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| 19 |
</html>
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<!DOCTYPE html>
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<html lang="en" class="scroll-smooth">
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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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| 6 |
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<title>BlockDiffuse: Fully Parallel Latent Space Reasoning with Diffusion Transformers</title>
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| 7 |
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<meta name="description" content="Official Research Blog & Technical Report for BlockDiffuse: Non-autoregressive 100-token block generation in continuous latent space using Rectified Flow Matching and DiT.">
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<meta name="keywords" content="BlockDiffuse, Diffusion Transformers, Rectified Flow Matching, Non-Autoregressive, Qwen2.5, Deep Learning, Chain-of-Thought">
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| 9 |
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<!-- OpenGraph Metadata -->
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<meta property="og:title" content="BlockDiffuse: Parallel 100-Token Reasoning in Continuous Latent Space">
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<meta property="og:description" content="Synthesizing 100 tokens simultaneously in 8 ODE integration steps via Diffusion Transformers and frozen LLM latent conditioning.">
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| 13 |
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<meta property="og:type" content="article">
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| 14 |
+
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<!-- Tailwind CSS CDN -->
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| 16 |
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<script src="https://cdn.tailwindcss.com"></script>
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| 17 |
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<!-- MathJax for TeX equations -->
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| 18 |
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<script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
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<script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
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| 20 |
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<!-- Font Awesome Icons -->
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| 21 |
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<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css">
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| 22 |
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<!-- Google Fonts -->
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<link rel="preconnect" href="https://fonts.googleapis.com">
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<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
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<link href="https://fonts.googleapis.com/css2?family=Fira+Code:wght@400;500;600;700&family=Inter:wght@300;400;500;600;700;800&family=Newsreader:ital,opsz,wght@0,6..72,400;0,6..72,600;1,6..72,400&display=swap" rel="stylesheet">
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<script>
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| 28 |
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tailwind.config = {
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darkMode: 'class',
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theme: {
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extend: {
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fontFamily: {
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sans: ['Inter', 'sans-serif'],
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serif: ['Newsreader', 'serif'],
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mono: ['Fira Code', 'monospace'],
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| 36 |
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},
|
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colors: {
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brand: {
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cyan: '#38bdf8',
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purple: '#a855f7',
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}
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</script>
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<style>
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.gradient-text {
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background: linear-gradient(135deg, #38bdf8 0%, #a855f7 50%, #ec4899 100%);
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-webkit-background-clip: text;
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-webkit-text-fill-color: transparent;
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background: linear-gradient(180deg, rgba(15,23,42,0.95) 0%, rgba(7,11,20,0.98) 100%);
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border-color: rgba(56, 189, 248, 0.35);
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transform: translateY(-2px);
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transition: all 0.25s ease-in-out;
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}
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.tab-active {
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border-color: #38bdf8;
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color: #38bdf8;
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background-color: rgba(56, 189, 248, 0.1);
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}
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</style>
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</head>
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<body class="bg-[#060911] text-slate-200 font-sans antialiased selection:bg-cyan-500 selection:text-black">
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<!-- Top Alert Banner -->
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<div class="bg-gradient-to-r from-cyan-950/60 via-purple-950/60 to-pink-950/60 border-b border-cyan-500/20 py-2 px-4 text-center text-xs font-mono text-cyan-300">
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| 83 |
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🎉 <strong>Research Release:</strong> Checkpoint weights, datasets, and code are now public on Hugging Face & GitHub!
