PLAN.md: Produktions-DB Schema, Verzeichnisstruktur, Statusmaschine, Orchestrator-Logik, CLI
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ki-video/PLAN.md
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ki-video/PLAN.md
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@ -362,13 +362,228 @@ Empfehlung: Erst aufbauen wenn 3080-Rig voll ausgelastet. Alternative: verkaufen
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Nicht aktiv aufbauen. Falls 3080-Rig irgendwann an Kapazitaetsgrenzen stoesst: 1-Karten-Test mit Whisper.
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Nicht aktiv aufbauen. Falls 3080-Rig irgendwann an Kapazitaetsgrenzen stoesst: 1-Karten-Test mit Whisper.
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Wenn selbst das nicht lohnt: verkaufen und in NVMe-Storage oder RAM investieren.
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Wenn selbst das nicht lohnt: verkaufen und in NVMe-Storage oder RAM investieren.
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## Produktions-Datenbank + Orchestrierung
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Zentrale Steuerung: **eine SQLite-Datei** (`production.db`) auf ki-tower.
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Alles passiert hier: Kanaele, Videos, Szenen, Jobs, Assets, Status.
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### Datenbank-Schema
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```sql
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-- Kanaele (je einer pro YouTube-Kanal)
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CREATE TABLE channels (
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id INTEGER PRIMARY KEY,
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name TEXT NOT NULL, -- "Geld & Imperien Stil"
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slug TEXT UNIQUE NOT NULL, -- "kanal-a"
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avatar_path TEXT NOT NULL, -- "/data/channels/kanal-a/avatar.png"
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voice_path TEXT NOT NULL, -- "/data/channels/kanal-a/voice-sample.wav"
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style TEXT NOT NULL, -- "pip_20" oder "fullscreen"
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prompt_template TEXT, -- System-Prompt fuer GPT-5.4 Skripte (Ton, Stil, Persoenlichkeit)
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created_at DATETIME DEFAULT CURRENT_TIMESTAMP
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);
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-- Videos (ein Eintrag pro Video)
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CREATE TABLE videos (
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id INTEGER PRIMARY KEY,
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channel_id INTEGER REFERENCES channels(id),
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title TEXT NOT NULL, -- "Irans Hyperschall-Schlag"
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topic TEXT, -- Thema + Recherche-Notizen
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status TEXT DEFAULT 'draft', -- draft → script → scenes → producing → assembly → review → published
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script TEXT, -- Fertiges Skript (aus GPT-5.4 + Review)
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scene_plan TEXT, -- JSON: Szenenplan (aus Qwen 14B)
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voiceover_path TEXT, -- Pfad zur fertigen WAV
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avatar_path TEXT, -- Pfad zum fertigen Avatar-Video
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subtitle_path TEXT, -- Pfad zur SRT-Datei
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final_path TEXT, -- Pfad zum fertigen MP4
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thumbnail_path TEXT, -- Pfad zum Thumbnail
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yt_title TEXT, -- YouTube-Titel
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yt_description TEXT, -- YouTube-Description
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yt_tags TEXT, -- YouTube-Tags (JSON array)
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created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
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published_at DATETIME
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);
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-- Szenen (aus dem Szenenplan, je eine Zeile pro Szene)
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CREATE TABLE scenes (
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id INTEGER PRIMARY KEY,
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video_id INTEGER REFERENCES videos(id),
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scene_nr INTEGER NOT NULL,
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scene_type TEXT NOT NULL, -- "hero", "standard", "karte", "infografik"
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prompt TEXT NOT NULL, -- Bildprompt (EN)
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overlay TEXT, -- Text-Overlay (oder NULL)
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duration_s REAL NOT NULL, -- Geschaetzte Dauer in Sekunden
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image_path TEXT, -- Pfad zum generierten Bild (nach Produktion)
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upscaled BOOLEAN DEFAULT 0,
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status TEXT DEFAULT 'pending' -- pending → generating → done → failed
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);
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-- GPU-Jobs (jede Aufgabe die ein Worker erledigt)
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CREATE TABLE jobs (
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id INTEGER PRIMARY KEY,
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video_id INTEGER REFERENCES videos(id),
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job_type TEXT NOT NULL, -- "tts", "sdxl", "flux", "sadtalker", "whisper", "esrgan", "assembly"
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gpu TEXT, -- "3090", "3080_0", "3080_1", "3080_2", "3080_3"
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status TEXT DEFAULT 'queued', -- queued → running → done → failed → retry
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input_data TEXT, -- JSON: was der Worker braucht
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output_path TEXT, -- Pfad zum Ergebnis
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error TEXT, -- Fehlermeldung falls failed
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queued_at DATETIME DEFAULT CURRENT_TIMESTAMP,
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started_at DATETIME,
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finished_at DATETIME
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);
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```
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### Verzeichnisstruktur
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```
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/data/ki-video/
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├── production.db # SQLite — zentrale Steuerung
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│
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├── channels/ # Pro Kanal
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│ ├── kanal-a/
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│ │ ├── avatar.png # Referenz-Portraet (FLUX-generiert)
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│ │ ├── voice-sample.wav # XTTS Voice-Cloning Referenz (~30s)
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│ │ ├── style.json # { "avatar": "pip_20", "position": "bottom_right" }
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│ │ └── prompt.md # GPT-5.4 System-Prompt (Persoenlichkeit, Ton, Stil)
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│ └── kanal-b/
