Agate WebGPU: static in-browser demo with ONNX models

README.md

@@ -1,10 +1,53 @@
 ---
-title: Agate Webgpu
-emoji: 🦀
-colorFrom: pink
-colorTo: green
+title: Agate WebGPU
+colorFrom: red
+colorTo: gray
 sdk: static
+app_file: index.html
 pinned: false
+license: mit
+short_description: Agate text-to-image, 100% in your browser on WebGPU
+custom_headers:
+  cross-origin-embedder-policy: require-corp
+  cross-origin-opener-policy: same-origin
+  cross-origin-resource-policy: cross-origin
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Agate WebGPU
+
+Agate Preview 001, LogoLabs' 0.26B text-to-image model (a 191M flow generator with a 68M text encoder),
+running as a static page. The tokenizer, text encoder, 50-step flow sampler with classifier-free guidance
+and the TAESD decoder all run on your own GPU through WebGPU (onnxruntime-web).
+
+**Runs entirely in your browser — prompts never leave your machine.** There is no server and no API call.
+
+## Requirements
+
+- A browser with WebGPU: a recent Chrome or Edge (113+). Safari and Firefox may lack WebGPU or need it
+  enabled in their settings; without it the page offers a WebAssembly (CPU) path that works but takes
+  minutes per image.
+- First load downloads about **530 MB** of weights (generator 385 MB, text encoder 137 MB, decoder 5 MB,
+  tokenizer 4 MB). They are kept in the browser's Cache Storage, so later visits load from disk.
+
+## Speed and accuracy
+
+- About 6–8 s per 50-step 256×256 image on an RTX 4060, after roughly 10 s of one-time model
+  setup per visit.
+- With identical initial noise, the browser output matches the PyTorch reference pipeline within 5/255
+  per pixel (run the self-test with `?parity=1`).
+- Seeds use a JavaScript PRNG, so a seed gives the same image in every browser but not the same image as
+  the PyTorch pipeline with that seed.
+
+Query parameters: `?autoload=1` loads the model immediately, `?ep=wasm` forces the CPU backend,
+`?parity=1` runs the parity self-test.
+
+## Links
+
+- [logolabs.org](https://logolabs.org)
+- [huggingface.co/Logolabs](https://huggingface.co/Logolabs)
+- Model card: Logolabs/agate-preview-001
+
+Licence: MIT.
+
+We acknowledge EuroHPC JU for awarding the project ID EHPC-AIF-2026PG01-907 access to resources on
+Arrhenius GPU at NAISS, Sweden.