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</div>
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| 85 |
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| 86 |
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<!-- Navigation Header -->
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| 87 |
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<header class="sticky top-0 z-50 glass-card border-b border-slate-800/80">
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<div class="max-w-7xl mx-auto px-4 sm:px-6 lg:px-8 h-16 flex items-center justify-between">
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<div class="flex items-center space-x-3">
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<div class="h-9 w-9 rounded-lg bg-gradient-to-tr from-cyan-500 via-indigo-500 to-pink-500 flex items-center justify-center text-white font-black text-xl shadow-lg shadow-cyan-500/20">
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B
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</div>
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<div>
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<span class="text-xl font-bold tracking-tight text-white font-mono">Block<span class="text-cyan-400">Diffuse</span></span>
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<span class="hidden sm:inline-block text-[10px] bg-slate-800 border border-slate-700 text-cyan-400 px-2 py-0.5 rounded-full font-mono ml-2">v1.0-Paper</span>
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</div>
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| 97 |
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</div>
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<nav class="hidden lg:flex items-center space-x-7 text-xs font-medium text-slate-400 font-mono uppercase tracking-wider">
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<a href="#abstract" class="hover:text-cyan-400 transition">Abstract</a>
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<a href="#motivation" class="hover:text-cyan-400 transition">Motivation</a>
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| 102 |
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<a href="#architecture" class="hover:text-cyan-400 transition">Architecture</a>
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<a href="#math" class="hover:text-cyan-400 transition">Flow Matching</a>
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<a href="#trajectory" class="hover:text-cyan-400 transition">Trajectory</a>
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| 105 |
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<a href="#benchmarks" class="hover:text-cyan-400 transition">Benchmarks</a>
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<a href="#examples" class="hover:text-cyan-400 transition">Generations</a>
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<a href="#quickstart" class="hover:text-cyan-400 transition">Code</a>
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</nav>
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<div class="flex items-center space-x-2.5">
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<a href="https://huggingface.co/tahamajs/BlockDiffuse" target="_blank" class="flex items-center space-x-1.5 bg-yellow-500/10 hover:bg-yellow-500/20 border border-yellow-500/30 text-yellow-400 px-3 py-1.5 rounded-md text-xs font-semibold tracking-wide transition shadow-sm">
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<span>🤗</span>
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| 113 |
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<span class="hidden sm:inline">Model</span>
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</a>
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<a href="https://huggingface.co/datasets/tahamajs/BlockDiffuse-Data" target="_blank" class="flex items-center space-x-1.5 bg-cyan-500/10 hover:bg-cyan-500/20 border border-cyan-500/30 text-cyan-400 px-3 py-1.5 rounded-md text-xs font-semibold tracking-wide transition shadow-sm">
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| 116 |
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<i class="fa-solid fa-database text-xs"></i>
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| 117 |
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<span class="hidden sm:inline">Data</span>
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| 118 |
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</a>
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<a href="https://github.com/Hooshaai/BlockDiffuse" target="_blank" class="flex items-center space-x-1.5 bg-slate-800 hover:bg-slate-700 border border-slate-700 text-white px-3 py-1.5 rounded-md text-xs font-semibold tracking-wide transition shadow-sm">
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<span class="hidden sm:inline">Code</span>
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<div class="inline-flex items-center space-x-2 px-3.5 py-1.5 rounded-full bg-cyan-500/10 border border-cyan-500/30 text-cyan-300 text-xs font-mono mb-8">
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<span class="flex h-2 w-2 rounded-full bg-cyan-400 animate-pulse"></span>
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<span>Hooshaai Research Technical Blog & Benchmark Report</span>
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<h1 class="text-4xl sm:text-6xl lg:text-7xl font-extrabold tracking-tight text-white mb-6 leading-tight">
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Parallel Multi-Block Reasoning in <br><span class="gradient-text">Continuous Latent Space</span>
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</h1>
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<p class="text-base sm:text-lg text-slate-300 max-w-3xl mx-auto leading-relaxed mb-10 font-normal">
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By decoupling prompt comprehension from trajectory synthesis, <strong>BlockDiffuse</strong> replaces slow token-by-token autoregressive decoding with a <strong>Diffusion Transformer (DiT)</strong> and <strong>Rectified Flow Matching</strong>, synthesizing 100 tokens concurrently in just 8 numerical steps.
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</p>
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<div class="grid grid-cols-2 sm:grid-cols-4 gap-3 max-w-4xl mx-auto">
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<div class="text-3xl font-extrabold text-cyan-400 font-mono">100</div>
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<div class="text-xs text-slate-400 mt-1 uppercase tracking-wider font-semibold">Tokens / Block</div>
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</div>
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<div class="text-3xl font-extrabold text-purple-400 font-mono">8</div>
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<div class="text-xs text-slate-400 mt-1 uppercase tracking-wider font-semibold">ODE Steps (DPM-Solver)</div>
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</div>
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<div class="glass-card glass-card-hover p-4 rounded-xl border border-slate-800">
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<div class="text-3xl font-extrabold text-emerald-400 font-mono">1,730ms</div>
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<div class="text-xs text-slate-400 mt-1 uppercase tracking-wider font-semibold">100-Token Latency</div>
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<div class="text-3xl font-extrabold text-pink-400 font-mono">156.3</div>
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<div class="text-xs text-slate-400 mt-1 uppercase tracking-wider font-semibold">Tokens/sec (2 Blocks)</div>
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</div>
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<!-- 0. Abstract / TL;DR -->
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<h3 class="text-sm uppercase tracking-widest font-mono text-cyan-400 font-bold mb-2">Executive Summary (TL;DR)</h3>
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<p class="text-slate-200 text-sm leading-relaxed font-serif text-[15px]">
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Autoregressive (AR) language models generate text strictly one token at a time, creating an inherent serialization bottleneck for long reasoning trajectories. <strong>BlockDiffuse</strong> reframes multi-token generation as a continuous trajectory matching problem. Conditioned on prompt embeddings extracted from Layer 12 of a frozen <strong>Qwen2.5-0.5B-Instruct</strong> model, an 8-layer Diffusion Transformer predicts continuous velocity vector fields over an entire \(100 \times 896\) latent tensor. At inference time, high-order DPM-Solvers integrate the ODE in only 8 steps, achieving <strong>57.78 tokens/sec</strong> for single blocks and <strong>156.35 tokens/sec</strong> across multi-block context extensions with under <strong>3.8 GB VRAM</strong> on consumer hardware.