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│ ├── avatar.png # Anderes Gesicht
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│ ├── voice-sample.wav # Andere Stimme
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│ ├── style.json # { "avatar": "fullscreen" }
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│ └── prompt.md # Anderer Stil/Persoenlichkeit
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│
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├── videos/ # Pro Video ein Ordner
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│ ├── 2026-03-16-iran-hyperschall/
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│ │ ├── script.md # Fertiges Skript
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│ │ ├── scenes.json # Szenenplan
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│ │ ├── images/
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│ │ │ ├── hero-001.png
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│ │ │ ├── scene-002.png
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│ │ │ ├── scene-003.png
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│ │ │ └── ... # 80-120 Bilder
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│ │ ├── audio/
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│ │ │ ├── voiceover.wav # XTTS v2 Output
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│ │ │ └── music.mp3 # Hintergrundmusik
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│ │ ├── avatar/
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│ │ │ ├── clip-001.mp4 # SadTalker 30s-Clips
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│ │ │ ├── clip-002.mp4
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│ │ │ └── full.mp4 # Zusammengefuegt
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│ │ ├── subtitles.srt # Whisper → SRT
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│ │ ├── thumbnail.png # FLUX hero + Text
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│ │ └── final.mp4 # FERTIGES VIDEO
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│ └── 2026-03-18-oelpreis-analyse/
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│ └── ...
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│
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└── templates/
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├── ffmpeg-pip20.sh # FFmpeg-Preset: 20% Avatar unten rechts
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├── ffmpeg-fullscreen.sh # FFmpeg-Preset: 100% Avatar
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├── ffmpeg-kenburns.sh # Ken-Burns-Effekt Presets
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└── music/ # Lizenzfreie Hintergrundmusik
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├── ambient-01.mp3
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└── ambient-02.mp3
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```
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### Produktions-Ablauf (Statusmaschine)
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```
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┌──────────────────────────────────────────────────┐
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│ │
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┌─────────┐ │ ┌──────────┐ ┌──────────┐ ┌───────────┐ │
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│ draft │──────┼─►│ script │───►│ scenes │───►│ producing │ │
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│ │ │ │ │ │ │ │ │ │
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│ Thema │ │ │ GPT-5.4 │ │ Qwen 14B │ │ 5 GPUs │ │
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│ anlegen │ │ │ + Review │ │ Szenen- │ │ parallel │ │
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└─────────┘ │ └──────────┘ │ plan │ └─────┬─────┘ │
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│ └──────────┘ │ │
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│ ▼ │
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│ ┌───────────┐ ┌──────────┐ ┌──────────┐ │
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│ │ published │◄───│ review │◄──│ assembly │ │
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│ │ │ │ │ │ │ │
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│ │ YouTube │ │ Mensch │ │ FFmpeg │ │
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│ │ Upload │ │ schaut │ │ PiP/Full │ │
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│ └───────────┘ └──────────┘ └──────────┘ │
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│ │
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└──────────────────────────────────────────────────┘
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Status-Uebergaenge:
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draft → script : Mensch gibt Thema ein, startet GPT-5.4 Skript
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script → scenes : Mensch reviewt Skript, bestaetigt, Qwen generiert Szenenplan
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scenes → producing : Orchestrator erstellt Jobs fuer alle 5 GPUs
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producing → assembly : Alle GPU-Jobs fertig (TTS + Avatar + Bilder + Untertitel)
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assembly → review : FFmpeg Assembly fertig, Video liegt als MP4 vor
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review → published : Mensch schaut Video, gibt frei, Upload
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```
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### Orchestrator-Logik (Python, laeuft auf ki-tower)
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```
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Alle 10 Sekunden:
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1. Checke videos WHERE status = 'scenes'
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→ Erstelle GPU-Jobs:
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- 1x TTS-Job (3080 #0)
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- N x SDXL-Jobs (3080 #0 nach TTS, #1)
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- M x FLUX-Jobs (3090, nur hero-Szenen)
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- 1x Whisper-Job (3080 #3, nach TTS)
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- 1x ESRGAN-Job (3080 #3, nach Bilder)
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- 1x SadTalker-Job (3080 #2, nach TTS)
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2. Checke jobs WHERE status = 'queued'
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→ Pruefe ob Abhaengigkeiten erfuellt:
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- SadTalker wartet auf TTS (voiceover.wav muss existieren)
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- Whisper wartet auf TTS
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- ESRGAN wartet auf Bilder
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→ Sende an Worker via HTTP REST
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3. Checke jobs WHERE status = 'running'
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→ Polle Worker-Status
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→ Bei done: speichere output_path, update status
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→ Bei failed: retry (max 3x), dann menschliche Aufmerksamkeit
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4. Checke videos WHERE status = 'producing'
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AND alle zugehoerigen jobs.status = 'done'
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→ Starte Assembly-Job (FFmpeg)
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→ Status → 'assembly'
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5. Checke videos WHERE status = 'assembly'
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AND assembly-job.status = 'done'
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→ Status → 'review'
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→ Benachrichtigung an Telegram (Hausmeister-Bot!)
