JSON.stringify(embeddingVec.map(n => n.toFixed(3))).replace(/"/g, "") : JSON.stringify(embeddingVec);
textBatch += `${filePath}\t${vecString}\n`;
i++;
if(i % 1000 === 0) {
await writable.write(textBatch);
textBatch = "";
}
}
await writable.write(textBatch);
await writable.close();
}
// Tweaked version of example from here: https://developer.mozilla.org/en-US/docs/Web/API/ReadableStreamDefaultReader/read
async function* makeTextFileLineIterator(blob, opts={}) {
const utf8Decoder = new TextDecoder("utf-8");
let stream = await blob.stream();
if(opts.decompress === "gzip") stream = stream.pipeThrough(new DecompressionStream("gzip"));
let reader = stream.getReader();
let {value: chunk, done: readerDone} = await reader.read();
chunk = chunk ? utf8Decoder.decode(chunk, {stream: true}) : "";
let re = /\r\n|\n|\r/gm;
let startIndex = 0;
while (true) {
let result = re.exec(chunk);
if (!result) {
if (readerDone) {
break;
}
let remainder = chunk.substr(startIndex);
({value: chunk, done: readerDone} = await reader.read());
chunk = remainder + (chunk ? utf8Decoder.decode(chunk, {stream: true}) : "");
startIndex = re.lastIndex = 0;
continue;
}
yield chunk.substring(startIndex, result.index);
startIndex = re.lastIndex;
}
if (startIndex < chunk.length) {
// last line didn't end in a newline char
yield chunk.substr(startIndex);
}
}
function cosineSimilarity(A, B) {
if(A.length !== B.length) throw new Error("A.length !== B.length");
let dotProduct = 0, mA = 0, mB = 0;
for(let i = 0; i < A.length; i++){
dotProduct += A[i] * B[i];
mA += A[i] * A[i];
mB += B[i] * B[i];
}
mA = Math.sqrt(mA);
mB = Math.sqrt(mB);
let similarity = dotProduct / (mA * mB);
return similarity;
}
function sleep(ms) {
return new Promise(r => setTimeout(r, ms));
}
function enableCtn(el) {
el.style.opacity = 1;
el.style.pointerEvents = "";
}
function disableCtn(el) {
el.style.opacity = 0.5;
el.style.pointerEvents = "none";
}
// From the PyTorch model running on CUDA:
// Text: "a portrait of an astronaut with the American flag"
// Embedding: [-1.6626e-01, 5.2277e-02, -1.5332e-01, 4.4946e-01, 2.0667e-01, -2.9565e-01, 4.0588e-02, -4.1016e-01, -1.5027e-01, 3.1934e-01, -6.9702e-02, -2.5488e-01, 1.2335e-01, -9.5337e-02, 2.4109e-01, -4.8950e-02, 2.6074e-01, 5.3835e-04, 2.1033e-01, 3.7012e-01, 4.5679e-01, 3.9795e-01, 3.1641e-01, 3.9551e-01, 1.3931e-02, -4.3060e-02, 4.8798e-02, 3.7158e-01, 1.1731e-01, -3.7256e-01, -2.7295e-01, 3.3130e-01, 5.4980e-01, -2.9816e-02, -2.5806e-01, -1.0016e-01, 8.0750e-02, -6.7139e-02, -2.4072e-01, 