openpilot v0.11.1 release
date: 2026-06-04T09:49:56 master commit: c0ab3550eca2e9daf197c46b7e4b24aa9637cf2e
This commit is contained in:
Vehicle Researcher
@@ -0,0 +1,632 @@
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>tinygrad has WebGPU</title>
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<style>
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/* General Reset */
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* {
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margin: 0;
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padding: 0;
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box-sizing: border-box;
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}
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body {
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font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
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background: #f4f7fb;
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color: #333;
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display: flex;
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justify-content: center;
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align-items: center;
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min-height: 100vh;
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padding: 20px;
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flex-direction: column;
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text-align: center;
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}
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h1 {
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font-size: 2.2rem;
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margin-bottom: 20px;
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color: #4A90E2;
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}
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#wgpuError {
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color: red;
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font-size: 1.2rem;
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margin-top: 20px;
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display: none;
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}
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#sdTitle {
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font-size: 1.5rem;
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margin-bottom: 30px;
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}
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#mybox {
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background: #ffffff;
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padding: 15px;
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border-radius: 10px;
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box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
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width: 120%;
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max-width: 550px;
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margin-bottom: 10px;
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}
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input[type="text"] {
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width: 100%;
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padding: 12px;
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margin-bottom: 20px;
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font-size: 1rem;
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border: 1px solid #ccc;
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border-radius: 8px;
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outline: none;
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transition: all 0.3s ease;
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}
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|
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input[type="text"]:focus {
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border-color: #4A90E2;
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}
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|
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label {
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display: flex;
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justify-content: space-between;
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||||
font-size: 1rem;
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margin-bottom: 15px;
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align-items: center;
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}
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input[type="range"] {
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width: 100%;
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margin-left: 10px;
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-webkit-appearance: none;
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appearance: none;
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height: 8px;
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border-radius: 4px;
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background: #ddd;
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outline: none;
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transition: background 0.3s ease;
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}
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input[type="range"]:focus {
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background: #4A90E2;
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}
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#stepRange,
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#guidanceRange {
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width: 80%;
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}
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span {
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font-size: 1.1rem;
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font-weight: 600;
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color: #333;
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}
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input[type="button"] {
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padding: 12px 25px;
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background-color: #4A90E2;
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color: #fff;
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font-size: 1.2rem;
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border: none;
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border-radius: 8px;
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cursor: pointer;
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transition: background-color 0.3s ease;
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width: 100%;
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margin-bottom: 20px;
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}
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input[type="button"]:disabled {
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background-color: #ccc;