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```
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### CLI fuer den Alltag
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```bash
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# Neues Video anlegen
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./produce.py new --channel kanal-a --topic "Irans Hyperschall-Schlag"
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# Status aller Videos
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./produce.py status
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# ID | Kanal | Thema | Status | Fortschritt
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# 42 | kanal-a | Irans Hyperschall-Schlag | producing | 3/5 Jobs done
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# 43 | kanal-b | Oelpreis-Analyse 2026 | script | Review pending
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# Skript bestaetigen und Produktion starten
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./produce.py approve-script 42
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# Fertiges Video reviewen und freigeben
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./produce.py publish 42 --yt-upload
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# GPU-Auslastung
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./produce.py gpus
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# GPU | Status | Job | Video | Progress
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# 3090 | busy | flux-hero | #42 | 8/15 images
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# 3080 #0 | busy | sdxl-batch | #42 | 23/50 images
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# 3080 #1 | busy | sdxl-batch | #42 | 31/50 images
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# 3080 #2 | busy | sadtalker | #42 | 12:30/24:00
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# 3080 #3 | idle | — | — | waiting for images
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```
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## Worker-Architektur
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## Worker-Architektur
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```
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```
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ki-tower (3090, Chef) gpu-worker (4x 3080, Worker) gpu-reserve (RX 6600 XT)
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ki-tower (3090, Chef) gpu-worker (4x 3080, Worker) gpu-reserve (RX 6600 XT)
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┌─────────────────────────┐ ┌──────────────────────────────┐ ┌──────────────────┐
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┌─────────────────────────┐ ┌──────────────────────────────┐ ┌──────────────────┐
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│ Orchestrator (Python) │ │ Debian 12 + Docker + CUDA │ │ Reserve/Whisper │
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│ Orchestrator (Python) │ │ Debian 12 + Docker + CUDA │ │ Reserve/Whisper │
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│ ├── Job Queue (SQLite) │ Tailscale │ │
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│ ├── production.db │ Tailscale │ │
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│ ├── /api/submit-job │◄────────────►│ GPU #0: xtts+sdxl :8501 │ │ Whisper :8601 │
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│ ├── /api/submit-job │◄────────────►│ GPU #0: xtts+sdxl :8501 │ │ Whisper :8601 │
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│ ├── /api/job-status │ │ GPU #1: sdxl-worker :8502 │ │ CPU-Batch :8602 │
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│ ├── /api/job-status │ │ GPU #1: sdxl-worker :8502 │ │ CPU-Batch :8602 │
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│ └── /api/get-result │ Tailscale │ GPU #2: sadtalker :8503 │ │ (nur bei Bedarf)│
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│ └── /api/get-result │ Tailscale │ GPU #2: sadtalker :8503 │ │ (nur bei Bedarf)│
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@ -379,12 +594,6 @@ ki-tower (3090, Chef) gpu-worker (4x 3080, Worker) gpu-
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└─────────────────────────┘ └──────────────────────────────┘
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└─────────────────────────┘ └──────────────────────────────┘
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```
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```
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Prinzipien:
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- 1 Container = 1 GPU = 1 Aufgabe. Feste Zuordnung, kein dynamisches Scheduling.
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- SQLite als Job-Queue. Ein User, nicht tausend. Eine Datei reicht.
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- HTTP-APIs pro Worker. Orchestrator ruft per REST auf, pollt Status.
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- Kein Service-Mesh, kein Kubernetes. Tailscale verbindet die zwei Maschinen.
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## VRAM-Budget (ki-tower, sequentiell)
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## VRAM-Budget (ki-tower, sequentiell)
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```
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```
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