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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>LoRA Lab — Training Config Generator</title>
<meta name="description" content="Generate optimal LoRA training configurations for SDXL, FLUX, HunyuanVideo and more. Supports AMD ROCm and NVIDIA GPUs.">
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@300;400;500;600;700&display=swap" rel="stylesheet">
<style>
*,*::before,*::after{box-sizing:border-box;margin:0;padding:0}
:root{
--bg:#0a0a0a;--bg2:#111;--bg3:#1a1a1a;--bg4:#222;
--green:#00ff41;--green-dim:#00cc33;--green-glow:rgba(0,255,65,.15);
--orange:#ff6b35;--orange-glow:rgba(255,107,53,.15);
--red:#ff3333;--red-glow:rgba(255,51,51,.15);
--cyan:#00d4ff;--cyan-dim:#0099bb;
--text:#e0e0e0;--text-dim:#888;--text-dark:#555;
--border:#2a2a2a;--border-hover:#3a3a3a;
--font:'JetBrains Mono',monospace;
--radius:6px;
}
html{font-size:14px;scroll-behavior:smooth}
body{background:var(--bg);color:var(--text);font-family:var(--font);line-height:1.6;min-height:100vh}
a{color:var(--green);text-decoration:none}
a:hover{text-decoration:underline}
/* Layout */
.container{max-width:960px;margin:0 auto;padding:1.5rem 1rem}
header{text-align:center;padding:2rem 0 1rem;border-bottom:1px solid var(--border);margin-bottom:2rem}
header h1{font-size:1.8rem;font-weight:700;color:var(--green);text-shadow:0 0 20px var(--green-glow);letter-spacing:.05em}
header .tagline{color:var(--text-dim);font-size:.85rem;margin-top:.4rem}
header .version{display:inline-block;background:var(--bg3);color:var(--green-dim);font-size:.7rem;padding:.15rem .5rem;border-radius:3px;margin-top:.5rem;border:1px solid var(--border)}
/* Form */
.form-grid{display:grid;grid-template-columns:1fr 1fr;gap:1rem;margin-bottom:1.5rem}
@media(max-width:640px){.form-grid{grid-template-columns:1fr}}
.form-group{display:flex;flex-direction:column;gap:.35rem}
.form-group.full-width{grid-column:1/-1}
label{font-size:.75rem;color:var(--green-dim);text-transform:uppercase;letter-spacing:.08em;font-weight:500}
select,input[type="number"],input[type="range"]{
background:var(--bg3);border:1px solid var(--border);color:var(--text);
font-family:var(--font);font-size:.85rem;padding:.5rem .6rem;border-radius:var(--radius);
outline:none;transition:border-color .2s;width:100%
}
select:hover,input:hover{border-color:var(--border-hover)}
select:focus,input:focus{border-color:var(--green-dim);box-shadow:0 0 0 2px var(--green-glow)}
input[type="range"]{padding:.2rem 0;cursor:pointer;accent-color:var(--green)}
.range-row{display:flex;align-items:center;gap:.75rem}
.range-row input[type="range"]{flex:1}
.range-row .range-val{min-width:3.5rem;text-align:center;font-size:.85rem;color:var(--green);font-weight:600}
/* Radio group */
.radio-group{display:flex;gap:.5rem;flex-wrap:wrap}
.radio-group label{display:flex;align-items:center;gap:.3rem;cursor:pointer;
background:var(--bg3);border:1px solid var(--border);padding:.4rem .7rem;
border-radius:var(--radius);font-size:.8rem;text-transform:none;letter-spacing:0;
color:var(--text);transition:all .2s;font-weight:400}
.radio-group label:hover{border-color:var(--border-hover)}
.radio-group input[type="radio"]{display:none}
.radio-group input[type="radio"]:checked+span{color:var(--green)}
.radio-group label:has(input:checked){border-color:var(--green-dim);background:var(--green-glow)}
/* Custom VRAM */
#custom-vram-group{display:none}
#custom-vram-group.visible{display:flex}
/* Generate button */
.btn-generate{
display:block;width:100%;padding:.7rem;margin-top:.5rem;
background:transparent;border:2px solid var(--green);color:var(--green);
font-family:var(--font);font-size:.95rem;font-weight:600;cursor:pointer;
border-radius:var(--radius);transition:all .25s;text-transform:uppercase;letter-spacing:.1em
}
.btn-generate:hover{background:var(--green);color:var(--bg);box-shadow:0 0 20px var(--green-glow)}
/* Output */
#output{display:none;margin-top:2rem}
#output.visible{display:block}
.output-header{display:flex;justify-content:space-between;align-items:center;flex-wrap:wrap;gap:.5rem;margin-bottom:1rem}
.output-header h2{font-size:1.1rem;color:var(--green)}
.btn-group{display:flex;gap:.5rem;flex-wrap:wrap}
.btn-sm{
background:var(--bg3);border:1px solid var(--border);color:var(--text-dim);
font-family:var(--font);font-size:.7rem;padding:.35rem .6rem;cursor:pointer;
border-radius:var(--radius);transition:all .2s;text-transform:uppercase;letter-spacing:.05em
