/* MMDiff project page — bottom sections: Method, Results, Citation, Footer */ const DSb = window.MMDiffDesignSystem_07b833; const { Button: Btn, Badge: Bdg, Kicker: Kick, SpectrumBar: Spec, Card: Crd, CodeBlock: Code, Tag: Chip } = DSb; const Db = window.MMD; const Ib = window.Icons; function SectionTitle({ index, kicker, title, sub, onDark }) { return (
{kicker}

{title}

{sub ?

{sub}

: null}
); } function Method() { return (
MMDiff architecture diagram
Concept tokens {Db.concepts.map((c) => {c.label})}
{Db.steps.map((s) => (
{s.n}

{s.title}

{s.body}

 ))}
); } function MapTile({ src, label, tint }) { return (
{label}
{label}
); } function SceneCard({ scene }) { const base = 'assets/gen/' + scene.id; return (
{scene.label}
{/* Generated image — the anchor */}
{scene.label} Generated
{/* the arrow / "decodes to" */}
decodes to
{/* Three decoded maps */}
{Db.modalities.map((m) => ( ))}
); } function fmtCell(raw) { if (typeof raw !== 'string') return { v: raw }; if (raw[0] === '*') return { v: raw.slice(1), best: true }; if (raw[0] === '~') return { v: raw.slice(1), second: true }; return { v: raw }; } function MetaMark({ v }) { if (v === '✓') return ✓; if (v === '✗') return ✗; return {v}; } function ResultsTables() { const tabs = Db.tableTabs; const [active, setActive] = React.useState(tabs[0]); const t = Db.tables[active]; const leadN = t.lead.length; return (
Training on the synthetic data

Since every generated image already comes with its labels, MMDiff is a strong way to make synthetic training data. Models trained on its images come close to real data and outperform every earlier synthetic generator, on segmentation (PASCAL VOC), saliency (DUTS) and depth (NYU Depth v2). The last tab looks at feature quality on real images.

{tabs.map((tb) => setActive(tb)}>{tb})}
{t.groups ? ( {t.groups.map((g, i) => ( ))} ) : null} {t.lead.map((c, i) => ( ))} {t.cols.map((c, i) => ( ))} {t.rows.map((r, ri) => ( {r.lead.map((cell, ci) => ( ))} {r.cells.map((raw, ci) => { const c = fmtCell(raw); return ( ); })} ))}
{g.label}
{c}{c}
{ci === 0 ? cell : }{c.v}

{t.caption} Bold = best, underline = second best.

); } function Results() { return (
{Db.genScenes.map((s) => )}

Synthetic samples · every generated image comes with its saliency, segmentation and depth, all produced together.

); } function UseCases() { return (
{Db.useCases.map((u) => (
{u.tag}

{u.title}

{u.body}

 ))}
); } function Citation() { return (
Read on arXiv View code Hugging Face Available after release
); } function Footer() { return ( ); } Object.assign(window, { Method, Results, UseCases, Citation, Footer });