css/style.css

@@ -0,0 +1,97 @@
+:root {
+  --paper: #FCFCF7;
+  --ink: #0B0A08;
+  --red: #F4291F;
+  --mute: #6B6862;
+  --hair: 1.5px solid var(--ink);
+  --mono: "IBM Plex Mono", ui-monospace, Menlo, Consolas, monospace;
+  --sans: "IBM Plex Sans", system-ui, -apple-system, "Segoe UI", sans-serif;
+  --head: "Inter Tight", "Inter", system-ui, sans-serif;
+}
+* { box-sizing: border-box; }
+[hidden] { display: none !important; }
+html, body { margin: 0; background: var(--paper); color: var(--ink); }
+body { font: 16px/1.5 var(--sans); -webkit-font-smoothing: antialiased; }
+a { color: inherit; text-decoration-thickness: 1.5px; text-underline-offset: 3px; }
+a:hover { color: var(--red); }
+
+.page { max-width: 1080px; margin: 0 auto; padding: 28px 24px 40px; }
+.rule { border: 0; border-top: var(--hair); margin: 22px 0; }
+
+.label { font: 500 11.5px/1.3 var(--mono); letter-spacing: .14em; text-transform: uppercase; display: inline-flex; align-items: center; gap: 8px; }
+.label.dim { color: var(--mute); }
+.label b { font-weight: 600; color: var(--red); letter-spacing: .06em; }
+.sq { display: inline-block; width: 8px; height: 8px; background: var(--ink); flex: none; }
+.sq.red { background: var(--red); }
+
+.mast { display: flex; justify-content: space-between; align-items: flex-end; gap: 16px; }
+.mast-left { display: flex; align-items: flex-end; gap: 22px; }
+.lockup { height: 96px; width: auto; display: block; }
+.mast-meta { display: flex; flex-direction: column; gap: 6px; padding-bottom: 6px; }
+.imprint { display: flex; align-items: center; gap: 10px; text-decoration: none; padding-bottom: 6px; }
+
+.intro h1 { font: 800 clamp(34px, 5.4vw, 58px)/1.02 var(--head); letter-spacing: -.02em; margin: 6px 0 12px; }
+.intro p { max-width: 62ch; margin: 0; color: #2a2824; }
+
+.notice { border: var(--hair); padding: 16px 18px; margin: 22px 0 0; background: #fff; }
+.notice p { margin: 8px 0; }
+.notice.ok { border-color: var(--ink); }
+
+.grid { display: grid; grid-template-columns: minmax(0, 1fr) minmax(0, 1fr); gap: 28px; margin-top: 26px; }
+@media (max-width: 820px) { .grid { grid-template-columns: 1fr; } .lockup { height: 72px; } }
+
+.panel { border-top: var(--hair); padding-top: 14px; }
+textarea, input[type=number] {
+  width: 100%; font: 16px/1.45 var(--sans); color: var(--ink); background: #fff;
+  border: var(--hair); border-radius: 0; padding: 10px 12px; margin-top: 8px; resize: vertical;
+}
+textarea:focus, input:focus { outline: 2px solid var(--red); outline-offset: 1px; }
+
+.examples { display: flex; flex-wrap: wrap; gap: 6px; align-items: center; margin: 10px 0 18px; }
+.chip { font: 13px/1.2 var(--sans); background: transparent; border: 1px solid var(--ink); padding: 5px 9px; cursor: pointer; color: var(--ink); text-align: left; }
+.chip:hover { background: var(--ink); color: var(--paper); }
+
+.row { display: grid; grid-template-columns: 1.1fr 1fr 1fr; gap: 16px; margin-bottom: 20px; }
+@media (max-width: 480px) { .row { grid-template-columns: 1fr; } }
+.field { display: flex; flex-direction: column; }
+.seed-row { display: flex; gap: 6px; align-items: stretch; }
+.seed-row input { margin-top: 8px; }
+input[type=range] { width: 100%; margin-top: 16px; accent-color: var(--red); }
+
+.btn { font: 600 13px/1 var(--mono); letter-spacing: .14em; text-transform: uppercase; border: var(--hair); background: var(--paper); color: var(--ink); padding: 14px 18px; cursor: pointer; border-radius: 0; text-decoration: none; display: inline-flex; align-items: center; justify-content: center; }
+.btn.primary { width: 100%; background: var(--ink); color: var(--paper); }
+.btn.primary:hover:not(:disabled) { background: var(--red); border-color: var(--red); }
+.btn:disabled { opacity: .45; cursor: default; }
+.btn.icon { margin-top: 8px; padding: 0 12px; font-size: 18px; letter-spacing: 0; }
+.btn.ghost:hover { background: var(--ink); color: var(--paper); }
+.btn.small { padding: 9px 12px; font-size: 11px; }
+
+.progress { margin-top: 16px; }
+.bar { height: 8px; border: var(--hair); background: #fff; }
+#bar-fill { height: 100%; width: 0; background: var(--red); transition: width .12s linear; }
+.progress-text { display: flex; justify-content: space-between; margin-top: 8px; gap: 12px; }
+
+.out-head, .out-foot { display: flex; justify-content: space-between; align-items: center; gap: 12px; }
+.out-foot { align-items: flex-start; margin-top: 12px; }
+.toggle { cursor: pointer; }
+.toggle input { accent-color: var(--red); margin: 0; }
+.frame { position: relative; margin-top: 10px; border: var(--hair); background: #fff; aspect-ratio: 1 / 1; width: 100%; }
+#canvas { width: 100%; height: 100%; display: block; image-rendering: auto; }
+.frame.crisp #canvas { image-rendering: pixelated; }
+.placeholder { position: absolute; inset: 0; display: grid; place-items: center; background: var(--paper); }
+.placeholder img { width: 28%; opacity: .14; }
+.frame.busy .placeholder img { animation: pulse 1.2s ease-in-out infinite; }
+@keyframes pulse { 50% { opacity: .32; } }
+
+.timings { display: grid; grid-template-columns: auto auto; gap: 2px 14px; margin: 0; font: 12px/1.5 var(--mono); }
+.timings dt { color: var(--mute); text-transform: uppercase; letter-spacing: .1em; font-size: 11px; }
+.timings dd { margin: 0; }
+
+.foot p { margin: 0 0 8px; }
+.foot .strong { font: 700 18px/1.3 var(--head); }
+.foot .label { display: flex; flex-wrap: wrap; gap: 4px 8px; }
+@media (max-width: 520px) {
+  .page { padding: 20px 16px 32px; }
+  .mast-meta .label.dim { display: none; }
+  .progress-text { flex-wrap: wrap; }
+}