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cursor: not-allowed;
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}
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input[type="button"]:hover {
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background-color: #357ABD;
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}
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#divModelDl, #divStepProgress {
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display: flex;
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justify-content: space-between;
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align-items: center;
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margin-bottom: 20px;
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}
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#modelDlProgressBar,
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#progressBar {
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width: 80%;
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height: 12px;
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border-radius: 6px;
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background-color: #e0e0e0;
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}
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#modelDlProgressBar::-webkit-progress-bar,
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#progressBar::-webkit-progress-bar {
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border-radius: 6px;
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}
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#modelDlProgressValue, #progressFraction {
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font-size: 1rem;
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font-weight: 600;
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color: #333;
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}
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canvas {
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max-width: 100%;
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max-height: 450px;
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margin-top: 10px;
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border-radius: 8px;
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border: 1px solid #ddd;
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}
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</style>
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<script type="module">
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import ClipTokenizer from './clip_tokenizer.js';
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window.clipTokenizer = new ClipTokenizer();
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</script>
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<script src="./net.js"></script>
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</head>
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<body>
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<h1 id="wgpuError" style="display: none;">WebGPU is not supported in this browser</h1>
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<h1 id="sdTitle">StableDiffusion powered by <a href="https://github.com/tinygrad/tinygrad" target="_blank" style="color: #4A90E2;">tinygrad</a></h1>
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<a href="https://github.com/tinygrad/tinygrad" target="_blank" style="position: absolute; top: 20px; right: 20px;">
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<img src="https://upload.wikimedia.org/wikipedia/commons/9/91/Octicons-mark-github.svg"
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alt="GitHub Logo"
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style="width: 32px; height: 32px;">
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</a>
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<div id="mybox">
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<form id="promptForm">
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<input id="promptText" type="text" placeholder="Enter your prompt here" value="a human standing on the surface of mars">
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<label>
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Steps: <span id="stepValue">9</span>
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<input id="stepRange" type="range" min="5" max="20" value="9" step="1">
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</label>
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<label>
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Guidance: <span id="guidanceValue">8.0</span>
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<input id="guidanceRange" type="range" min="3" max="15" value="8.0" step="0.1">
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</label>
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<input id="btnRunNet" type="button" value="Run" disabled>
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</form>
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<div id="divModelDl" style="display: flex; align-items: center; width: 100%; gap: 10px;">
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<span id="modelDlTitle">Downloading model</span>
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<progress id="modelDlProgressBar" value="0" max="100" style="flex-grow: 1;"></progress>
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<span id="modelDlProgressValue"></span>
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</div>
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<div id="divStepProgress" style="display: none; align-items: center; width: 100%; gap: 10px;">
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<progress id="progressBar" value="0" max="100" style="flex-grow: 1;"></progress>
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<span id="progressFraction"></span>
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</div>
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<div id="divStepTime" style="display: none; align-items: center; width: 100%; gap: 10px;">
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<span id="stepTimeValue">0 ms</span>
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</div>
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</div>
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<canvas id="canvas" width="512" height="512"></canvas>
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<script>
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let f16decomp = null;
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function initDb() {
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return new Promise((resolve, reject) => {
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let db;
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const request = indexedDB.open('tinydb', 1);
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request.onerror = (event) => {
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console.error('Database error:', event.target.error);
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resolve(null);
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};