2.4353e-01, -3.2202e-01, -1.0327e-01, 1.1566e-01, 6.2646e-01, 1.8262e-01, 2.7539e-01, -1.1816e-01, 4.9512e-01, 8.9539e-02, 5.6299e-01, 2.1313e-01, -1.5625e-01, 1.9958e-01, -5.0049e-01, -2.5854e-01, -4.0430e-01, -1.1298e-01, -6.6338e-03, 2.5391e-01, -5.0629e-02, 2.2253e-01, -2.7295e-01, -5.8289e-03, -4.8804e-01, -7.7820e-02, -3.5187e-02, -3.7537e-02, 4.3213e-01, 3.8300e-02, 2.1045e-01, -3.0347e-01, -9.8999e-02, -1.7407e-01, 2.8882e-01, 1.1322e-01, -1.0883e-01, 1.7065e-01, -2.1191e-01, 1.7920e-01, -1.2805e-01, -4.6924e-01, 1.1957e-01, -1.1829e-01, -1.1902e-01, -2.4353e-01, -9.6008e-02, 2.2913e-01, -1.0948e-02, -1.5686e-01, -2.0483e-01, -2.4756e-01, 9.1125e-02, -9.5557e-01, -4.2511e-02, 4.6356e-02, 4.3481e-01, 2.3633e-01, -3.3252e-01, 3.7231e-01, -5.5695e-02, 7.1777e-02, -1.0370e-01, -2.1912e-01, -1.3733e-01, 1.2048e-01, 1.7151e-01, -1.2659e-01, 2.3523e-01, 2.6001e-01, -4.0381e-01, 1.1761e-01, -4.1626e-02, 1.0974e-01, -5.5206e-02, 4.9713e-02, 5.2197e-01, 3.9124e-02, 5.7959e-01, 9.9609e-02, -3.3740e-01, -2.7295e-01, -7.3389e-01, 1.0962e-01, -3.2178e-01, 6.5869e-01, 2.3460e-03, 2.6733e-02, 3.2471e-02, -2.4500e-01, 7.9041e-02, 1.5405e-01, -3.5547e-01, -1.5625e-01, 4.2695e+00, -1.6113e-01, 1.6467e-01, -3.8794e-01, 2.1545e-02, 1.5771e-01, 2.0068e-01, -3.4741e-01, 2.5244e-01, 1.2201e-01, -3.9795e-02, 3.2471e-01, 2.6562e-01, 1.2915e-01, 1.4465e-02, -2.1265e-01, -2.5055e-02, 5.6689e-01, -6.5125e-02, 1.1652e-01, -5.1025e-01, 1.0712e-01, 8.8867e-02, 1.6882e-01, -6.5125e-02, 4.5929e-02, 2.9517e-01, 6.5479e-01, -9.0881e-02, -4.6417e-02, -3.0713e-01, -5.1537e-03, 4.9390e-01, 5.4492e-01, -3.1812e-01, -6.0577e-02, 3.6591e-02, 7.6782e-02, -3.5181e-01, 4.9487e-01, -4.1040e-01, 4.3970e-01, 1.6553e-01, -3.7329e-01, 3.3594e-01, -1.9263e-01, -1.8225e-01, -1.9623e-02, -4.0454e-01, 1.6187e-01, 1.5259e-01, 2.5122e-01, 1.1993e-01, -8.4595e-02, -4.1016e-01, -1.8225e-01, 1.8555e-01, -3.9124e-02, 2.5122e-01, -2.2949e-01, 5.3125e-01, -1.0504e-01, -2.1439e-02, -2.2559e-01, 5.9357e-02, -3.9160e-01, -3.3716e-01, -9.0393e-02, -1.7493e-01, -2.5952e-01, 2.3401e-01, 3.8013e-01, 1.2927e-01, 1.5491e-01, 1.1920e-01, -1.5906e-01, 5.7487e-03, 7.6172e-02, 1.5552e-01, 2.1790e-01, 9.9304e-02, -3.8025e-02, -1.1829e-01, -1.3293e-01, -2.2278e-01, -2.1472e-01, 3.0957e-01, -1.0254e-01, -2.0264e-01, -2.6840e-02, 8.8379e-02, -8.2092e-02, 1.3647e-01, -2.1399e-01, -2.5684e-01, 1.3745e-01, 6.1371e-02, -1.2988e-01, 6.2683e-02, 1.2964e-01, -3.3112e-02, 1.4111e-01, -2.6440e-01, 4.7379e-03, -1.0815e-01, 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