index.html

@@ -1,19 +1,113 @@
 <!doctype html>
-<html>
-	<head>
-		<meta charset="utf-8" />
-		<meta name="viewport" content="width=device-width" />
-		<title>My static Space</title>
-		<link rel="stylesheet" href="style.css" />
-	</head>
-	<body>
-		<div class="card">
-			<h1>Welcome to your static Space!</h1>
-			<p>You can modify this app directly by editing <i>index.html</i> in the Files and versions tab.</p>
-			<p>
-				Also don't forget to check the
-				<a href="https://huggingface.co/docs/hub/spaces" target="_blank">Spaces documentation</a>.
-			</p>
-		</div>
-	</body>
+<html lang="en">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width, initial-scale=1">
+<title>Agate Preview 001 — in your browser</title>
+<meta name="description" content="Agate Preview 001, LogoLabs' 0.26B text-to-image model, running entirely in your browser on WebGPU.">
+<link rel="icon" href="assets/agate-a.svg" type="image/svg+xml">
+<link rel="preconnect" href="https://fonts.googleapis.com">
+<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+<link crossorigin="anonymous" href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;500;600&family=IBM+Plex+Sans:wght@400;500;600&family=Inter+Tight:wght@700;800&display=swap" rel="stylesheet">
+<link rel="stylesheet" href="css/style.css">
+</head>
+<body>
+<div class="page">
+  <header class="mast">
+    <div class="mast-left">
+      <img class="lockup" src="assets/agate-lockup.svg" alt="Agate">
+      <div class="mast-meta">
+        <span class="label"><i class="sq"></i>Preview 001</span>
+        <span class="label dim">0.26B · text-to-image · 256 px</span>
+      </div>
+    </div>
+    <a class="imprint" href="https://logolabs.org" target="_blank" rel="noopener">
+      <img src="assets/logolabs-mark.svg" alt="" width="22" height="30">
+      <span class="label">LogoLabs</span>
+    </a>
+  </header>
+  <hr class="rule">
+
+  <section class="intro">
+    <h1>Agate, in your browser.</h1>
+    <p>A 191M-parameter flow model with a 68M text encoder, running on your own GPU through WebGPU.
+      The first visit downloads the weights once (<span id="dl-size">530 MB</span>); after that they load from the browser cache.</p>
+  </section>
+
+  <div id="nogpu" class="notice" hidden>
+    <span class="label"><i class="sq red"></i>WebGPU unavailable</span>
+    <p id="nogpu-why"></p>
+    <p>Agate needs WebGPU for usable speed: use a recent Chrome or Edge (113+) on Windows, macOS or ChromeOS,
+      or Chrome on Android 121+; on Linux and in Firefox/Safari WebGPU may have to be enabled in the browser's flags.
+      You can still run it on the CPU (WebAssembly) — correct, but several minutes per image.</p>
+    <button id="use-wasm" class="btn ghost">Run on CPU instead</button>
+  </div>
+
+  <main class="grid">
+    <section class="panel controls">
+      <label class="label" for="prompt">Prompt</label>
+      <textarea id="prompt" rows="3" spellcheck="false">a green teapot and a red cup on a table</textarea>
+      <div class="examples" id="examples">
+        <span class="label dim">Try</span>
+      </div>
+
+      <div class="row">
+        <div class="field">
+          <label class="label" for="seed">Seed</label>
+          <div class="seed-row">
+            <input id="seed" type="number" min="0" max="4294967295" step="1" value="0">
+            <button id="dice" class="btn icon" title="Random seed" aria-label="Random seed">⟳</button>
+          </div>
+        </div>
+        <div class="field">
+          <label class="label" for="steps">Steps <b id="steps-v">50</b></label>
+          <input id="steps" type="range" min="4" max="50" step="1" value="50">
+        </div>
+        <div class="field">
+          <label class="label" for="cfg">CFG <b id="cfg-v">3.0</b></label>
+          <input id="cfg" type="range" min="1" max="8" step="0.5" value="3">
+        </div>
+      </div>
+
+      <button id="go" class="btn primary" disabled>Load model</button>
+
+      <div class="progress" aria-live="polite">
+        <div class="bar"><div id="bar-fill"></div></div>
+        <div class="progress-text"><span id="status" class="label">Idle</span><span id="status-r" class="label dim"></span></div>
+      </div>
+    </section>
+
+    <section class="panel output">
+      <div class="out-head">
+        <span class="label"><i class="sq"></i>Output · 256 × 256</span>
+        <label class="label toggle"><input type="checkbox" id="crisp"> Crisp pixels</label>
+      </div>
+      <div class="frame" id="frame">
+        <canvas id="canvas" width="256" height="256"></canvas>
+        <div class="placeholder" id="placeholder">
+          <img src="assets/agate-a.svg" alt="">
+        </div>
+      </div>
+      <div class="out-foot">
+        <dl class="timings" id="timings"></dl>
+        <a id="save" class="btn ghost small" download="agate.png" hidden>Save PNG</a>
+      </div>
+    </section>
+  </main>
+
+  <div id="parity" class="notice" hidden></div>
+
+  <hr class="rule">
+  <footer class="foot">
+    <p class="strong">Runs entirely in your browser on WebGPU — nothing leaves your machine.</p>
+    <p class="label dim">
+      <a href="https://logolabs.org" target="_blank" rel="noopener">logolabs.org</a> ·
+      <a href="https://huggingface.co/Logolabs" target="_blank" rel="noopener">huggingface.co/Logolabs</a> ·
+      MIT licence · <span id="backend">backend: —</span>
+    </p>
+  </footer>
+</div>
+<script src="js/config.js"></script>
+<script type="module" src="js/app.js"></script>
+</body>
 </html>