}
.btn-sm:hover{border-color:var(--green-dim);color:var(--green)}
.btn-sm.copied{border-color:var(--green);color:var(--green);background:var(--green-glow)}
/* Warnings */
.warnings{margin-bottom:1rem}
.warning-item{
display:flex;align-items:flex-start;gap:.5rem;padding:.6rem .8rem;
border-radius:var(--radius);margin-bottom:.4rem;font-size:.8rem;line-height:1.5
}
.warning-item.error{background:var(--red-glow);border:1px solid rgba(255,51,51,.3);color:#ff8888}
.warning-item.warn{background:var(--orange-glow);border:1px solid rgba(255,107,53,.3);color:#ffaa77}
.warning-item.info{background:rgba(0,212,255,.08);border:1px solid rgba(0,212,255,.2);color:#88ddff}
.warning-item .icon{flex-shrink:0;font-size:1rem;line-height:1}
/* Config block */
.config-block{
background:var(--bg2);border:1px solid var(--border);border-radius:var(--radius);
overflow:hidden;margin-bottom:1rem
}
.config-block-header{
display:flex;justify-content:space-between;align-items:center;
padding:.5rem .8rem;background:var(--bg3);border-bottom:1px solid var(--border);
cursor:pointer;user-select:none
}
.config-block-header h3{font-size:.8rem;color:var(--cyan);text-transform:uppercase;letter-spacing:.05em}
.config-block-header .toggle{color:var(--text-dim);font-size:.75rem}
.config-block-body{padding:.8rem}
.config-block-body.collapsed{display:none}
/* Config table */
.config-table{width:100%}
.config-table tr{border-bottom:1px solid var(--border)}
.config-table tr:last-child{border-bottom:none}
.config-table td{padding:.35rem 0;vertical-align:top;font-size:.8rem}
.config-table .key{color:var(--green-dim);width:45%;padding-right:1rem;white-space:nowrap}
.config-table .val{color:var(--text);font-weight:500}
.config-table .note{color:var(--text-dim);font-size:.7rem;display:block;margin-top:.15rem;font-weight:300}
/* Estimates bar */
.estimates{
display:grid;grid-template-columns:repeat(auto-fit,minmax(140px,1fr));gap:.8rem;
margin-bottom:1rem
}
.estimate-card{
background:var(--bg3);border:1px solid var(--border);border-radius:var(--radius);
padding:.6rem .8rem;text-align:center
}
.estimate-card .est-label{font-size:.65rem;color:var(--text-dim);text-transform:uppercase;letter-spacing:.05em}
.estimate-card .est-value{font-size:1.2rem;color:var(--green);font-weight:700;margin-top:.2rem}
.estimate-card .est-value.warning{color:var(--orange)}
.estimate-card .est-value.danger{color:var(--red)}
.estimate-card .est-sub{font-size:.65rem;color:var(--text-dim);margin-top:.15rem}
/* AMD section */
.amd-section{
background:rgba(255,107,53,.05);border:1px solid rgba(255,107,53,.2);
border-radius:var(--radius);padding:.8rem;margin-bottom:1rem
}
.amd-section h3{font-size:.8rem;color:var(--orange);text-transform:uppercase;margin-bottom:.5rem;letter-spacing:.05em}
.amd-section ul{list-style:none;padding:0}
.amd-section li{font-size:.75rem;color:var(--text-dim);padding:.2rem 0;padding-left:1rem;position:relative}
.amd-section li::before{content:">";position:absolute;left:0;color:var(--orange)}
.amd-section code{background:var(--bg);padding:.1rem .3rem;border-radius:3px;color:var(--orange);font-size:.7rem}
/* Raw config */
.raw-config{
background:var(--bg);border:1px solid var(--border);border-radius:var(--radius);
padding:.8rem;overflow-x:auto;font-size:.75rem;line-height:1.7;color:var(--text-dim);
white-space:pre;max-height:400px;overflow-y:auto
}
.raw-config .comment{color:var(--text-dark)}
.raw-config .key-hl{color:var(--cyan)}
.raw-config .val-hl{color:var(--green)}
.raw-config .str-hl{color:var(--orange)}
/* Footer */
footer{text-align:center;padding:2rem 0 1rem;margin-top:2rem;border-top:1px solid var(--border);color:var(--text-dark);font-size:.7rem}
footer a{color:var(--green-dim)}
/* Scrollbar */
::-webkit-scrollbar{width:6px;height:6px}
::-webkit-scrollbar-track{background:var(--bg)}
::-webkit-scrollbar-thumb{background:var(--border);border-radius:3px}
::-webkit-scrollbar-thumb:hover{background:var(--text-dark)}
</style>
</head>
<body>
<div class="container">
<header>
<h1>⚡ LoRA Lab</h1>
<div class="tagline">Training Config Generator — battle-tested configs from real training runs</div>
<div class="version">v1.0.0 · SDXL · FLUX · HunyuanVideo · AMD ROCm support</div>
</header>
<main>
<div class="form-grid">
<div class="form-group">
<label for="gpu">GPU</label>
<select id="gpu">
<option value="" disabled selected>Select your GPU...</option>
<optgroup label="NVIDIA — Consumer">