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request.onsuccess = (event) => {
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db = event.target.result;
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console.log("Db initialized.");
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resolve(db);
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};
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request.onupgradeneeded = (event) => {
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db = event.target.result;
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if (!db.objectStoreNames.contains('tensors')) {
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db.createObjectStore('tensors', { keyPath: 'id' });
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}
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};
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});
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}
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function saveTensorToDb(db, id, tensor) {
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return readTensorFromDb(db, id).then((result) => {
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if (!result) {
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new Promise((resolve, reject) => {
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if (db == null) {
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resolve(null);
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}
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const transaction = db.transaction(['tensors'], 'readwrite');
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const store = transaction.objectStore('tensors');
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const request = store.put({ id: id, content: tensor });
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transaction.onabort = (event) => {
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console.log("Transaction error while saving tensor: " + event.target.error);
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resolve(null);
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};
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request.onsuccess = () => {
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console.log('Tensor saved successfully.');
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resolve();
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};
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request.onerror = (event) => {
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console.error('Tensor save failed:', event.target.error);
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resolve(null);
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};
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});
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} else {
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return null;
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}
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}).catch(()=> null);
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}
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function readTensorFromDb(db, id) {
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return new Promise((resolve, reject) => {
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if (db == null) {
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resolve(null);
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}
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const transaction = db.transaction(['tensors'], 'readonly');
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const store = transaction.objectStore('tensors');
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const request = store.get(id);
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transaction.onabort = (event) => {
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console.log("Transaction error while reading tensor: " + event.target.error);
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resolve(null);
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};
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request.onsuccess = (event) => {
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||||
const result = event.target.result;
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||||
if (result) {
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resolve(result);
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} else {
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resolve(null);
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}
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};
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request.onerror = (event) => {
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console.error('Tensor retrieve failed: ', event.target.error);
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resolve(null);
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};
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});
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}
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window.addEventListener('load', async function() {
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if (!navigator.gpu) {
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document.getElementById("wgpuError").style.display = "block";
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document.getElementById("sdTitle").style.display = "none";
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return;
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}
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let db = await initDb();
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const ctx = document.getElementById("canvas").getContext("2d", { willReadFrequently: true });
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let labels, nets, safetensorParts;
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const getDevice = async () => {
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const adapter = await navigator.gpu.requestAdapter();
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const requiredLimits = {};
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||||
const maxBufferSizeInSDModel = 1073741824;
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requiredLimits.maxStorageBufferBindingSize = maxBufferSizeInSDModel;
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requiredLimits.maxBufferSize = maxBufferSizeInSDModel;
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return await adapter.requestDevice({
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requiredLimits,
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requiredFeatures: ["shader-f16"],
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powerPreference: "high-performance"
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});
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||||
};
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const timer = async (func, label = "") => {
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const start = performance.now();
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const out = await func();