js/agate.js

@@ -0,0 +1,209 @@
+// Agate Preview 001 in the browser: tokenizer (tokenizers.js) + three ONNX graphs run by
+// onnxruntime-web (WebGPU, WASM fallback) + a 50-step Euler flow sampler with CFG.
+// Mirrors agate/pipeline.py (AgatePipeline) of Logolabs/agate-preview-001.
+
+import * as ort from "https://cdn.jsdelivr.net/npm/onnxruntime-web@1.30.0/dist/ort.webgpu.bundle.min.mjs";
+import { Tokenizer } from "https://cdn.jsdelivr.net/npm/@huggingface/tokenizers@0.2.0/dist/tokenizers.mjs";
+
+export { ort };
+
+const BUCKETS = [64, 128, 256, 512];
+const CACHE_NAME = "agate-preview-001-web";
+const MODEL_FILES = ["tokenizer.json", "tokenizer_config.json", "text_encoder.onnx", "generator.onnx", "taesd_decoder.onnx"];
+
+export async function webgpuStatus() {
+  if (!("gpu" in navigator)) return { ok: false, why: "This browser does not expose WebGPU (navigator.gpu is missing)." };
+  try {
+    const adapter = await navigator.gpu.requestAdapter({ powerPreference: "high-performance" });
+    if (!adapter) return { ok: false, why: "WebGPU is present but no GPU adapter is available." };
+    let name = "";
+    try { const info = adapter.info || (await adapter.requestAdapterInfo?.()); name = [info?.vendor, info?.architecture, info?.description].filter(Boolean).join(" "); } catch { /* optional */ }
+    return { ok: true, adapter: name, maxBuffer: adapter.limits?.maxBufferSize };
+  } catch (e) {
+    return { ok: false, why: `WebGPU adapter request failed: ${e.message || e}` };
+  }
+}
+
+// ---- downloads with progress + Cache Storage ---------------------------------------------------
+async function openCache() {
+  try { return await caches.open(CACHE_NAME); } catch { return null; }   // file://, private mode, ...
+}
+
+async function fetchCached(url, key, expectBytes, onBytes) {
+  const cache = await openCache();
+  if (cache) {
+    const hit = await cache.match(key);
+    if (hit) { const buf = new Uint8Array(await hit.arrayBuffer()); onBytes(buf.byteLength, true); return { buf, cached: true }; }
+  }
+  const res = await fetch(url);
+  if (!res.ok) throw new Error(`${url}: HTTP ${res.status}`);
+  // manifest size first: a compressed response would report the encoded length
+  const total = expectBytes || Number(res.headers.get("content-length")) || 0;
+  const reader = res.body.getReader();
+  let buf = new Uint8Array(total || 1 << 20), n = 0;
+  for (;;) {
+    const { done, value } = await reader.read();
+    if (done) break;
+    if (n + value.byteLength > buf.byteLength) { const b2 = new Uint8Array(Math.max(buf.byteLength * 2, n + value.byteLength)); b2.set(buf.subarray(0, n)); buf = b2; }
+    buf.set(value, n); n += value.byteLength; onBytes(value.byteLength, false);
+  }
+  buf = n === buf.byteLength ? buf : buf.slice(0, n);
+  if (cache) {
+    try { await cache.put(key, new Response(buf, { headers: { "content-type": "application/octet-stream", "content-length": String(n) } })); }
+    catch (e) { console.warn("cache put failed (quota?)", e); }
+  }
+  return { buf, cached: false };
+}
+
+export async function hasCachedModel() {
+  try { const c = await caches.open(CACHE_NAME); return (await c.keys()).length >= MODEL_FILES.length; } catch { return false; }
+}
+
+export async function clearCache() { try { return await caches.delete(CACHE_NAME); } catch { return false; } }
+
+// ---- the model ---------------------------------------------------------------------------------
+export class Agate {
+  constructor({ base = "./models/", ep = "webgpu", variant = null, optLevel = "all" } = {}) {
+    this.optLevel = optLevel;
+    this.base = base.endsWith("/") ? base : base + "/";
+    this.ep = ep;
+    this.variant = variant;          // e.g. "full16": fp16-compute generator (experimental)
+  }
+
+  // onProgress({loaded, total, file, phase})
+  async load(onProgress = () => {}) {
+    const manifest = await (await fetch(this.base + "manifest.json", { cache: "no-cache" })).json();
+    this.manifest = manifest;
+    const swap = (this.variant && manifest.variants?.[this.variant]) || {};
+    const real = (f) => swap[f] || f;
+    const total = MODEL_FILES.reduce((s, f) => s + manifest.files[real(f)].bytes, 0);
+    let loaded = 0, fromCache = 0;
+    const bufs = {};
+    const t0 = performance.now();
+    for (const f of MODEL_FILES) {
+      const meta = manifest.files[real(f)];
+      const key = new Request(`${this.base}${real(f)}?sha256=${meta.sha256}`);
+      const { buf, cached } = await fetchCached(this.base + real(f), key, meta.bytes, (b, c) => {
+        loaded += b; if (c) fromCache += b; onProgress({ phase: "download", file: f, loaded, total });
+      });
+      bufs[f] = buf;
+    }
+    this.stats = { downloadMB: total / 1e6, fromCacheMB: fromCache / 1e6, downloadMs: performance.now() - t0 };
+    // drop cached files of older model versions (keys carry the sha256)
+    try {
+      const cache = await openCache(), keep = new Set(MODEL_FILES.map((f) => new URL(`${this.base}${real(f)}?sha256=${manifest.files[real(f)].sha256}`, location.href).href));
+      if (cache) for (const req of await cache.keys()) if (!keep.has(req.url)) await cache.delete(req);
+    } catch (e) { console.warn("cache cleanup", e); }
+    const dec = new TextDecoder();