<option value="3060" data-vram="12" data-vendor="nvidia">RTX 3060 — 12 GB</option>
<option value="3070" data-vram="8" data-vendor="nvidia">RTX 3070 — 8 GB</option>
<option value="3080" data-vram="10" data-vendor="nvidia">RTX 3080 — 10 GB</option>
<option value="3090" data-vram="24" data-vendor="nvidia">RTX 3090 — 24 GB</option>
<option value="4060" data-vram="8" data-vendor="nvidia">RTX 4060 — 8 GB</option>
<option value="4070" data-vram="12" data-vendor="nvidia">RTX 4070 — 12 GB</option>
<option value="4080" data-vram="16" data-vendor="nvidia">RTX 4080 — 16 GB</option>
<option value="4090" data-vram="24" data-vendor="nvidia">RTX 4090 — 24 GB</option>
<option value="5090" data-vram="32" data-vendor="nvidia">RTX 5090 — 32 GB</option>
</optgroup>
<optgroup label="AMD — Consumer">
<option value="7900xtx" data-vram="24" data-vendor="amd">AMD RX 7900 XTX — 24 GB</option>
<option value="9700" data-vram="32" data-vendor="amd">AMD R9700 — 32 GB (RDNA4)</option>
</optgroup>
<optgroup label="Data Center">
<option value="a100-40" data-vram="40" data-vendor="nvidia">A100 — 40 GB</option>
<option value="a100-80" data-vram="80" data-vendor="nvidia">A100 — 80 GB</option>
<option value="h100" data-vram="80" data-vendor="nvidia">H100 — 80 GB</option>
</optgroup>
<option value="custom" data-vram="0" data-vendor="nvidia">Custom (enter VRAM)</option>
</select>
</div>
<div class="form-group" id="custom-vram-group">
<label for="custom-vram">VRAM (GB)</label>
<input type="number" id="custom-vram" min="4" max="192" value="24" step="1">
</div>
<div class="form-group">
<label for="model">Model Type</label>
<select id="model">
<option value="" disabled selected>Select model...</option>
<option value="sd15">Stable Diffusion 1.5</option>
<option value="sdxl">SDXL 1.0</option>
<option value="flux-dev">FLUX.1-dev</option>
<option value="flux-klein">FLUX.2 Klein 4B</option>
<option value="hunyuan-1.0">HunyuanVideo 1.0</option>
<option value="hunyuan-1.5">HunyuanVideo 1.5 I2V</option>
</select>
</div>
<div class="form-group">
<label for="framework">Training Framework</label>
<select id="framework">
<option value="" disabled selected>Select framework...</option>
<option value="kohya">Kohya_ss / sd-scripts</option>
<option value="ai-toolkit">ai-toolkit</option>
<option value="musubi">musubi-tuner</option>
<option value="simpletuner">SimpleTuner</option>
</select>
</div>
<div class="form-group full-width">
<label for="dataset-size">Dataset Size: <span id="dataset-val" class="range-val" style="color:var(--green)">50</span> images</label>
<div class="range-row">
<span style="font-size:.7rem;color:var(--text-dim)">10</span>
<input type="range" id="dataset-size" min="10" max="200" value="50" step="5">
<span style="font-size:.7rem;color:var(--text-dim)">200</span>
</div>
</div>
<div class="form-group full-width">
<label>Quality Priority</label>
<div class="radio-group">
<label><input type="radio" name="quality" value="speed"><span>⚡ Speed</span></label>
<label><input type="radio" name="quality" value="balanced" checked><span>⚖️ Balanced</span></label>
<label><input type="radio" name="quality" value="quality"><span>🎯 Maximum Quality</span></label>
</div>
</div>
</div>
<button class="btn-generate" id="btn-generate">▶ Generate Config</button>
<div id="output">
<div class="output-header">
<h2>📋 Generated Config</h2>
<div class="btn-group">
<button class="btn-sm" id="btn-copy" title="Copy to clipboard">📋 Copy TOML</button>
<button class="btn-sm" id="btn-download-toml" title="Download as TOML">⬇ TOML</button>
<button class="btn-sm" id="btn-download-json" title="Download as JSON">⬇ JSON</button>
</div>
</div>
<div id="warnings" class="warnings"></div>
<div id="estimates" class="estimates"></div>
<div id="config-sections"></div>
<div id="amd-section"></div>
<h3 style="font-size:.8rem;color:var(--cyan);text-transform:uppercase;letter-spacing:.05em;margin:1rem 0 .5rem">Raw Config (TOML)</h3>
<div id="raw-config" class="raw-config"></div>
</div>
</main>
<footer>
Built by <a href="https://ziondelta.com/alpha/" target="_blank">Alpha</a> · LoRA Lab v1.0.0 ·
<a href="https://github.com/AlphafromZion/lora-lab" target="_blank">GitHub</a>
</footer>
</div>
<script>
// ==================== MODEL DATABASE ====================
const MODELS = {
'sd15': {
name: 'Stable Diffusion 1.5',
baseVram: { fp16: 4, bf16: 4, fp8: 3 },
resolution: 512,
bucketMin: 256, bucketMax: 768,
dims: { speed: [16, 8], balanced: [32, 16], quality: [64, 32] },
