# IGNY8 Phase 2: Video Creator (02I)
## AI Video Creation Pipeline — Stage 9

**Document Version:** 1.0
**Date:** 2026-03-23
**Phase:** IGNY8 Phase 2 — Feature Expansion
**Status:** Build Ready
**Source of Truth:** Codebase at `/data/app/igny8/`
**Audience:** Claude Code, Backend Developers, Architects

---

## 1. CURRENT STATE

### Video Today
There is **no** video creation capability in IGNY8. No TTS, no FFmpeg pipeline, no video publishing. Images exist (generated by pipeline Stages 5-6) and can feed into video as visual assets.

### What Exists
- `Images` model (writer app) — generated images from pipeline, usable as video visual assets
- `SocialAccount` model (02H) — provides OAuth connections to YouTube, Instagram, TikTok for video publishing
- Self-hosted AI infrastructure (Phase 0F) — provides GPU for TTS and AI image generation
- Content generation pipeline (01E) — content records provide source material for video scripts
- Celery infrastructure with multiple queues — supports dedicated `video` queue for long-running renders

### What Does Not Exist
- No video app or models
- No script generation from articles
- No TTS (text-to-speech) voiceover generation
- No FFmpeg/MoviePy video composition pipeline
- No subtitle generation
- No video publishing to platforms
- No Stage 9 pipeline integration

---

## 2. WHAT TO BUILD

### Overview
Build Stage 9 of the automation pipeline: an AI video creation system that converts published content into videos. The pipeline has 5 stages: script generation → voiceover → visual assets → composition → publishing. Videos publish to YouTube, Instagram Reels, and TikTok.

### 2.1 Video Types

| Type | Duration | Aspect Ratio | Primary Platform |
|------|----------|-------------|-----------------|
| **Short** | 30-90s | 9:16 vertical | YouTube Shorts, Instagram Reels, TikTok |
| **Medium** | 60-180s | 9:16 or 16:9 | TikTok, YouTube |
| **Long** | 5-15m | 16:9 horizontal | YouTube |

### 2.2 Platform Specs

| Platform | Max Duration | Resolution | Encoding | Max File Size |
|----------|-------------|------------|----------|--------------|
| YouTube Long | Up to 12h | 1920×1080 | MP4 H.264, AAC audio | 256GB |
| YouTube Shorts | ≤60s | 1080×1920 | MP4 H.264, AAC audio | 256GB |
| Instagram Reels | ≤90s | 1080×1920 | MP4 H.264, AAC audio | 650MB |
| TikTok | ≤10m | 1080×1920 | MP4 H.264, AAC audio | 72MB |

### 2.3 Five-Stage Video Pipeline

#### Stage 1 — Script Generation (AI)
**Input:** Content record (title, content_html, meta_description, keywords, images)

AI extracts key points and produces:
```json
{
    "hook": "text (3-5 sec)",
    "intro": "text (10-15 sec)",
    "points": [
        {"text": "...", "duration_est": 20, "visual_cue": "show chart", "text_overlay": "Key stat"}
    ],
    "cta": "text (5-10 sec)",
    "chapter_markers": [{"time": 0, "title": "Intro"}],
    "total_estimated_duration": 120
}
```

SEO: AI generates platform-specific title, description, tags for each target platform.

#### Stage 2 — Voiceover (TTS)

**Cloud Providers:**
| Provider | Cost | Quality | Features |
|----------|------|---------|----------|
| OpenAI TTS | $15-30/1M chars | High | Voices: alloy, echo, fable, onyx, nova, shimmer |
| ElevenLabs | Plan-based | Highest | Voice cloning, ultra-realistic |

**Self-Hosted (via 0F GPU):**
| Model | Quality | Speed | Notes |
|-------|---------|-------|-------|
| Coqui XTTS-v2 | Good | Medium | Multi-language, free |
| Bark | Expressive | Slow | Emotional speech |
| Piper TTS | Moderate | Fast | Lightweight |

**Features:** Voice selection, speed control, multi-language support
**Output:** WAV/MP3 audio file + word-level timestamps (for subtitle sync)

#### Stage 3 — Visual Assets
**Sources:**
- Article images from `Images` model (already generated by pipeline Stages 5-6)
- AI-generated scenes (Runware/DALL-E/Stable Diffusion via 0F)
- Stock footage APIs: Pexels, Pixabay (free, API key required)
- Text overlay frames (rendered via Pillow)
- Code snippet frames (via Pygments syntax highlighting)

**Effects:**
- Ken Burns effect on still images (zoom/pan animation)
- Transition effects between scenes (fade, slide, dissolve)