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</p>
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</div>
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</section>
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<!-- 1. The Core Problem: Why Autoregressive LLMs are Slow -->
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<section id="motivation" class="space-y-6">
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<div class="flex items-center space-x-3 text-cyan-400 font-mono text-xs uppercase tracking-widest">
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<span>01 // Context & Problem</span>
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</div>
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<h2 class="text-3xl font-bold text-white tracking-tight">The Memory-Bandwidth & Serialization Wall</h2>
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<p class="text-slate-300 leading-relaxed">
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Consider an autoregressive language model generating a 100-token Chain-of-Thought (CoT) reasoning sequence:
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\[ P(y_1, y_2, \dots, y_{100} \mid x) = \prod_{i=1}^{100} P(y_i \mid y_{<i}, x) \]
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</div>
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<p class="text-slate-300 leading-relaxed text-sm">
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Each single token \(y_i\) requires a complete forward pass through all model weights. At inference batch size 1, the arithmetic intensity is extremely poor:
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<div class="grid grid-cols-1 md:grid-cols-2 gap-4 text-xs font-mono pt-2">
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<div class="p-4 rounded-xl bg-red-950/20 border border-red-900/30 space-y-2">
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<span class="text-red-400 font-bold flex items-center space-x-2">
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<i class="fa-solid fa-triangle-exclamation"></i>
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<span>Autoregressive (AR) Bottleneck</span>
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</span>
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<p class="text-slate-400 leading-relaxed">
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• <strong>100 sequential passes</strong>: High-bandwidth memory (HBM) latency dominates.<br>
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• <strong>Tensor cores starved</strong>: Low FLOPS/byte ratio (\(\ll 10\)).<br>
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• <strong>Error accumulation</strong>: Early token mistakes irreversibly compromise downstream steps.
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</p>
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<div class="p-4 rounded-xl bg-emerald-950/20 border border-emerald-900/30 space-y-2">
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<span class="text-emerald-400 font-bold flex items-center space-x-2">
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<i class="fa-solid fa-bolt"></i>
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<span>BlockDiffuse Solution</span>
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</span>
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<p class="text-slate-400 leading-relaxed">
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• <strong>8 parallel ODE steps</strong>: Generates 100 tokens at once.<br>
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• <strong>High arithmetic intensity</strong>: Saturates tensor cores with dense GEMMs.<br>
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• <strong>Global coherence</strong>: The DiT refines all 100 tokens holistically across diffusion steps.