optimizer: { speed: ['adamw8bit', '1e-4'], balanced: ['adafactor', '8e-5'], quality: ['prodigy', '1'] },
epochs: { speed: 8, balanced: 12, quality: 20 },
precision: ['fp16', 'bf16'],
frameworks: ['kohya', 'ai-toolkit', 'simpletuner'],
notes: [],
stepsPerImage: 1.0,
},
'sdxl': {
name: 'SDXL 1.0',
baseVram: { fp16: 7, bf16: 7, fp8: 5 },
resolution: 1024,
bucketMin: 512, bucketMax: 1536,
dims: { speed: [16, 8], balanced: [32, 16], quality: [64, 32] },
optimizer: { speed: ['adamw8bit', '1e-4'], balanced: ['adafactor', '8e-5'], quality: ['prodigy', '1'] },
epochs: { speed: 8, balanced: 12, quality: 20 },
precision: ['fp16', 'bf16'],
frameworks: ['kohya', 'ai-toolkit', 'simpletuner'],
notes: [],
stepsPerImage: 1.2,
},
'flux-dev': {
name: 'FLUX.1-dev',
baseVram: { fp16: 12, bf16: 12, fp8: 8 },
resolution: 1024,
bucketMin: 512, bucketMax: 1536,
dims: { speed: [32, 16], balanced: [64, 32], quality: [128, 64] },
optimizer: { speed: ['adamw8bit', '1e-4'], balanced: ['adafactor', '8e-5'], quality: ['adafactor', '4e-5'] },
epochs: { speed: 8, balanced: 16, quality: 24 },
precision: ['fp16', 'bf16', 'fp8'],
frameworks: ['kohya', 'ai-toolkit', 'simpletuner'],
notes: [
'Cannot use shuffle_caption with cache_text_encoder_outputs',
'Must accept FLUX.1-dev license on HuggingFace before downloading',
'fp8 reduces VRAM ~4GB but may slightly impact quality',
],
stepsPerImage: 1.5,
},
'flux-klein': {
name: 'FLUX.2 Klein 4B',
baseVram: { fp16: 8, bf16: 8, fp8: 5 },
resolution: 1024,
bucketMin: 512, bucketMax: 1536,
dims: { speed: [32, 16], balanced: [64, 32], quality: [128, 64] },
optimizer: { speed: ['adamw8bit', '1e-4'], balanced: ['adafactor', '8e-5'], quality: ['adafactor', '4e-5'] },
epochs: { speed: 8, balanced: 16, quality: 24 },
precision: ['fp16', 'bf16', 'fp8'],
frameworks: ['kohya', 'ai-toolkit', 'simpletuner'],
notes: ['Smaller 4B model — trains faster than FLUX.1-dev with similar quality'],
stepsPerImage: 1.2,
},
'hunyuan-1.0': {
name: 'HunyuanVideo 1.0',
baseVram: { bf16: 30, fp16: 30 },
resolution: 512,
bucketMin: 256, bucketMax: 768,
dims: { speed: [64, 32], balanced: [128, 64], quality: [128, 64] },
optimizer: { speed: ['adafactor', '4e-5'], balanced: ['adafactor', '2e-5'], quality: ['adafactor', '1e-5'] },
epochs: { speed: 16, balanced: 32, quality: 48 },
precision: ['bf16'],
frameworks: ['musubi'],
notes: [
'Video model — requires significantly more VRAM than image models',
'bf16 only — fp16 causes NaN, fp8 training untested',
'Use max_grad_norm 1.0 to prevent gradient explosion',
'Cache text encoder outputs to CPU to save VRAM',
],
blocksToSwap: { threshold: 32, count: 20 },
stepsPerImage: 4.0,
requireGradNorm: true,
requireWarmup: true,
},
'hunyuan-1.5': {
name: 'HunyuanVideo 1.5 I2V',
baseVram: { bf16: 33, fp16: 33 },
resolution: 512,
bucketMin: 256, bucketMax: 768,
dims: { speed: [64, 32], balanced: [128, 64], quality: [128, 64] },
optimizer: { speed: ['adafactor', '4e-5'], balanced: ['adafactor', '2e-5'], quality: ['adafactor', '1e-5'] },
epochs: { speed: 16, balanced: 32, quality: 48 },
precision: ['bf16'],
frameworks: ['musubi'],
notes: [
'I2V (Image-to-Video) model — heaviest VRAM requirements',
'MUST use bf16 — fp8 training is untested and likely broken',
'MUST use max_grad_norm 1.0 + lr_warmup_steps 50 — otherwise NaN at step 2',
'Text encoder caching on CPU ONLY — GPU caching crashes even in bf16',
'Framework: musubi-tuner ONLY — no other framework supports this yet',
'Total model size ~53GB in bf16 — blocks_to_swap is essential for <80GB GPUs',
],
blocksToSwap: { threshold: 40, count: 20 },
stepsPerImage: 5.0,
requireGradNorm: true,
requireWarmup: true,
},
};
// ==================== GPU DATABASE ====================
function getGpuInfo() {
const sel = document.getElementById('gpu');
const opt = sel.options[sel.selectedIndex];
if (!opt || !opt.value) return null;
let vram, vendor;
if (opt.value === 'custom') {
vram = parseInt(document.getElementById('custom-vram').value) || 24;
// Check if user selected AMD vendor for custom
vendor = 'nvidia'; // default for custom
} else {
vram = parseInt(opt.dataset.vram);
vendor = opt.dataset.vendor;
}
return { id: opt.value, vram, vendor, name: opt.textContent.trim() };
}
// ==================== CONFIG GENERATOR ====================
function generateConfig() {
const gpu = getGpuInfo();
const modelKey = document.getElementById('model').value;
const frameworkKey = document.getElementById('framework').value;
const datasetSize = parseInt(document.getElementById('dataset-size').value);