#### Stage 4 — Video Composition (FFmpeg + MoviePy)

**Libraries:** FFmpeg (encoding), MoviePy (high-level composition), Pillow (text overlays), pydub (audio processing)

**Process:**
1. Create visual timeline from script sections
2. Assign visuals to each section (image/video clip per point)
3. Add text overlays at specified timestamps
4. Mix voiceover audio with background music (royalty-free, 20% volume)
5. Apply transitions between sections
6. Render to target resolution/format

**Render Presets:**
| Preset | Resolution | Duration Range | Encoding |
|--------|-----------|----------------|----------|
| `youtube_long` | 1920×1080 | 3-15m | H.264/AAC |
| `youtube_short` | 1080×1920 | 30-60s | H.264/AAC |
| `instagram_reel` | 1080×1920 | 30-90s | H.264/AAC |
| `tiktok` | 1080×1920 | 30-180s | H.264/AAC |

#### Stage 5 — SEO & Publishing
- Auto-generate SRT subtitle file from TTS word-level timestamps
- AI thumbnail: hero image with title text overlay
- Platform-specific metadata: title (optimized per platform), description (with timestamps for YouTube), tags, category
- Publishing via platform APIs (reuses OAuth from 02H SocialAccount)
- Confirmation logging with platform video ID

### 2.4 User Flow

1. Select content → choose video type (short/medium/long) → target platforms
2. AI generates script → user reviews/edits script
3. Select voice → preview audio → approve
4. Auto-assign visuals → user can swap images → preview composition
5. Render video → preview final → approve
6. Publish to selected platforms → track performance

### 2.5 Dedicated Celery Queue

Video rendering is CPU/GPU intensive and requires isolation:

- **Dedicated `video` queue:** `celery -A igny8_core worker -Q video --concurrency=1`
- **Long-running tasks:** 5-30 minutes per video render
- **Progress tracking:** via Celery result backend (task status updates)
- **Temp file cleanup:** after publish, clean up intermediate files (audio, frames, raw renders)

---

## 3. DATA MODELS & APIS

### 3.1 New Models

All models in a new `video` app.

#### VideoProject (video app)

```python
class VideoProject(SiteSectorBaseModel):
    """
    Top-level container for a video creation project.
    Links to source content and tracks overall progress.
    """
    content = models.ForeignKey(
        'writer.Content',
        on_delete=models.SET_NULL,
        null=True,
        blank=True,
        related_name='video_projects',
        help_text='Source content (null for standalone video)'
    )
    project_type = models.CharField(
        max_length=10,
        choices=[
            ('short', 'Short (30-90s)'),
            ('medium', 'Medium (60-180s)'),
            ('long', 'Long (5-15m)'),
        ]
    )
    target_platforms = models.JSONField(
        default=list,
        help_text='List of target platform strings: youtube_long, youtube_short, instagram_reel, tiktok'
    )
    status = models.CharField(
        max_length=15,
        choices=[
            ('draft', 'Draft'),
            ('scripting', 'Script Generation'),
            ('voiceover', 'Voiceover Generation'),
            ('composing', 'Visual Composition'),
            ('rendering', 'Rendering'),
            ('review', 'Ready for Review'),
            ('published', 'Published'),
            ('failed', 'Failed'),
        ],
        default='draft'
    )
    settings = models.JSONField(
        default=dict,
        help_text='{voice_id, voice_provider, music_track, transition_style}'
    )

    class Meta:
        app_label = 'video'
        db_table = 'igny8_video_projects'
```

**PK:** BigAutoField (integer) — inherits from SiteSectorBaseModel

#### VideoScript (video app)

```python
class VideoScript(models.Model):
    """
    Script for a video project — generated by AI, editable by user.
    """
    project = models.OneToOneField(
        'video.VideoProject',
        on_delete=models.CASCADE,
        related_name='script'
    )
    script_text = models.TextField(help_text='Full narration text')
    sections = models.JSONField(
        default=list,
        help_text='[{text, duration_est, visual_cue, text_overlay}]'
    )
    hook = models.TextField(blank=True, default='')
    cta = models.TextField(blank=True, default='')
    chapter_markers = models.JSONField(
        default=list,
        help_text='[{time, title}]'
    )
    total_estimated_duration = models.IntegerField(
        default=0,
        help_text='Total estimated duration in seconds'
    )
    seo_metadata = models.JSONField(
        default=dict,
        help_text='{platform: {title, description, tags}}'
    )
    version = models.IntegerField(default=1)

    class Meta:
        app_label = 'video'
        db_table = 'igny8_video_scripts'
```

