feat(playground): image & video generation tabs + real chat metrics
The infosave collection now ships media models (Lumina-Image-2.0, MiniMax-H3 Turbo) that cortiq runs via `imagine`/`animate` CLI, not `cortiq serve`. The gateway grows a media API: local .cmf files are classified by `cortiq info` (chat/image/video, cached), generations run as tracked jobs with step progress parsed from stdout, one at a time, cancellable, outputs under <data>/media. The playground becomes three tabs. Image: width/height/steps/CFG/seed, live progress, result rendered from P6 PPM to a canvas in the browser (PNG export via toBlob). Video: frames/steps/quality/seed plus optional first/last keyframes (rasterized to PPM client-side); the MJPEG+PCM AVI is decoded in JS and played on a canvas synced to the wav — no ffmpeg on either side (mp4 remux happens opportunistically when ffmpeg exists). A metrics card shows resolution, steps, seed, total time, time per step/frame and file size. Chat metrics: the local cortiq server sends no usage chunk in streams, so token counts showed 0/0. tap_stream now injects one OpenAI-shaped usage event ahead of [DONE] with the gateway's ~4-chars/token estimate, flagged `cortiq_estimated`; the UI renders it as "≈ N / M" and adds tok/s and time-to-first-token. Registration skips media packs (a `cortiq serve` entry for a diffusion model would only fail) in both Register and the wizard's Finish. 36 new UI strings translated across all 7 languages.
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Vendored
+38
@@ -468,4 +468,42 @@ export default {
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"onboarding.deleteFile": "Die .cmf-Datei von der Festplatte löschen",
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"onboarding.confirmDelete": "„{id}“ löschen? Die .cmf-Datei wird von der Festplatte entfernt.",
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"onboarding.unregistered": "Ebenfalls abgemeldet: {ids}",
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// playground: media generation + streaming metrics
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"pg.tab.chat": "Chat",
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"pg.tab.image": "Bild",
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"pg.tab.video": "Video",
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"pg.generate": "Generieren",
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"pg.generating": "Wird generiert…",
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"pg.genFailed": "Generierung fehlgeschlagen — siehe Log.",
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"pg.result": "Ergebnis",
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"pg.genMetrics": "Generierungsmetriken",
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"pg.imagePromptPlaceholder": "ein Rotfuchs im Schnee bei Sonnenuntergang, fotorealistisch, detailliertes Fell",
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"pg.videoPromptPlaceholder": "ein Corgi mit Kochmütze wirft einen Pfannkuchen, Brutzeln und ein fröhliches Bellen",
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"pg.imageEmpty": "Das generierte Bild erscheint hier.",
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"pg.videoEmpty": "Der generierte Clip (mit Ton) erscheint hier.",
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"pg.width": "Breite",
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"pg.height": "Höhe",
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"pg.steps": "Schritte",
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"pg.seed": "Seed",
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"pg.randomSeed": "Zufälliger Seed",
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"pg.cfgHint": "Guidance-Stärke; ≤ 1 schaltet CFG ab und halbiert die Arbeit.",
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"pg.frames": "Frames",
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"pg.framesHint": "Bei 24 fps; auf das Raster des Modells gerundet.",
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"pg.quality": "JPEG-Qualität",
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"pg.videoSizeHint": "Vielfache von 32; die trainierte kurze Kante ist 768.",
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"pg.videoStepsHint": "Die Turbo-LoRA ist auf 4 Schritte trainiert.",
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"pg.firstFrame": "Erstes Frame (optional)",
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"pg.lastFrame": "Letztes Frame (optional)",
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"pg.keyframeHint": "Macht den Lauf zu Keyframe-to-Video.",
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"pg.resolution": "Auflösung",
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"pg.genTime": "Generierungszeit",
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"pg.timePerStep": "Pro Schritt",
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"pg.timePerFrame": "Pro Frame",
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"pg.fileSize": "Dateigröße",
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"pg.duration": "Dauer",
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"pg.play": "Abspielen",
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"pg.noMediaModels": "Noch kein lokales {kind}-Modell. Lade eines (z. B. aus der infosave-Sammlung) unter Import herunter:",
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"pg.tokSpeed": "Geschwindigkeit",
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"pg.ttft": "Erstes Token",
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};
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