const quality = document.querySelector('input[name="quality"]:checked')?.value || 'balanced';
// Validation
if (!gpu || !modelKey || !frameworkKey) {
alert('Please select GPU, Model, and Framework');
return null;
}
const model = MODELS[modelKey];
const warnings = [];
const infos = [];
let canRun = true;
// Framework compatibility
if (!model.frameworks.includes(frameworkKey)) {
const supported = model.frameworks.map(f => {
return { kohya: 'Kohya_ss', 'ai-toolkit': 'ai-toolkit', musubi: 'musubi-tuner', simpletuner: 'SimpleTuner' }[f];
}).join(', ');
warnings.push({ level: 'error', text: `${model.name} is not supported by this framework. Use: ${supported}` });
canRun = false;
}
// Determine precision
let precision = model.precision[0]; // default to first
if (gpu.vendor === 'amd') {
// AMD: always bf16 for training
precision = model.precision.includes('bf16') ? 'bf16' : model.precision[0];
if (model.precision.includes('fp8')) {
infos.push({ level: 'info', text: 'AMD ROCm: fp8 works for inference on ROCm 7.2+ but training should use bf16' });
}
} else {
// NVIDIA: try fp8 if VRAM constrained and supported
const baseVram = model.baseVram[precision] || Object.values(model.baseVram)[0];
if (gpu.vram < baseVram + 4 && model.precision.includes('fp8')) {
precision = 'fp8';
} else if (model.precision.includes('bf16')) {
precision = 'bf16';
}
}
// Force bf16 for HunyuanVideo
if (modelKey.startsWith('hunyuan')) {
precision = 'bf16';
}
const baseVram = model.baseVram[precision] || model.baseVram[Object.keys(model.baseVram)[0]];
// VRAM check
let blocksToSwap = 0;
let gradCheckpoint = true;
let estimatedVram = baseVram;
if (model.blocksToSwap && gpu.vram < model.blocksToSwap.threshold) {
blocksToSwap = model.blocksToSwap.count;
estimatedVram = Math.max(baseVram - 8, baseVram * 0.7);
warnings.push({ level: 'warn', text: `blocks_to_swap ${blocksToSwap} enabled — offloads transformer blocks to CPU. Training will be ~30-50% slower.` });
}
if (gpu.vram < baseVram * 0.6) {
warnings.push({ level: 'error', text: `Your GPU (${gpu.vram}GB) likely cannot run ${model.name}. Minimum ~${Math.ceil(baseVram * 0.6)}GB VRAM required even with block swapping.` });
canRun = false;
} else if (gpu.vram < baseVram) {
if (!model.blocksToSwap) {
warnings.push({ level: 'error', text: `Your GPU (${gpu.vram}GB) does not have enough VRAM for ${model.name} (needs ~${baseVram}GB in ${precision}). No block_swap available for this model.` });
canRun = false;
}
}
// AMD block_swap warning
if (gpu.vendor === 'amd' && blocksToSwap > 0) {
warnings.push({ level: 'warn', text: 'AMD + blocks_to_swap: PyTorch nightly ROCm 7.0 is BROKEN (hipErrorIllegalAddress). Use official ROCm 7.2 wheels from repo.radeon.com.' });
}
// Config values
const [dim, alpha] = model.dims[quality];
const [optimizer, lr] = model.optimizer[quality];
const epochs = model.epochs[quality];
// Batch size based on available VRAM headroom
const vramHeadroom = gpu.vram - estimatedVram;
let batchSize = 1;
if (vramHeadroom > 12) batchSize = 4;
else if (vramHeadroom > 6) batchSize = 2;
else batchSize = 1;
// Calculate steps
const stepsPerEpoch = Math.ceil(datasetSize / batchSize);
const totalSteps = stepsPerEpoch * epochs;
// Time estimate (rough: seconds per step)
let secPerStep = model.stepsPerImage;
if (blocksToSwap > 0) secPerStep *= 1.5;
if (gpu.vram >= 40) secPerStep *= 0.6; // datacenter GPUs are faster
if (gpu.vendor === 'amd') secPerStep *= 1.1; // AMD slightly slower typically
const totalMinutes = Math.round((totalSteps * secPerStep) / 60);
// VRAM estimate (more detailed)
const loraOverhead = dim * 0.01; // rough
const batchOverhead = batchSize * 0.5;
estimatedVram = Math.round((estimatedVram + loraOverhead + batchOverhead) * 10) / 10;
// Warmup steps for HunyuanVideo
let warmupSteps = 0;
if (model.requireWarmup) {
warmupSteps = 50;
}
// Resolution
const resolution = model.resolution;
// Cache settings
const cacheLatents = true;
const cacheLatentsDisk = gpu.vram < 16;
const cacheTextEncoderOutputs = modelKey.startsWith('flux') || modelKey.startsWith('hunyuan');
const cacheTextEncoderOnCPU = modelKey.startsWith('hunyuan');
// Model notes
model.notes.forEach(n => infos.push({ level: 'info', text: n }));
// HuggingFace license reminder for FLUX
if (modelKey === 'flux-dev') {
infos.push({ level: 'info', text: 'Remember to accept the FLUX.1-dev license at huggingface.co/black-forest-labs/FLUX.1-dev' });
}
const config = {