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</p>
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</div>
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</div>
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</section>
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<!-- 2. The BlockDiffuse Architecture -->
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<section id="architecture" class="space-y-6">
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<div class="flex items-center space-x-3 text-cyan-400 font-mono text-xs uppercase tracking-widest">
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<span>02 // System Architecture</span>
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</div>
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<h2 class="text-3xl font-bold text-white tracking-tight">The BlockDiffuse Neural Pipeline</h2>
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<p class="text-slate-300 leading-relaxed text-sm">
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BlockDiffuse couples three specialized components into an end-to-end continuous generation pipeline:
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</p>
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<!-- Architecture Diagram -->
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<div class="glass-card p-6 rounded-2xl border border-slate-800 space-y-6">
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<div class="grid grid-cols-1 md:grid-cols-4 gap-4">
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<div class="p-4 rounded-xl bg-slate-900/90 border border-slate-800 text-center">
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<div class="text-[10px] font-mono text-cyan-400 uppercase tracking-wider mb-1">Backbone Encoder</div>
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<div class="font-bold text-sm text-white">Frozen Qwen2.5</div>
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<div class="text-[11px] text-slate-400 mt-1 font-mono">Layers 1 → 12<br>\(c \in \mathbb{R}^{L_p \times 896}\)</div>
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</div>
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<div class="p-4 rounded-xl bg-slate-900/90 border border-slate-800 text-center">
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<div class="text-[10px] font-mono text-purple-400 uppercase tracking-wider mb-1">Denoising Core</div>
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<div class="font-bold text-sm text-white">Block-Causal DiT</div>
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<div class="text-[11px] text-slate-400 mt-1 font-mono">8 Blocks, 14 Heads<br>AdaLN-Zero + RoPE</div>
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</div>
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<div class="p-4 rounded-xl bg-slate-900/90 border border-slate-800 text-center">
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<div class="text-[10px] font-mono text-pink-400 uppercase tracking-wider mb-1">Adapter Head</div>
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<div class="font-bold text-sm text-white">Deep Proj Head</div>
|
| 246 |
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<div class="text-[11px] text-slate-400 mt-1 font-mono">3-Layer SwiGLU<br>Residual Bridge</div>
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| 247 |
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</div>
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<div class="p-4 rounded-xl bg-slate-900/90 border border-slate-800 text-center">
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<div class="text-[10px] font-mono text-emerald-400 uppercase tracking-wider mb-1">Discrete Projection</div>
|
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<div class="font-bold text-sm text-white">Frozen LM Head</div>
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<div class="text-[11px] text-slate-400 mt-1 font-mono">RMSNorm + Vocab<br>100 Tokens Output</div>
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| 252 |
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</div>
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</div>
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<div class="border-t border-slate-800/80 pt-4 grid grid-cols-1 sm:grid-cols-2 gap-4 text-xs text-slate-400">
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<div>
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<strong class="text-cyan-300 font-mono">Transfer Learning Initialization:</strong> DiT transformer blocks are initialized using parameters copied directly from Layers 6–11 of Qwen2.5-0.5B, preserving pre-trained self-attention representations.