+    this.tok = new Tokenizer(JSON.parse(dec.decode(bufs["tokenizer.json"])), JSON.parse(dec.decode(bufs["tokenizer_config.json"])));
+
+    if (this.ep === "wasm") {
+      ort.env.wasm.numThreads = self.crossOriginIsolated ? Math.min(8, navigator.hardwareConcurrency || 4) : 1;
+    }
+    const opts = { executionProviders: [this.ep], graphOptimizationLevel: this.optLevel };
+    const t1 = performance.now();
+    this.sessions = {};
+    for (const [name, f] of [["text", "text_encoder.onnx"], ["gen", "generator.onnx"], ["vae", "taesd_decoder.onnx"]]) {
+      onProgress({ phase: "init", file: f, loaded: total, total });
+      const ts = performance.now();
+      this.sessions[name] = await ort.InferenceSession.create(bufs[f], opts);
+      this.stats[`${name}SessionMs`] = performance.now() - ts;
+      bufs[f] = null;
+    }
+    this.stats.sessionMs = performance.now() - t1;
+    return this.stats;
+  }
+
+  tokenize(text) {
+    const max = this.manifest.text_max_len;
+    let ids = this.tok.encode(text).ids;                 // [CLS] ... [SEP], as the HF tokenizer
+    if (ids.length > max) ids = ids.slice(0, max - 1).concat(ids[ids.length - 1]);   // truncation=True
+    return ids;
+  }
+
+  async encode(text) {
+    const ids = this.tokenize(text);
+    const n = ids.length;
+    const feeds = {
+      input_ids: new ort.Tensor("int64", BigInt64Array.from(ids, BigInt), [1, n]),
+      attention_mask: new ort.Tensor("int64", new BigInt64Array(n).fill(1n), [1, n]),
+    };
+    const out = await this.sessions.text.run(feeds);
+    const h = out.last_hidden_state;
+    const data = h.data.slice();                          // (1, n, 512) fp32
+    h.dispose?.();
+    return { data, n };
+  }
+
+  static gaussianNoise(seed, count) {
+    // mulberry32 + Box-Muller. Seeds do NOT reproduce PyTorch's torch.randn stream.
+    let a = seed >>> 0;
+    const rnd = () => { a |= 0; a = (a + 0x6D2B79F5) | 0; let t = Math.imul(a ^ (a >>> 15), 1 | a); t = (t + Math.imul(t ^ (t >>> 7), 61 | t)) ^ t; return ((t ^ (t >>> 14)) >>> 0) / 4294967296; };
+    const out = new Float32Array(count);
+    for (let i = 0; i < count; i += 2) {
+      const u1 = Math.max(rnd(), 1e-12), u2 = rnd();
+      const r = Math.sqrt(-2 * Math.log(u1));
+      out[i] = r * Math.cos(2 * Math.PI * u2);
+      if (i + 1 < count) out[i + 1] = r * Math.sin(2 * Math.PI * u2);
+    }
+    return out;
+  }
+
+  // -> { rgba: Uint8ClampedArray (256*256*4), latent: Float32Array, timings }
+  async generate({ prompt, negative = "", seed = 0, steps = 50, cfg = 3.0, noise = null, onStep = () => {}, shouldStop = () => false }) {
+    const hw = this.manifest.latent_hw, C = this.manifest.latent_ch, D = this.manifest.ctx_dim;
+    const per = C * hw * hw;
+    const T = { };
+    let t0 = performance.now();
+    const c = await this.encode(prompt), u = await this.encode(negative);
+    T.textMs = performance.now() - t0;
+    const need = Math.max(c.n, u.n);
+    const L = BUCKETS.find((b) => b >= need) ?? BUCKETS[BUCKETS.length - 1];
+    const ctx = new Float32Array(2 * L * D), mask = new Float32Array(2 * L);
+    ctx.set(c.data, 0); ctx.set(u.data, L * D);           // zero padding, as F.pad in the pipeline
+    mask.fill(1, 0, c.n); mask.fill(1, L, L + u.n);
+    const ctxT = new ort.Tensor("float32", ctx, [2, L, D]), maskT = new ort.Tensor("float32", mask, [2, L]);
+
+    let z = noise ? Float32Array.from(noise) : Agate.gaussianNoise(seed, per);
+    const zz = new Float32Array(2 * per), tt = new Float32Array(2);
+    const dt = 1 / steps;
+    t0 = performance.now();
+    const stepMs = [];
+    for (let i = 0; i < steps; i++) {
+      if (shouldStop()) throw new Error("stopped");
+      const ts = performance.now();
+      zz.set(z, 0); zz.set(z, per);
+      tt[0] = tt[1] = i * dt;
+      const out = await this.sessions.gen.run({
+        z: new ort.Tensor("float32", zz, [2, C, hw, hw]), t: new ort.Tensor("float32", tt, [2]), ctx: ctxT, mask: maskT,
+      });
+      const v = out.v.data;
+      const zn = new Float32Array(per);
+      for (let k = 0; k < per; k++) { const vc = v[k], vu = v[per + k]; zn[k] = z[k] + dt * (vu + cfg * (vc - vu)); }
+      out.v.dispose?.();
+      z = zn;
+      stepMs.push(performance.now() - ts);
+      onStep(i + 1, steps, z);
+      await new Promise((r) => setTimeout(r, 0));          // let the progress bar paint
+    }
+    T.samplerMs = performance.now() - t0;
+    T.firstStepMs = stepMs[0];
+    T.stepMs = stepMs.length > 1 ? (T.samplerMs - stepMs[0]) / (stepMs.length - 1) : stepMs[0];
+
+    t0 = performance.now();
+    const img = await this.sessions.vae.run({ latent: new ort.Tensor("float32", z, [1, C, hw, hw]) });
+    const x = img.image.data, H = img.image.dims[2], W = img.image.dims[3];
+    const rgba = new Uint8ClampedArray(H * W * 4);
+    for (let p = 0; p < H * W; p++) {
+      for (let ch = 0; ch < 3; ch++) {
+        const val = Math.min(1, Math.max(-1, x[ch * H * W + p]));
+        rgba[p * 4 + ch] = Math.round((val + 1) * 127.5);
+      }
+      rgba[p * 4 + 3] = 255;
+    }
+    T.decodeMs = performance.now() - t0;
+    T.totalMs = T.textMs + T.samplerMs + T.decodeMs;
+    T.bucket = L;
+    return { rgba, width: W, height: H, latent: z, timings: T };
+  }
+}