// Meta
_model: model.name,
_framework: frameworkKey,
_gpu: gpu.name,
_quality: quality,
// Network
network_dim: dim,
network_alpha: alpha,
network_module: 'networks.lora',
// Optimizer
optimizer_type: optimizer,
learning_rate: lr,
// Training
max_train_epochs: epochs,
max_train_steps: totalSteps,
train_batch_size: batchSize,
max_data_loader_n_workers: 2,
// Resolution
resolution: `${resolution},${resolution}`,
enable_bucket: true,
min_bucket_reso: model.bucketMin,
max_bucket_reso: model.bucketMax,
// Precision
mixed_precision: precision === 'fp8' ? 'bf16' : precision,
full_precision: precision,
// Memory
gradient_checkpointing: gradCheckpoint,
blocks_to_swap: blocksToSwap > 0 ? blocksToSwap : undefined,
// Cache
cache_latents: cacheLatents,
cache_latents_to_disk: cacheLatentsDisk,
cache_text_encoder_outputs: cacheTextEncoderOutputs || undefined,
cache_text_encoder_outputs_to_disk: cacheTextEncoderOnCPU || undefined,
// Stability
max_grad_norm: model.requireGradNorm ? 1.0 : undefined,
lr_warmup_steps: warmupSteps > 0 ? warmupSteps : undefined,
lr_scheduler: 'cosine',
// Dataset
dataset_size: datasetSize,
// Misc
seed: 42,
clip_skip: modelKey === 'sd15' ? 2 : undefined,
save_every_n_epochs: Math.max(1, Math.floor(epochs / 4)),
save_model_as: 'safetensors',
caption_extension: '.txt',
shuffle_caption: cacheTextEncoderOutputs ? false : true,
keep_tokens: 1,
};
return {
config,
warnings: [...warnings, ...infos],
canRun,
estimates: {
vram: estimatedVram,
gpuVram: gpu.vram,
time: totalMinutes,
steps: totalSteps,
epochs: epochs,
},
isAmd: gpu.vendor === 'amd',
modelKey,
precision,
blocksToSwap,
};
}
// ==================== TOML FORMATTER ====================
function configToToml(cfg) {
const c = cfg.config;
const lines = [];
lines.push(`# LoRA Training Config — generated by LoRA Lab`);
lines.push(`# Model: ${c._model} | GPU: ${c._gpu} | Quality: ${c._quality}`);
lines.push(`# https://github.com/AlphafromZion/lora-lab`);
lines.push('');
lines.push('[network]');
lines.push(`network_module = "${c.network_module}"`);
lines.push(`network_dim = ${c.network_dim}`);
lines.push(`network_alpha = ${c.network_alpha}`);
lines.push('');
lines.push('[optimizer]');
lines.push(`optimizer_type = "${c.optimizer_type}"`);
lines.push(`learning_rate = ${c.learning_rate}`);
if (c.lr_scheduler) lines.push(`lr_scheduler = "${c.lr_scheduler}"`);
if (c.lr_warmup_steps) lines.push(`lr_warmup_steps = ${c.lr_warmup_steps}`);
if (c.max_grad_norm !== undefined) lines.push(`max_grad_norm = ${c.max_grad_norm}`);
lines.push('');
lines.push('[training]');
lines.push(`max_train_epochs = ${c.max_train_epochs}`);
lines.push(`train_batch_size = ${c.train_batch_size}`);
lines.push(`max_data_loader_n_workers = ${c.max_data_loader_n_workers}`);
lines.push(`seed = ${c.seed}`);
lines.push(`save_every_n_epochs = ${c.save_every_n_epochs}`);
lines.push(`save_model_as = "${c.save_model_as}"`);
lines.push('');
lines.push('[resolution]');
lines.push(`resolution = "${c.resolution}"`);
lines.push(`enable_bucket = ${c.enable_bucket}`);
lines.push(`min_bucket_reso = ${c.min_bucket_reso}`);
lines.push(`max_bucket_reso = ${c.max_bucket_reso}`);
lines.push('');
lines.push('[precision]');
lines.push(`mixed_precision = "${c.mixed_precision}"`);
if (cfg.precision === 'fp8') lines.push(`# Using fp8 quantization for model weights`);
lines.push('');
lines.push('[memory]');
lines.push(`gradient_checkpointing = ${c.gradient_checkpointing}`);
if (c.blocks_to_swap) lines.push(`blocks_to_swap = ${c.blocks_to_swap}`);
lines.push('');
lines.push('[cache]');
lines.push(`cache_latents = ${c.cache_latents}`);
lines.push(`cache_latents_to_disk = ${c.cache_latents_to_disk}`);
if (c.cache_text_encoder_outputs) lines.push(`cache_text_encoder_outputs = ${c.cache_text_encoder_outputs}`);
if (c.cache_text_encoder_outputs_to_disk) lines.push(`cache_text_encoder_outputs_to_disk = ${c.cache_text_encoder_outputs_to_disk}`);
lines.push('');
lines.push('[captions]');
lines.push(`caption_extension = "${c.caption_extension}"`);
lines.push(`shuffle_caption = ${c.shuffle_caption}`);
lines.push(`keep_tokens = ${c.keep_tokens}`);
if (c.clip_skip) lines.push(`clip_skip = ${c.clip_skip}`);
return lines.join('\n');
}
function configToJson(cfg) {
const c = { ...cfg.config };
delete c._model; delete c._framework; delete c._gpu; delete c._quality;
// Clean undefined
Object.keys(c).forEach(k => { if (c[k] === undefined) delete c[k]; });
return JSON.stringify(c, null, 2);