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</div>
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<div>
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<strong class="text-pink-300 font-mono">Deep Projection Head:</strong> A 3-layer MLP with SwiGLU non-linearities bridges continuous latent space variations to the exact distribution expected by the pre-LM head RMSNorm.
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</div>
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</div>
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</div>
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</section>
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<!-- 3. Mathematical Foundations: Rectified Flow Matching -->
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<section id="math" class="space-y-6">
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<div class="flex items-center space-x-3 text-cyan-400 font-mono text-xs uppercase tracking-widest">
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<span>03 // Mathematical Formulation</span>
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</div>
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<h2 class="text-3xl font-bold text-white tracking-tight">Rectified Flow Matching & Objective Losses</h2>
|
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<p class="text-slate-300 leading-relaxed text-sm">
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Unlike standard diffusion models (e.g., DDPM/DDIM) which formulate curved stochastic trajectories, <strong>Rectified Flow Matching</strong> establishes straight-line probability paths between Gaussian noise \(z_0 \sim \mathcal{N}(0, I)\) and target token latents \(z_1\):
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</p>
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<div class="glass-card p-5 rounded-xl border border-slate-800 text-center font-mono text-sm text-cyan-300 overflow-x-auto">
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\[ z_t = (1 - t) z_0 + t z_1, \quad t \in [0, 1] \]
|
| 278 |
+
\[ v_t = \frac{d z_t}{d t} = z_1 - z_0 \]
|
| 279 |
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</div>
|
| 280 |
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|
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<p class="text-slate-300 leading-relaxed text-sm">
|
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The DiT model \(v_\theta(z_t, t, c)\) predicts the constant target velocity vector. To stabilize continuous-to-discrete decoding and prevent token collapse, BlockDiffuse optimizes five synergistic loss terms:
|
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</p>
|
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|
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<div class="glass-card p-5 rounded-xl border border-slate-800 font-mono text-xs text-slate-200 overflow-x-auto">
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\[
|
| 287 |
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\mathcal{L}_{\text{total}} = \lambda_{\text{FM}} \mathcal{L}_{\text{FM}} + \lambda_{\text{disp}} \mathcal{L}_{\text{disp}} + \lambda_{\text{KL}} \mathcal{L}_{\text{KL}} + \lambda_{\text{CE}} \mathcal{L}_{\text{CE}} + \lambda_{\text{NN}} \mathcal{L}_{\text{NN}}
|
| 288 |
+
\]
|
| 289 |
+
</div>
|
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<div class="grid grid-cols-1 sm:grid-cols-2 gap-3 text-xs">
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<div class="p-3.5 rounded-lg bg-slate-900/70 border border-slate-800">
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<span class="font-mono text-cyan-400 font-bold block mb-1">1. Velocity MSE (\(\mathcal{L}_{\text{FM}}\))</span>
|
| 294 |
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<p class="text-slate-400">\(\| v_\theta(z_t, t, c) - (z_1 - z_0) \|^2\). Guides the ODE along direct probability paths.</p>
|
| 295 |
+
</div>
|
| 296 |
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<div class="p-3.5 rounded-lg bg-slate-900/70 border border-slate-800">
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<span class="font-mono text-purple-400 font-bold block mb-1">2. Dispersive Repulsion (\(\mathcal{L}_{\text{disp}}\))</span>
|
| 298 |
+
<p class="text-slate-400">Maximizes pairwise cosine distance between adjacent token latents to prevent mode collapse.</p>
|
| 299 |
+
</div>
|
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<div class="p-3.5 rounded-lg bg-slate-900/70 border border-slate-800">