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const delta = (performance.now() - start).toFixed(1)
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||||
console.log(`${delta} ms ${label}`);
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return out;
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||||
}
|
||||
|
||||
const getProgressDlForPart = async (part, progressCallback) => {
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const response = await fetch(part);
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||||
const contentLength = response.headers.get('content-length');
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const total = parseInt(contentLength, 10);
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|
||||
const res = new Response(new ReadableStream({
|
||||
async start(controller) {
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||||
const reader = response.body.getReader();
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||||
for (;;) {
|
||||
const { done, value } = await reader.read();
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if (done) break;
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progressCallback(part, value.byteLength, total);
|
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controller.enqueue(value);
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||||
}
|
||||
|
||||
controller.close();
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||||
},
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||||
}));
|
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|
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return res.arrayBuffer();
|
||||
};
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|
||||
const getAndDecompressF16Safetensors = async (device, progress) => {
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let totalLoaded = 0;
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||||
let totalSize = 0;
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||||
let partSize = {};
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||||
|
||||
const getPart = async(key) => {
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let part = await readTensorFromDb(db, key);
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|
||||
if (part) {
|
||||
console.log(`Cache hit: ${key}`);
|
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return Promise.resolve(part.content);
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||||
} else {
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console.log(`Cache miss: ${key}`);
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return getProgressDlForPart(`${window.MODEL_BASE_URL}/${key}.safetensors`, progressCallback);
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}
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}
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const progressCallback = (part, loaded, total) => {
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totalLoaded += loaded;
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|
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if (!partSize[part]) {
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totalSize += total;
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partSize[part] = true;
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}
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progress(totalLoaded, totalSize);
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||||
};
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||||
|
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let netKeys = ["net_part0", "net_part1", "net_part2", "net_part3", "net_textmodel"];
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let buffers = await Promise.all(netKeys.map(key => getPart(key)));
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// Combine everything except for text model, since that's already f32
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const totalLength = buffers.reduce((acc, buffer, index, array) => {
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if (index < 4) {
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return acc + buffer.byteLength;
|
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} else {
|
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return acc;
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}
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}, 0
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);
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|
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combinedBuffer = new Uint8Array(totalLength);
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let offset = 0;
|
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buffers.forEach((buffer, index) => {
|
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saveTensorToDb(db, netKeys[index], new Uint8Array(buffer));
|
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if (index < 4) {
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combinedBuffer.set(new Uint8Array(buffer), offset);
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offset += buffer.byteLength;
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buffer = null;
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}
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});
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|
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let textModelU8 = new Uint8Array(buffers[4]);
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document.getElementById("modelDlTitle").innerHTML = "Decompressing model";
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const textModelOffset = 3772703308;
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const metadataLength = Number(new DataView(combinedBuffer.buffer).getBigUint64(0, true));
|
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const metadata = JSON.parse(new TextDecoder("utf8").decode(combinedBuffer.subarray(8, 8 + metadataLength)));
|
||||
|
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const allToDecomp = combinedBuffer.byteLength - (8 + metadataLength);
|
||||
const decodeChunkSize = 8388480;
|
||||
const numChunks = Math.ceil(allToDecomp/decodeChunkSize);
|
||||
|
||||
console.log(allToDecomp + " bytes to decompress");
|
||||
console.log("Will be decompressed in " + numChunks+ " chunks");
|
||||
|
||||
let partOffsets = [{start: 0, end: 1131408336}, {start: 1131408336, end: 2227518416}, {start: 2227518416, end: 3308987856}, {start: 3308987856, end: 4265298864}];
|
||||
let parts = [];
|
||||
|
||||
for (let offsets of partOffsets) {
|
||||
parts.push(new Uint8Array(offsets.end-offsets.start));
|
||||
}
|
||||
parts[0].set(new Uint8Array(new BigUint64Array([BigInt(metadataLength)]).buffer), 0);
|
||||
parts[0].set(combinedBuffer.subarray(8, 8 + metadataLength), 8);
|
||||
parts[3].set(textModelU8, textModelOffset+8+metadataLength - partOffsets[3].start);
|
||||
|
||||
let start = Date.now();
|
||||
let cursor = 0;
|
||||
|
||||
for (let i = 0; i < numChunks; i++) {
|
||||
progress(i, numChunks);
|
||||
let chunkStartF16 = 8 + metadataLength + (decodeChunkSize * i);
|
||||
let chunkEndF16 = chunkStartF16 + decodeChunkSize;