}
// ==================== RENDER ====================
function renderOutput(result) {
const output = document.getElementById('output');
output.classList.add('visible');
// Warnings
const warningsEl = document.getElementById('warnings');
warningsEl.innerHTML = result.warnings.map(w => {
const icon = w.level === 'error' ? '🚫' : w.level === 'warn' ? '⚠️' : 'ℹ️';
return `<div class="warning-item ${w.level}"><span class="icon">${icon}</span><span>${w.text}</span></div>`;
}).join('');
// Estimates
const est = result.estimates;
const vramClass = est.vram > est.gpuVram ? 'danger' : est.vram > est.gpuVram * 0.85 ? 'warning' : '';
const timeStr = est.time < 60 ? `${est.time}m` : `${Math.floor(est.time/60)}h ${est.time%60}m`;
document.getElementById('estimates').innerHTML = `
<div class="estimate-card">
<div class="est-label">Est. VRAM Usage</div>
<div class="est-value ${vramClass}">~${est.vram} GB</div>
<div class="est-sub">of ${est.gpuVram} GB available</div>
</div>
<div class="estimate-card">
<div class="est-label">Training Time</div>
<div class="est-value">~${timeStr}</div>
<div class="est-sub">${est.steps.toLocaleString()} total steps</div>
</div>
<div class="estimate-card">
<div class="est-label">Epochs</div>
<div class="est-value">${est.epochs}</div>
<div class="est-sub">${result.config.save_every_n_epochs} checkpoint saves</div>
</div>
<div class="estimate-card">
<div class="est-label">Precision</div>
<div class="est-value">${result.precision.toUpperCase()}</div>
<div class="est-sub">${result.blocksToSwap > 0 ? 'blocks_swap ' + result.blocksToSwap : 'no block swap'}</div>
</div>
`;
// Config sections
const c = result.config;
const sections = [
{
title: 'Network Architecture',
rows: [
['network_dim', c.network_dim, 'Rank of LoRA matrices — higher = more capacity, more VRAM'],
['network_alpha', c.network_alpha, 'Scaling factor — typically half of dim for stable training'],
['network_module', c.network_module, ''],
]
},
{
title: 'Optimizer & Learning Rate',
rows: [
['optimizer_type', c.optimizer_type, optimizerNote(c.optimizer_type)],
['learning_rate', c.learning_rate, lrNote(c.optimizer_type)],
['lr_scheduler', c.lr_scheduler, 'Cosine decay — gradually reduces LR'],
...(c.lr_warmup_steps ? [['lr_warmup_steps', c.lr_warmup_steps, 'Critical for HunyuanVideo — prevents NaN at early steps']] : []),
...(c.max_grad_norm !== undefined ? [['max_grad_norm', c.max_grad_norm, 'Gradient clipping — essential for video model stability']] : []),
]
},
{
title: 'Training Schedule',
rows: [
['max_train_epochs', c.max_train_epochs, ''],
['train_batch_size', c.train_batch_size, `Based on ~${Math.round(result.estimates.gpuVram - result.estimates.vram + c.train_batch_size * 0.5)}GB headroom`],
['total_steps', result.estimates.steps, `${c.dataset_size} images × ${c.max_train_epochs} epochs ÷ batch ${c.train_batch_size}`],
['save_every_n_epochs', c.save_every_n_epochs, ''],
['seed', c.seed, ''],
]
},
{
title: 'Resolution & Bucketing',
rows: [
['resolution', c.resolution, ''],
['enable_bucket', c.enable_bucket, 'Allows mixed aspect ratios in training data'],
['min_bucket_reso', c.min_bucket_reso, ''],
['max_bucket_reso', c.max_bucket_reso, ''],
]
},
{
title: 'Memory & Precision',
rows: [
['mixed_precision', c.mixed_precision, ''],
['gradient_checkpointing', c.gradient_checkpointing, 'Trades compute for memory — always on for large models'],
...(c.blocks_to_swap ? [['blocks_to_swap', c.blocks_to_swap, 'Offloads transformer blocks to CPU — slower but fits in VRAM']] : []),
]
},
{
title: 'Caching',
rows: [
['cache_latents', c.cache_latents, 'Pre-encodes images — saves VRAM during training'],
['cache_latents_to_disk', c.cache_latents_to_disk, c.cache_latents_to_disk ? 'Disk caching — slower but saves RAM' : 'In-memory caching'],
...(c.cache_text_encoder_outputs ? [['cache_text_encoder_outputs', c.cache_text_encoder_outputs, 'Pre-encodes captions — required for FLUX/Hunyuan']] : []),
...(c.cache_text_encoder_outputs_to_disk ? [['cache_text_encoder_outputs_to_disk', c.cache_text_encoder_outputs_to_disk, 'CPU-only text encoder cache — GPU caching crashes for video models']] : []),
]
},
{
title: 'Captions',
rows: [
['caption_extension', c.caption_extension, ''],
['shuffle_caption', c.shuffle_caption, c.shuffle_caption ? 'Randomizes tag order for better generalization' : 'Disabled — incompatible with text encoder caching'],
['keep_tokens', c.keep_tokens, 'First N tokens always kept in position (trigger word)'],