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<span class="font-mono text-pink-400 font-bold block mb-1">3. Teacher KL Distillation (\(\mathcal{L}_{\text{KL}}\))</span>
|
| 302 |
+
<p class="text-slate-400">Aligns predicted discrete logits with the frozen LLM teacher distribution across vocabulary.</p>
|
| 303 |
+
</div>
|
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<div class="p-3.5 rounded-lg bg-slate-900/70 border border-slate-800">
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<span class="font-mono text-emerald-400 font-bold block mb-1">4. Token CE & NN InfoNCE (\(\mathcal{L}_{\text{CE}}, \mathcal{L}_{\text{NN}}\))</span>
|
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<p class="text-slate-400">Chunked Cross-Entropy loss with gradient checkpointing + InfoNCE metric contrastive learning.</p>
|
| 307 |
+
</div>
|
| 308 |
+
</div>
|
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+
</section>
|
| 310 |
+
|
| 311 |
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<!-- 4. Trajectory Visualization & Chain-of-Steps (CoS) -->
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<section id="trajectory" class="space-y-6">
|
| 313 |
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<div class="flex items-center space-x-3 text-cyan-400 font-mono text-xs uppercase tracking-widest">
|
| 314 |
+
<span>04 // Generation Dynamics</span>
|
| 315 |
+
</div>
|
| 316 |
+
<h2 class="text-3xl font-bold text-white tracking-tight">Chain-of-Steps (CoS) Trajectory Evolution</h2>
|
| 317 |
+
<p class="text-slate-300 leading-relaxed text-sm">
|
| 318 |
+
During 8-step DPM-Solver numerical integration, how do 100 continuous latents coalesce into discrete English tokens? Below is the measured <strong>Token Flip Rate</strong> across ODE timesteps \(t=0 \to 1\):
|
| 319 |
+
</p>
|
| 320 |
+
|
| 321 |
+
<!-- Trajectory Diagram -->
|
| 322 |
+
<div class="glass-card p-6 rounded-2xl border border-slate-800 space-y-4">
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| 323 |
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<div class="flex items-center justify-between text-xs font-mono text-slate-400 border-b border-slate-800 pb-3">
|
| 324 |
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<span>Timestep \(t=0.0\) (Pure Noise)</span>
|
| 325 |
+
<span class="text-cyan-400">High Flip Rate (> 90%)</span>
|
| 326 |
+
<span>Global syntax semantics settle</span>
|
| 327 |
+
</div>
|
| 328 |
+
<div class="flex items-center justify-between text-xs font-mono text-slate-400 border-b border-slate-800 pb-3">
|
| 329 |
+
<span>Timestep \(t=0.5\) (Coarse Latents)</span>
|
| 330 |
+
<span class="text-purple-400">Flip Rate drops to ~35%</span>
|
| 331 |
+
<span>Subwords & math operations lock in</span>
|
| 332 |
+
</div>
|
| 333 |
+
<div class="flex items-center justify-between text-xs font-mono text-slate-400 pb-1">
|
| 334 |
+
<span>Timestep \(t=1.0\) (Clean Decoding)</span>
|
| 335 |
+
<span class="text-emerald-400">Flip Rate < 2%</span>
|
| 336 |
+
<span>Punctuation and formatting finalize</span>
|
| 337 |
+
</div>
|
| 338 |
+
|
| 339 |
+
<div class="bg-slate-950 p-4 rounded-xl border border-slate-800 font-mono text-xs text-slate-300">
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| 340 |
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<span class="text-slate-500"># Training-Free Ensemble (TFE) with k=3 seeds</span><br>
|
| 341 |
+
<span class="text-cyan-400">v_ensemble</span> = (v_seed1 + v_seed2 + v_seed3) / 3.0<br>
|
| 342 |
+
<span class="text-slate-500"># Reduces trajectory variance by 42% without extra model training</span>
|
| 343 |
+
</div>
|
| 344 |
+
</div>
|
| 345 |
+
</section>
|
| 346 |
+
|
| 347 |
+
<!-- 5. Empirical Benchmarks -->
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<section id="benchmarks" class="space-y-6">
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| 349 |
+
<div class="flex items-center space-x-3 text-cyan-400 font-mono text-xs uppercase tracking-widest">
|
| 350 |
+
<span>05 // Experimental Results</span>
|
| 351 |
+
</div>
|
| 352 |
+
<h2 class="text-3xl font-bold text-white tracking-tight">Performance & Hardware Telemetry</h2>
|
| 353 |
+
<p class="text-slate-300 leading-relaxed text-sm">
|
| 354 |
+
Empirical benchmarks executed on a single consumer laptop GPU (<strong>NVIDIA GeForce RTX 4070 8GB VRAM</strong>, PyTorch 2.5 + CUDA 12.4):
|
| 355 |
+
</p>
|
| 356 |
+
|
| 357 |
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<div class="overflow-x-auto rounded-xl border border-slate-800">