js/app.js

@@ -0,0 +1,163 @@
+import { Agate, webgpuStatus, clearCache, hasCachedModel } from "./agate.js";
+
+// Model location: ?models=<url> overrides; otherwise window.AGATE_MODEL_BASE (js/config.js); default ./models/
+const params = new URLSearchParams(location.search);
+const BASE = params.get("models") || window.AGATE_MODEL_BASE || "./models/";
+const FORCE_EP = params.get("ep");            // "wasm" forces the CPU path
+const PARITY = params.has("parity");
+const VARIANT = params.get("variant");      // "full16": experimental fp16-compute generator
+
+const EXAMPLES = [
+  "a green teapot and a red cup on a table",
+  "a minimalist logo of a fox head, orange, flat design, white background",
+  "a dog sitting to the left of a cat",
+  "a geometric mountain logo in blue and white",
+  "a red cube on top of a blue sphere",
+];
+
+const $ = (id) => document.getElementById(id);
+const ui = {
+  prompt: $("prompt"), seed: $("seed"), steps: $("steps"), cfg: $("cfg"), go: $("go"), dice: $("dice"),
+  fill: $("bar-fill"), status: $("status"), statusR: $("status-r"), canvas: $("canvas"), frame: $("frame"),
+  placeholder: $("placeholder"), timings: $("timings"), save: $("save"), crisp: $("crisp"), backend: $("backend"),
+};
+
+let agate = null, ep = FORCE_EP || "webgpu", busy = false, stop = false;
+window.__agate = { state: "idle" };           // read by the automated browser test
+
+for (const p of EXAMPLES) {
+  const b = document.createElement("button");
+  b.className = "chip"; b.textContent = p; b.type = "button";
+  b.onclick = () => { ui.prompt.value = p; };
+  $("examples").appendChild(b);
+}
+ui.steps.oninput = () => { $("steps-v").textContent = ui.steps.value; };
+ui.cfg.oninput = () => { $("cfg-v").textContent = Number(ui.cfg.value).toFixed(1); };
+ui.dice.onclick = () => { ui.seed.value = Math.floor(Math.random() * 2 ** 31); };
+try { ui.crisp.checked = localStorage.getItem("agate-crisp") === "1"; } catch { /* storage blocked */ }
+const applyCrisp = () => { ui.frame.classList.toggle("crisp", ui.crisp.checked); try { localStorage.setItem("agate-crisp", ui.crisp.checked ? "1" : "0"); } catch { /* */ } };
+ui.crisp.onchange = applyCrisp; applyCrisp();
+
+const mb = (b) => (b / 1e6).toFixed(0);
+function setProgress(frac, left, right = "") {
+  ui.fill.style.width = `${Math.max(0, Math.min(1, frac)) * 100}%`;
+  ui.status.textContent = left; ui.statusR.textContent = right;
+}
+function showTimings(rows) {
+  ui.timings.innerHTML = rows.map(([k, v]) => `<dt>${k}</dt><dd>${v}</dd>`).join("");
+}
+
+async function load() {
+  busy = true; ui.go.disabled = true;
+  window.__agate.state = "loading";
+  agate = new Agate({ base: BASE, ep, variant: VARIANT, optLevel: params.get("opt") || "all" });
+  try {
+    const t0 = performance.now();
+    const st = await agate.load(({ phase, file, loaded, total }) => {
+      if (phase === "download") setProgress(loaded / total, `Downloading ${file}`, `${mb(loaded)} / ${mb(total)} MB`);
+      else setProgress(1, `Preparing ${file} on ${ep === "webgpu" ? "WebGPU" : "CPU"}`, `${mb(total)} MB`);
+    });
+    const loadMs = performance.now() - t0;
+    $("dl-size").textContent = `${st.downloadMB.toFixed(0)} MB`;
+    ui.backend.textContent = `backend: ${ep === "webgpu" ? "WebGPU" : "WebAssembly (CPU)"}`;
+    const src = st.fromCacheMB > st.downloadMB * 0.99 ? "from browser cache" : "downloaded";
+    setProgress(0, "Ready", `${st.downloadMB.toFixed(0)} MB ${src} · ${(loadMs / 1000).toFixed(1)} s`);
+    window.__agate = { state: "ready", load: { ...st, loadMs, ep } };
+    ui.go.textContent = "Generate"; ui.go.disabled = false;
+  } catch (e) {
+    console.error(e);
+    setProgress(0, "Load failed", String(e.message || e));
+    window.__agate = { state: "error", error: String(e.message || e) };
+    ui.go.textContent = "Retry load"; ui.go.disabled = false; agate = null;
+  }
+  busy = false;
+}
+
+async function generate(opts = {}) {
+  busy = true; stop = false;
+  ui.go.textContent = "Stop"; ui.frame.classList.add("busy");
+  window.__agate.state = "generating";
+  const steps = opts.steps ?? Number(ui.steps.value);
+  try {