**PK:** BigAutoField (integer) — standard Django Model

#### VideoAsset (video app)

```python
class VideoAsset(models.Model):
    """
    Individual asset (image, footage, music, overlay, subtitle) for a video project.
    """
    project = models.ForeignKey(
        'video.VideoProject',
        on_delete=models.CASCADE,
        related_name='assets'
    )
    asset_type = models.CharField(
        max_length=15,
        choices=[
            ('image', 'Image'),
            ('footage', 'Footage'),
            ('music', 'Background Music'),
            ('overlay', 'Text Overlay'),
            ('subtitle', 'Subtitle File'),
        ]
    )
    source = models.CharField(
        max_length=20,
        choices=[
            ('article_image', 'Article Image'),
            ('ai_generated', 'AI Generated'),
            ('stock_pexels', 'Pexels Stock'),
            ('stock_pixabay', 'Pixabay Stock'),
            ('uploaded', 'User Uploaded'),
            ('rendered', 'Rendered'),
        ]
    )
    file_path = models.CharField(max_length=500, help_text='Path in media storage')
    file_url = models.URLField(blank=True, default='')
    duration = models.FloatField(
        null=True, blank=True,
        help_text='Duration in seconds (for video/audio assets)'
    )
    section_index = models.IntegerField(
        null=True, blank=True,
        help_text='Which script section this asset belongs to'
    )
    order = models.IntegerField(default=0)

    class Meta:
        app_label = 'video'
        db_table = 'igny8_video_assets'
```

**PK:** BigAutoField (integer) — standard Django Model

#### RenderedVideo (video app)

```python
class RenderedVideo(models.Model):
    """
    A rendered video file for a specific platform preset.
    One project can have multiple renders (one per target platform).
    """
    project = models.ForeignKey(
        'video.VideoProject',
        on_delete=models.CASCADE,
        related_name='rendered_videos'
    )
    preset = models.CharField(
        max_length=20,
        choices=[
            ('youtube_long', 'YouTube Long'),
            ('youtube_short', 'YouTube Short'),
            ('instagram_reel', 'Instagram Reel'),
            ('tiktok', 'TikTok'),
        ]
    )
    resolution = models.CharField(
        max_length=15,
        help_text='e.g. 1920x1080 or 1080x1920'
    )
    duration = models.FloatField(help_text='Duration in seconds')
    file_size = models.BigIntegerField(help_text='File size in bytes')
    file_path = models.CharField(max_length=500)
    file_url = models.URLField(blank=True, default='')
    subtitle_file_path = models.CharField(max_length=500, blank=True, default='')
    thumbnail_path = models.CharField(max_length=500, blank=True, default='')
    render_started_at = models.DateTimeField()
    render_completed_at = models.DateTimeField(null=True, blank=True)
    status = models.CharField(
        max_length=15,
        choices=[
            ('queued', 'Queued'),
            ('rendering', 'Rendering'),
            ('completed', 'Completed'),
            ('failed', 'Failed'),
        ],
        default='queued'
    )

    class Meta:
        app_label = 'video'
        db_table = 'igny8_rendered_videos'
```

**PK:** BigAutoField (integer) — standard Django Model

#### PublishedVideo (video app)

```python
class PublishedVideo(models.Model):
    """
    Tracks a rendered video published to a social platform.
    Uses SocialAccount from 02H for OAuth credentials.
    """
    rendered_video = models.ForeignKey(
        'video.RenderedVideo',
        on_delete=models.CASCADE,
        related_name='publications'
    )
    social_account = models.ForeignKey(
        'social.SocialAccount',
        on_delete=models.CASCADE,
        related_name='published_videos'
    )
    platform = models.CharField(max_length=15)
    platform_video_id = models.CharField(max_length=255, blank=True, default='')
    published_url = models.URLField(blank=True, default='')
    title = models.CharField(max_length=255)
    description = models.TextField()
    tags = models.JSONField(default=list)
    thumbnail_url = models.URLField(blank=True, default='')
    published_at = models.DateTimeField(null=True, blank=True)
    status = models.CharField(
        max_length=15,
        choices=[
            ('publishing', 'Publishing'),
            ('published', 'Published'),
            ('failed', 'Failed'),
            ('removed', 'Removed'),
        ],
        default='publishing'
    )

    class Meta:
        app_label = 'video'
        db_table = 'igny8_published_videos'
```

**PK:** BigAutoField (integer) — standard Django Model

#### VideoEngagement (video app)

```python
class VideoEngagement(models.Model):
    """
    Engagement metrics for a published video.
    Fetched periodically from platform APIs.
    """
    published_video = models.ForeignKey(
        'video.PublishedVideo',
        on_delete=models.CASCADE,
        related_name='engagement_records'
    )
    views = models.IntegerField(default=0)
    likes = models.IntegerField(default=0)
    comments = models.IntegerField(default=0)
    shares = models.IntegerField(default=0)
    watch_time_seconds = models.IntegerField(default=0)
    avg_view_duration = models.FloatField(default=0.0)
    raw_data = models.JSONField(default=dict, help_text='Full platform API response')
    fetched_at = models.DateTimeField(auto_now_add=True)

    class Meta:
        app_label = 'video'
        db_table = 'igny8_video_engagement'
```