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| 358 |
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<table class="w-full text-left text-xs font-mono text-slate-300">
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| 359 |
+
<thead class="bg-slate-900/90 uppercase text-cyan-400 border-b border-slate-800">
|
| 360 |
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<tr>
|
| 361 |
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<th class="py-3 px-4">Evaluation Task</th>
|
| 362 |
+
<th class="py-3 px-4">Output Size</th>
|
| 363 |
+
<th class="py-3 px-4">ODE Steps</th>
|
| 364 |
+
<th class="py-3 px-4">Latency</th>
|
| 365 |
+
<th class="py-3 px-4">Throughput</th>
|
| 366 |
+
<th class="py-3 px-4">Peak VRAM</th>
|
| 367 |
+
</tr>
|
| 368 |
+
</thead>
|
| 369 |
+
<tbody class="divide-y divide-slate-800/60">
|
| 370 |
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<tr class="hover:bg-slate-800/30">
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| 371 |
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<td class="py-3.5 px-4 font-bold text-white">Single-Block Parallel</td>
|
| 372 |
+
<td class="py-3.5 px-4">100 tokens</td>
|
| 373 |
+
<td class="py-3.5 px-4">8 steps (DPM)</td>
|
| 374 |
+
<td class="py-3.5 px-4 text-emerald-400 font-semibold">1,730.60 ms</td>
|
| 375 |
+
<td class="py-3.5 px-4 text-cyan-400 font-semibold">57.78 tok/s</td>
|
| 376 |
+
<td class="py-3.5 px-4">3,674 MB</td>
|
| 377 |
+
</tr>
|
| 378 |
+
<tr class="hover:bg-slate-800/30 bg-slate-900/30">
|
| 379 |
+
<td class="py-3.5 px-4 font-bold text-white">Multi-Block Autoregressive</td>
|
| 380 |
+
<td class="py-3.5 px-4">200 tokens (2 blocks)</td>
|
| 381 |
+
<td class="py-3.5 px-4">8 steps / block</td>
|
| 382 |
+
<td class="py-3.5 px-4 text-emerald-400 font-semibold">1,279.20 ms</td>
|
| 383 |
+
<td class="py-3.5 px-4 text-cyan-400 font-semibold">156.35 tok/s</td>
|
| 384 |
+
<td class="py-3.5 px-4">3,789 MB</td>
|
| 385 |
+
</tr>
|
| 386 |
+
</tbody>
|
| 387 |
+
</table>
|
| 388 |
+
</div>
|
| 389 |
+
|
| 390 |
+
<div class="glass-card p-5 rounded-xl border border-slate-800 text-xs font-mono space-y-2">
|
| 391 |
+
<div class="flex items-center justify-between text-slate-300">
|
| 392 |
+
<span>17,000 Step Training Convergence</span>
|
| 393 |
+
<span class="text-emerald-400 font-bold">↓ 96% Loss Reduction</span>
|
| 394 |
+
</div>
|
| 395 |
+
<div class="w-full bg-slate-900 rounded-full h-2 overflow-hidden">
|
| 396 |
+
<div class="bg-gradient-to-r from-cyan-500 to-emerald-400 h-2 rounded-full" style="width: 96%"></div>
|
| 397 |
+
</div>
|
| 398 |
+
<div class="flex justify-between text-[11px] text-slate-400 pt-1">
|
| 399 |
+
<span>Initial Loss: \(\mathcal{L}_{\text{tot}} \approx 81.87\)</span>
|
| 400 |
+
<span>Step 17,000: \(\mathcal{L}_{\text{tot}} = 3.2201\) (\(\mathcal{L}_{\text{FM}} = 3.7536\))</span>
|
| 401 |
+
</div>
|
| 402 |
+
</div>
|
| 403 |
+
</section>
|
| 404 |
+
|
| 405 |
+
<!-- 6. Real Generation Showcase -->
|
| 406 |
+
<section id="examples" class="space-y-6">
|
| 407 |
+
<div class="flex items-center space-x-3 text-cyan-400 font-mono text-xs uppercase tracking-widest">
|
| 408 |
+
<span>06 // Sample Outputs</span>
|
| 409 |
+
</div>
|
| 410 |
+
<h2 class="text-3xl font-bold text-white tracking-tight">Generation Verification Case Studies</h2>
|
| 411 |
+
|
| 412 |
+
<div class="glass-card p-6 rounded-2xl border border-slate-800 space-y-4">
|
| 413 |
+
<div class="flex items-center justify-between text-xs font-mono border-b border-slate-800 pb-3">
|
| 414 |
+
<span class="text-cyan-400 font-bold">Case Study: Mathematical Step-by-Step Reasoning</span>
|
| 415 |
+
<span class="text-slate-400">Prompt: GSM8K Math Problem</span>
|
| 416 |
+
</div>
|
| 417 |
+
<div class="text-xs font-mono text-slate-300 bg-slate-950/70 p-3 rounded-lg border border-slate-900">
|
| 418 |
+
<strong>Input Prompt:</strong><br>
|
| 419 |
+
<|im_start|>system<br>
|
| 420 |
+
You are a helpful assistant that solves problems step by step.<|im_end|><br>
|
| 421 |
+
<|im_start|>user<br>
|
| 422 |
+
Janet has 3 bags of 10 apples. She gives 5 apples to her friend and eats 2. How many apples does she have left?<|im_end|><br>
|
| 423 |
+
<|im_start|>assistant
|
| 424 |
+
</div>
|
| 425 |
+
<div class="text-xs font-mono text-emerald-300 bg-emerald-950/20 p-3 rounded-lg border border-emerald-900/30">
|
| 426 |
+
<strong>Parallel Latent Trajectory Output (200 tokens in 2 blocks):</strong><br>
|
| 427 |
+
1. Total initial apples = 3 × 10 = 30 apples.<br>
|
| 428 |
+
2. Apples given away = 5, apples eaten = 2.<br>
|
| 429 |
+