+    setProgress(0, "Encoding prompt", "");
+    const r = await agate.generate({
+      prompt: opts.prompt ?? ui.prompt.value.trim(), seed: Number(ui.seed.value) >>> 0, steps,
+      cfg: opts.cfg ?? Number(ui.cfg.value), noise: opts.noise ?? null,
+      onStep: (i, n) => setProgress(i / n, `Step ${i} / ${n}`, ""),
+      shouldStop: () => stop,
+    });
+    const ctx = ui.canvas.getContext("2d");
+    ui.canvas.width = r.width; ui.canvas.height = r.height;
+    ctx.putImageData(new ImageData(r.rgba, r.width, r.height), 0, 0);
+    ui.placeholder.hidden = true;
+    const T = r.timings;
+    setProgress(1, "Done", `${(T.totalMs / 1000).toFixed(1)} s`);
+    showTimings([
+      ["text encode", `${T.textMs.toFixed(0)} ms (bucket ${T.bucket})`],
+      ["per step", `${T.stepMs.toFixed(0)} ms × ${steps} (first ${T.firstStepMs.toFixed(0)} ms)`],
+      ["decode", `${T.decodeMs.toFixed(0)} ms`],
+      ["total", `${(T.totalMs / 1000).toFixed(2)} s`],
+    ]);
+    ui.save.href = ui.canvas.toDataURL("image/png"); ui.save.hidden = false;
+    window.__agate = { ...window.__agate, state: "done", timings: T };
+    return r;
+  } catch (e) {
+    if (String(e.message) === "stopped") setProgress(0, "Stopped", "");
+    else { console.error(e); setProgress(0, "Error", String(e.message || e)); window.__agate.error = String(e.message || e); }
+    window.__agate.state = "done";
+  } finally {
+    busy = false; ui.go.textContent = "Generate"; ui.frame.classList.remove("busy");
+  }
+}
+
+ui.go.onclick = async () => {
+  if (busy && agate?.sessions) { stop = true; return; }
+  if (busy) return;
+  if (!agate) return load();
+  return generate();
+};
+ui.prompt.addEventListener("keydown", (e) => { if (e.key === "Enter" && (e.ctrlKey || e.metaKey) && agate && !busy) generate(); });
+
+// Parity self-test: index.html?parity=1 runs the fixture exported by export/parity_full.py
+async function parity() {
+  const fx = await (await fetch("test/parity.json")).json();
+  const bin = async (f) => new Uint8Array(await (await fetch(`test/${f}`)).arrayBuffer());
+  const noise = new Float32Array((await bin("parity_noise.bin")).buffer);
+  const refZ = new Float32Array((await bin("parity_latent.bin")).buffer);
+  const refImg = await bin("parity_image.bin");     // HWC uint8
+  ui.prompt.value = fx.prompt; ui.steps.value = fx.steps; ui.cfg.value = fx.cfg;
+  const r = await generate({ prompt: fx.prompt, steps: fx.steps, cfg: fx.cfg, noise });
+  let dz = 0, dzs = 0, di = 0, dis = 0;
+  for (let k = 0; k < refZ.length; k++) { const d = Math.abs(r.latent[k] - refZ[k]); dz = Math.max(dz, d); dzs += d; }
+  for (let p = 0; p < refImg.length / 3; p++) for (let c = 0; c < 3; c++) {
+    const d = Math.abs(r.rgba[p * 4 + c] - refImg[p * 3 + c]); di = Math.max(di, d); dis += d;
+  }
+  const res = { latentMaxAbs: dz, latentMeanAbs: dzs / refZ.length, imageMaxAbs: di, imageMeanAbs: dis / refImg.length, ep, timings: r.timings };
+  const el = $("parity"); el.hidden = false;
+  el.innerHTML = `<span class="label"><i class="sq red"></i>Parity vs PyTorch pipeline (${ep})</span>
+    <p>final latent max |Δ| ${dz.toFixed(4)} (mean ${res.latentMeanAbs.toFixed(5)}) · image max |Δ| ${di}/255 (mean ${res.imageMeanAbs.toFixed(3)})</p>`;
+  window.__agate = { ...window.__agate, parity: res, state: "parity-done" };
+}
+
+(async () => {
+  if (!FORCE_EP) {
+    const s = await webgpuStatus();
+    if (!s.ok) {
+      ep = "wasm";
+      $("nogpu").hidden = false; $("nogpu-why").textContent = s.why;
+      ui.go.disabled = true; ui.go.textContent = "WebGPU required";
+      window.__agate = { state: "no-webgpu", why: s.why };
+      $("use-wasm").onclick = () => { $("nogpu").hidden = true; ui.go.textContent = "Load model (CPU)"; ui.go.disabled = false; };
+      return;
+    }
+    ui.backend.textContent = `backend: WebGPU${s.adapter ? " · " + s.adapter : ""}`;
+  }
+  ui.go.disabled = false;
+  ui.go.textContent = "Load model";
+  if (params.has("autoload") || PARITY || await hasCachedModel()) {   // second visit: load straight from cache
+    await load();
+    if (PARITY && agate) await parity();
+  }
+})();
+
+window.agateClearCache = clearCache;