...(c.clip_skip ? [['clip_skip', c.clip_skip, 'SD 1.5 typically uses clip_skip 2']] : []),
]
},
];
document.getElementById('config-sections').innerHTML = sections.map((s, i) => `
<div class="config-block">
<div class="config-block-header" onclick="this.nextElementSibling.classList.toggle('collapsed')">
<h3>${s.title}</h3>
<span class="toggle">▼</span>
</div>
<div class="config-block-body">
<table class="config-table">
${s.rows.map(([k, v, note]) => `
<tr>
<td class="key">${k}</td>
<td class="val">${v}${note ? `<span class="note">${note}</span>` : ''}</td>
</tr>
`).join('')}
</table>
</div>
</div>
`).join('');
// AMD section
if (result.isAmd) {
document.getElementById('amd-section').innerHTML = `
<div class="amd-section">
<h3>🔴 AMD ROCm Notes</h3>
<ul>
<li>Install official ROCm 7.2 wheels from <code>repo.radeon.com</code> — NOT PyTorch nightly</li>
<li>PyTorch nightly ROCm 7.0 is <strong style="color:var(--red)">BROKEN</strong> for block_swap (hipErrorIllegalAddress)</li>
<li>fp8 (e4m3fn) works on ROCm 7.2+ for <strong>inference only</strong> — training must use bf16</li>
<li>Set environment variables before training:</li>
<li><code>export HSA_ENABLE_SDMA=0</code></li>
<li><code>export GPU_MAX_HW_QUEUES=1</code></li>
<li>These are critical for training stability — without them, expect random hangs</li>
<li>ROCm memory management differs from CUDA — monitor with <code>rocm-smi</code> not <code>nvidia-smi</code></li>
</ul>
</div>
`;
} else {
document.getElementById('amd-section').innerHTML = '';
}
// Raw config
const toml = configToToml(result);
const rawEl = document.getElementById('raw-config');
rawEl.innerHTML = toml.split('\n').map(line => {
if (line.startsWith('#')) return `<span class="comment">${esc(line)}</span>`;
if (line.startsWith('[')) return `<span class="str-hl">${esc(line)}</span>`;
const eq = line.indexOf('=');
if (eq > 0) {
const key = line.substring(0, eq).trim();
const val = line.substring(eq + 1).trim();
return `<span class="key-hl">${esc(key)}</span> = <span class="val-hl">${esc(val)}</span>`;
}
return esc(line);
}).join('\n');
// Store for copy/download
window._lastToml = toml;
window._lastJson = configToJson(result);
// Scroll to output
output.scrollIntoView({ behavior: 'smooth', block: 'start' });
}
function esc(s) { return s.replace(/&/g,'&').replace(/</g,'<').replace(/>/g,'>'); }
function optimizerNote(opt) {
const notes = {
'adafactor': 'Memory-efficient, no momentum state — great for LoRA',
'adamw8bit': '8-bit Adam — fast, lower memory than full AdamW',
'prodigy': 'Self-adapting LR — set learning_rate to 1 and let it find optimal',
};
return notes[opt] || '';
}
function lrNote(opt) {
if (opt === 'prodigy') return 'Prodigy ignores this — it self-adapts';
return '';
}
// ==================== EVENT HANDLERS ====================
document.getElementById('gpu').addEventListener('change', function() {
const grp = document.getElementById('custom-vram-group');
grp.classList.toggle('visible', this.value === 'custom');
});
document.getElementById('dataset-size').addEventListener('input', function() {
document.getElementById('dataset-val').textContent = this.value;
});
document.getElementById('btn-generate').addEventListener('click', function() {
const result = generateConfig();
if (result) renderOutput(result);
});
document.getElementById('btn-copy').addEventListener('click', function() {
navigator.clipboard.writeText(window._lastToml).then(() => {
this.classList.add('copied');
this.textContent = '✅ Copied!';
setTimeout(() => { this.classList.remove('copied'); this.textContent = '📋 Copy TOML'; }, 2000);
});
});
document.getElementById('btn-download-toml').addEventListener('click', function() {
download(window._lastToml, 'lora-config.toml', 'text/plain');
});
document.getElementById('btn-download-json').addEventListener('click', function() {
download(window._lastJson, 'lora-config.json', 'application/json');
});
function download(content, name, type) {
const blob = new Blob([content], { type });
const url = URL.createObjectURL(blob);
const a = document.createElement('a');
a.href = url; a.download = name; a.click();
URL.revokeObjectURL(url);
}
// Auto-select framework when model changes
document.getElementById('model').addEventListener('change', function() {
const fw = document.getElementById('framework');
const model = MODELS[this.value];
if (model && model.frameworks.length === 1) {
fw.value = model.frameworks[0];
}
});
</script>
</body>
</html>