|
||||
let chunk = combinedBuffer.subarray(chunkStartF16, chunkEndF16);
|
||||
let uint32Chunk = new Uint32Array(chunk.buffer, chunk.byteOffset, chunk.byteLength / 4);
|
||||
let result = await f16decomp(uint32Chunk);
|
||||
let resultUint8 = new Uint8Array(result.buffer);
|
||||
let chunkStartF32 = 8 + metadataLength + (decodeChunkSize * i * 2);
|
||||
let chunkEndF32 = chunkStartF32 + resultUint8.byteLength;
|
||||
let offsetInPart = chunkStartF32 - partOffsets[cursor].start;
|
||||
|
||||
if (chunkEndF32 < partOffsets[cursor].end || cursor === parts.length - 1) {
|
||||
parts[cursor].set(resultUint8, offsetInPart);
|
||||
} else {
|
||||
let spaceLeftInCurrentPart = partOffsets[cursor].end - chunkStartF32;
|
||||
parts[cursor].set(resultUint8.subarray(0, spaceLeftInCurrentPart), offsetInPart);
|
||||
|
||||
cursor++;
|
||||
|
||||
if (cursor < parts.length) {
|
||||
let nextPartOffset = spaceLeftInCurrentPart;
|
||||
let nextPartLength = resultUint8.length - nextPartOffset;
|
||||
parts[cursor].set(resultUint8.subarray(nextPartOffset, nextPartOffset + nextPartLength), 0);
|
||||
}
|
||||
}
|
||||
|
||||
resultUint8 = null;
|
||||
result = null;
|
||||
}
|
||||
|
||||
combinedBuffer = null;
|
||||
|
||||
let end = Date.now();
|
||||
console.log("Decoding took: " + ((end - start) / 1000) + " s");
|
||||
console.log("Avarage " + ((end - start) / numChunks) + " ms per chunk");
|
||||
|
||||
return parts;
|
||||
};
|
||||
|
||||
const loadNet = async () => {
|
||||
const modelDlTitle = document.getElementById("modelDlTitle");
|
||||
|
||||
const progress = (loaded, total) => {
|
||||
document.getElementById("modelDlProgressBar").value = (loaded/total) * 100
|
||||
document.getElementById("modelDlProgressValue").innerHTML = Math.trunc((loaded/total) * 100) + "%"
|
||||
}
|
||||
|
||||
const device = await getDevice();
|
||||
f16decomp = await f16tof32().setup(device, safetensorParts),
|
||||
safetensorParts = await getAndDecompressF16Safetensors(device, progress);
|
||||
|
||||
modelDlTitle.innerHTML = "Compiling model"
|
||||
|
||||
let models = ["textModel", "diffusor", "decoder"];
|
||||
|
||||
nets = await timer(() => Promise.all([
|
||||
textModel().setup(device, safetensorParts),
|
||||
diffusor().setup(device, safetensorParts),
|
||||
decoder().setup(device, safetensorParts)
|
||||
]).then((loadedModels) => loadedModels.reduce((acc, model, index) => { acc[models[index]] = model; return acc; }, {})), "(compilation)")
|
||||
|
||||
progress(1, 1);
|
||||
|
||||
modelDlTitle.innerHTML = "Model ready"
|
||||
setTimeout(() => {
|
||||
document.getElementById("modelDlProgressBar").style.display = "none";
|
||||
document.getElementById("modelDlProgressValue").style.display = "none";
|
||||
document.getElementById("divStepProgress").style.display = "flex";
|
||||
}, 1000);
|
||||
document.getElementById("btnRunNet").disabled = false;
|
||||
}
|
||||
|
||||
function runStableDiffusion(prompt, steps, guidance, showStep) {
|
||||
return new Promise(async (resolve, reject) => {
|
||||
let context = await timer(() => nets["textModel"](clipTokenizer.encodeForCLIP(prompt)));
|
||||
let unconditional_context = await timer(() => nets["textModel"](clipTokenizer.encodeForCLIP("")));
|
||||
|
||||
let timesteps = [];
|
||||
|
||||
for (let i = 1; i < 1000; i += (1000/steps)) {
|
||||
timesteps.push(i);
|
||||
}
|
||||
|
||||
console.log("Timesteps: " + timesteps);
|
||||
|
||||
let alphasCumprod = getWeight(safetensorParts,"alphas_cumprod");
|
||||
let alphas = [];
|
||||
|
||||
for (t of timesteps) {
|
||||
alphas.push(alphasCumprod[Math.floor(t)]);
|
||||
}
|
||||
|
||||
alphas_prev = [1.0];
|
||||
|
||||
for (let i = 0; i < alphas.length-1; i++) {
|
||||
alphas_prev.push(alphas[i]);
|
||||
}
|
||||
|
||||
let inpSize = 4*64*64;
|
||||
latent = new Float32Array(inpSize);
|
||||
|
||||
for (let i = 0; i < inpSize; i++) {
|
||||
latent[i] = Math.sqrt(-2.0 * Math.log(Math.random())) * Math.cos(2.0 * Math.PI * Math.random());
|
||||
}
|
||||
|
||||
for (let i = timesteps.length - 1; i >= 0; i--) {
|
||||
let timestep = new Float32Array([timesteps[i]]);
|
||||
let start = performance.now()
|
||||
let x_prev = await nets["diffusor"](unconditional_context, context, latent, timestep, new Float32Array([alphas[i]]), new Float32Array([alphas_prev[i]]), new Float32Array([guidance]));
|
||||
document.getElementById("divStepTime").style.display = "block";
|
||||
document.getElementById("stepTimeValue").innerText = `${(performance.now() - start).toFixed(1)} ms / step`
|
||||
latent = x_prev;
|
||||
|
||||
if (showStep != null) {
|
||||
showStep(await nets["decoder"](latent));
|
||||
}
|
||||
|
||||
document.getElementById("progressBar").value = ((steps - i) / steps) * 100
|
||||
document.getElementById("progressFraction").innerHTML = (steps - i) + "/" + steps
|
||||
}
|
||||
|
||||
resolve(await timer(() => nets["decoder"](latent)));
|
||||
});
|
||||
}
|
||||
|
||||
function renderImage(image) {
|
||||
let pixels = []
|
||||
let pixelCounter = 0
|
||||
|
||||
for (let j = 0; j < 512; j++) {
|
||||
for (let k = 0; k < 512; k++) {
|
||||
pixels.push(image[pixelCounter])
|
||||
pixels.push(image[pixelCounter+1])
|
||||
pixels.push(image[pixelCounter+2])
|
||||
pixels.push(255)
|
||||
pixelCounter += 3
|
||||
}
|
||||
}
|
||||
|
||||
ctx.putImageData(new ImageData(new Uint8ClampedArray(pixels), 512, 512), 0, 0);
|
||||
}
|
||||
|
||||
const handleRunNetAndRenderResult = () => {
|
||||
document.getElementById("btnRunNet").disabled = true;
|
||||
const canvas = document.getElementById("canvas");
|
||||
ctx.clearRect(0, 0, canvas.width, canvas.height);
|
||||
|
||||
const prevTitleValue = document.getElementById("modelDlTitle").innerHTML;
|
||||
document.getElementById("modelDlTitle").innerHTML = "Running model";
|
||||
|
||||
runStableDiffusion(
|
||||
document.getElementById("promptText").value,
|
||||
document.getElementById("stepRange").value,
|
||||
document.getElementById("guidanceRange").value,
|
||||
// Decode at each step
|
||||
null
|
||||
).then((image) => {
|
||||
renderImage(image);
|
||||
}).finally(() => {
|
||||
document.getElementById("modelDlTitle").innerHTML = prevTitleValue;
|
||||
document.getElementById("btnRunNet").disabled = false;
|
||||
});
|
||||
};
|
||||
|
||||
document.getElementById("btnRunNet").addEventListener("click", handleRunNetAndRenderResult, false);
|
||||
|
||||
document.getElementById("promptForm").addEventListener("submit", function (event) {
|
||||
event.preventDefault();
|
||||
if (document.getElementById("btnRunNet").disabled) return;
|
||||
|
||||
handleRunNetAndRenderResult();
|
||||
})
|
||||
|
||||
const stepSlider = document.getElementById('stepRange');
|
||||
const stepValue = document.getElementById('stepValue');
|
||||
|
||||
stepSlider.addEventListener('input', function() {
|
||||
stepValue.textContent = stepSlider.value;
|
||||
});
|
||||
|
||||
const guidanceSlider = document.getElementById('guidanceRange');
|
||||
const guidanceValue = document.getElementById('guidanceValue');
|
||||
|
||||
guidanceSlider.addEventListener('input', function() {
|
||||
guidanceValue.textContent = guidanceSlider.value;
|
||||
});
|
||||
|
||||
loadNet();
|
||||
});
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
Reference in New Issue
Block a user