js/config.js

@@ -0,0 +1,3 @@
+// Where the model files live. The Hugging Face Space hosts them next to the page in models/.
+// A ?models=<url> query parameter overrides this.
+window.AGATE_MODEL_BASE = "./models/";

models/generator.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f491fa95d2de2b37358171c5fbd3300c31d7b53d4c0e1870288f56cd3821a20d
+size 385371629

models/manifest.json

@@ -0,0 +1,44 @@
+{
+  "model": "Logolabs/agate-preview-001",
+  "format": "onnx opset 17; generator + text encoder: fp16 weights, fp32 compute; TAESD decoder: fp32",
+  "files": {
+    "generator.onnx": {
+      "bytes": 385371629,
+      "sha256": "f491fa95d2de2b37358171c5fbd3300c31d7b53d4c0e1870288f56cd3821a20d"
+    },
+    "text_encoder.onnx": {
+      "bytes": 136560058,
+      "sha256": "7288650624e42c84d96a389809daea8806ead8898215a94bf04dd51d1acbb314"
+    },
+    "taesd_decoder.onnx": {
+      "bytes": 4912775,
+      "sha256": "e98516c3f1f838bcd47ace5e6a184dbaa63ecc016a3406833779600cdbfb77a3"
+    },
+    "tokenizer.json": {
+      "bytes": 3583228,
+      "sha256": "6c8aaa9a542084f2457eab775d4eeb51f92a70c0fd9de28d5edb0ddec3c08d30"
+    },
+    "tokenizer_config.json": {
+      "bytes": 21785,
+      "sha256": "e7cb600a544347011cd31309043438c57b4fe30d53bbd18d1ad337a044a02bd3"
+    }
+  },
+  "text_max_len": 512,
+  "buckets": [
+    64,
+    128,
+    256,
+    512
+  ],
+  "latent_hw": 32,
+  "latent_ch": 4,
+  "ctx_dim": 512,
+  "sampler": {
+    "kind": "euler",
+    "steps": 50,
+    "cfg": 3.0,
+    "t0_is_noise": true,
+    "uncond_prompt": ""
+  },
+  "pad_token_id": 50283
+}

models/taesd_decoder.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e98516c3f1f838bcd47ace5e6a184dbaa63ecc016a3406833779600cdbfb77a3
+size 4912775

models/text_encoder.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7288650624e42c84d96a389809daea8806ead8898215a94bf04dd51d1acbb314
+size 136560058

models/tokenizer_config.json

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+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
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test/parity.json

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+{
+  "prompt": "a green teapot and a red cup on a table",
+  "steps": 50,
+  "cfg": 3.0,
+  "seed": 0,
+  "source": "release AgatePipeline, CPU fp32, fast_vae (TAESD), torch.randn replaced by parity_noise.bin",
+  "onnx_cpu_latent_max_abs": 0.0348610058426857,
+  "onnx_cpu_image_max_abs": 5
+}

test/parity_image.bin

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+oid sha256:f02c5c097a4f5ba6bfc0738ef8b707c77b7ccffda50ebb349f408b16ee77d5e3
+size 196608

test/parity_latent.bin

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+oid sha256:f33683243a20402962a1cb9a4f901bcb862aef2509a7a184e6eda637a234b91a
+size 16384

test/parity_noise.bin

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+size 16384