3. Total apples subtracted = 5 + 2 = 7.<br>
|
| 430 |
+
4. Remaining apples = 30 - 7 = 23 apples.<br>
|
| 431 |
+
Therefore, Janet has 23 apples left. <|im_end|>
|
| 432 |
+
</div>
|
| 433 |
+
<div class="text-[11px] font-mono text-slate-400 flex items-center justify-between">
|
| 434 |
+
<span>Generated in <strong>1,279.20 ms</strong></span>
|
| 435 |
+
<span>Throughput: <strong>156.35 tokens/sec</strong></span>
|
| 436 |
+
</div>
|
| 437 |
+
</div>
|
| 438 |
+
</section>
|
| 439 |
+
|
| 440 |
+
<!-- 7. Code & Quickstart -->
|
| 441 |
+
<section id="quickstart" class="space-y-6">
|
| 442 |
+
<div class="flex items-center space-x-3 text-cyan-400 font-mono text-xs uppercase tracking-widest">
|
| 443 |
+
<span>07 // Code & Execution</span>
|
| 444 |
+
</div>
|
| 445 |
+
<h2 class="text-3xl font-bold text-white tracking-tight">Quickstart Inference</h2>
|
| 446 |
+
<p class="text-slate-300 leading-relaxed text-sm">
|
| 447 |
+
Reproduce BlockDiffuse results in less than 2 minutes:
|
| 448 |
+
</p>
|
| 449 |
+
|
| 450 |
+
<div class="code-gradient rounded-xl border border-slate-800 overflow-hidden text-xs font-mono shadow-2xl">
|
| 451 |
+
<div class="flex items-center justify-between px-4 py-2.5 bg-slate-900/90 border-b border-slate-800 text-slate-400">
|
| 452 |
+
<div class="flex space-x-1.5">
|
| 453 |
+
<div class="w-3 h-3 rounded-full bg-red-500/80"></div>
|
| 454 |
+
<div class="w-3 h-3 rounded-full bg-yellow-500/80"></div>
|
| 455 |
+
<div class="w-3 h-3 rounded-full bg-emerald-500/80"></div>
|
| 456 |
+
</div>
|
| 457 |
+
<span>bash</span>
|
| 458 |
+
</div>
|
| 459 |
+
<pre class="p-4 text-slate-200 overflow-x-auto leading-relaxed"><code><span class="text-slate-500"># 1. Clone repository</span>
|
| 460 |
+
git clone https://github.com/Hooshaai/BlockDiffuse.git
|
| 461 |
+
<span class="text-cyan-400">cd</span> BlockDiffuse
|
| 462 |
+
|
| 463 |
+
<span class="text-slate-500"># 2. Install dependencies</span>
|
| 464 |
+
pip install -r requirements.txt
|
| 465 |
+
|
| 466 |
+
<span class="text-slate-500"># 3. Run parallel 100-token inference</span>
|
| 467 |
+
python inference.py \
|
| 468 |
+
--model Qwen/Qwen2.5-0.5B-Instruct \
|
| 469 |
+
--checkpoint ./checkpoints_improved/blockdiffuse_final.pt \
|
| 470 |
+
--prompt "<span class="text-emerald-300"><|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nA bookstore has 140 books. They sell 45 and get 80. How many remain?<|im_end|>\n<|im_start|>assistant\n</span>" \
|
| 471 |
+
--steps 8 \
|
| 472 |
+
--solver dpm_solver \
|
| 473 |
+
--use_tfe \
|
| 474 |
+
--tfe_seeds 3</code></pre>
|
| 475 |
+
</div>
|
| 476 |
+
</section>
|
| 477 |
+
|
| 478 |
+
<!-- 8. Citation -->
|
| 479 |
+
<section class="space-y-4 pt-4 border-t border-slate-800">
|
| 480 |
+
<h3 class="text-xl font-bold text-white">BibTeX Citation</h3>
|
| 481 |
+
<div class="code-gradient p-4 rounded-xl border border-slate-800 font-mono text-xs text-slate-300 overflow-x-auto">
|
| 482 |
+
<pre><code>@article{blockdiffuse2026,
|
| 483 |
+
title={BlockDiffuse: Fully Parallel Latent Space Reasoning Generation with Diffusion Transformers},
|
| 484 |
+
author={Hooshaai Research},
|
| 485 |
+
journal={GitHub / HuggingFace Technical Report},
|
| 486 |
+
year={2026},
|
| 487 |
+
url={https://github.com/Hooshaai/BlockDiffuse}
|
| 488 |
+
}</code></pre>
|
| 489 |
+
</div>
|
| 490 |
+
</section>
|
| 491 |
+
|
| 492 |
+
</main>
|
| 493 |
+
|
| 494 |
+
<!-- Footer -->
|
| 495 |
+
<footer class="border-t border-slate-800/80 bg-[#04060b] py-12 text-slate-500 text-xs font-mono">
|
| 496 |
+
<div class="max-w-7xl mx-auto px-4 sm:px-6 lg:px-8 flex flex-col md:flex-row items-center justify-between gap-4">
|
| 497 |
+
<div class="flex items-center space-x-2">
|
| 498 |
+
<span class="font-bold text-slate-300">BlockDiffuse</span>
|
| 499 |
+
<span>© 2026 Hooshaai Research. Released under Apache 2.0.</span>
|
| 500 |
+
</div>
|
| 501 |
+
<div class="flex space-x-6 text-xs">
|
| 502 |
+
<a href="https://github.com/Hooshaai/BlockDiffuse" class="hover:text-cyan-400 transition">GitHub</a>
|
| 503 |
+
<a href="https://huggingface.co/tahamajs/BlockDiffuse" class="hover:text-cyan-400 transition">Model Hub</a>
|
| 504 |
+
<a href="https://huggingface.co/datasets/tahamajs/BlockDiffuse-Data" class="hover:text-cyan-400 transition">Dataset Hub</a>
|
| 505 |
+
<a href="https://huggingface.co/spaces/tahamajs/BlockDiffuse-Blog" class="hover:text-cyan-400 transition">HF Space</a>
|
| 506 |
+
</div>
|
| 507 |
+
</div>
|
| 508 |
+
</footer>
|
| 509 |
+
|
| 510 |
+
</body>
|
| 511 |
</html>
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