**PK:** BigAutoField (integer) — standard Django Model

### 3.2 New App Registration

Create video app:
- **App config:** `igny8_core/modules/video/apps.py` with `app_label = 'video'`
- **Add to INSTALLED_APPS** in `igny8_core/settings.py`

### 3.3 Migration

```
igny8_core/migrations/XXXX_add_video_models.py
```

**Operations:**
1. `CreateModel('VideoProject', ...)` — with indexes on content, status
2. `CreateModel('VideoScript', ...)` — OneToOne to VideoProject
3. `CreateModel('VideoAsset', ...)` — with index on project
4. `CreateModel('RenderedVideo', ...)` — with index on project, status
5. `CreateModel('PublishedVideo', ...)` — with indexes on rendered_video, social_account
6. `CreateModel('VideoEngagement', ...)` — with index on published_video

### 3.4 API Endpoints

All endpoints under `/api/v1/video/`:

#### Project Management
| Method | Path | Description |
|--------|------|-------------|
| POST | `/api/v1/video/projects/` | Create video project. Body: `{content_id, project_type, target_platforms}`. |
| GET | `/api/v1/video/projects/?site_id=X` | List projects with filters (status, project_type). |
| GET | `/api/v1/video/projects/{id}/` | Project detail with script, assets, renders. |

#### Script
| Method | Path | Description |
|--------|------|-------------|
| POST | `/api/v1/video/scripts/generate/` | AI-generate script from content. Body: `{project_id}`. |
| PUT | `/api/v1/video/scripts/{project_id}/` | Edit script (user modifications). |

#### Voiceover
| Method | Path | Description |
|--------|------|-------------|
| POST | `/api/v1/video/voiceover/generate/` | Generate TTS audio. Body: `{project_id, voice_id, provider}`. |
| POST | `/api/v1/video/voiceover/preview/` | Preview voice sample (short clip). Body: `{text, voice_id, provider}`. |

#### Assets
| Method | Path | Description |
|--------|------|-------------|
| GET | `/api/v1/video/assets/{project_id}/` | List project assets. |
| POST | `/api/v1/video/assets/{project_id}/` | Add/replace asset. |

#### Rendering & Publishing
| Method | Path | Description |
|--------|------|-------------|
| POST | `/api/v1/video/render/` | Queue video render. Body: `{project_id, presets: []}`. |
| GET | `/api/v1/video/render/{id}/status/` | Render progress (queued/rendering/completed/failed). |
| GET | `/api/v1/video/rendered/{project_id}/` | List rendered videos for project. |
| POST | `/api/v1/video/publish/` | Publish to platform. Body: `{rendered_video_id, social_account_id}`. |

#### Analytics
| Method | Path | Description |
|--------|------|-------------|
| GET | `/api/v1/video/analytics/?site_id=X` | Aggregate video analytics across projects. |
| GET | `/api/v1/video/analytics/{published_video_id}/` | Single video analytics with engagement timeline. |

**Permissions:** All endpoints use `SiteSectorModelViewSet` permission patterns.

### 3.5 AI Functions

#### GenerateVideoScriptFunction

**Registry key:** `generate_video_script`
**Location:** `igny8_core/ai/functions/generate_video_script.py`

```python
class GenerateVideoScriptFunction(BaseAIFunction):
    """
    Generates video script from content record.
    Produces hook, intro, body points, CTA, chapter markers,
    and platform-specific SEO metadata.
    """
    function_name = 'generate_video_script'

    def validate(self, project_id, **kwargs):
        # Verify project exists, has linked content with content_html
        pass

    def prepare(self, project_id, **kwargs):
        # Load VideoProject + Content
        # Extract key points, images, meta_description
        # Determine target duration from project_type
        pass

    def build_prompt(self):
        # Include: content title, key points, meta_description
        # Target duration constraints
        # Per target_platform: SEO metadata requirements
        pass

    def parse_response(self, response):
        # Parse script structure: hook, intro, points[], cta, chapter_markers
        # Parse seo_metadata per platform
        pass

    def save_output(self, parsed):
        # Create/update VideoScript record
        # Update VideoProject.status = 'scripting' → 'voiceover'
        pass
```

### 3.6 TTS Service

**Location:** `igny8_core/business/tts_service.py`

```python
class TTSService:
    """
    Text-to-speech service. Supports cloud providers and self-hosted models.
    Returns audio file + word-level timestamps.
    """

    PROVIDERS = {
        'openai': OpenAITTSProvider,
        'elevenlabs': ElevenLabsTTSProvider,
        'coqui': CoquiTTSProvider,      # Self-hosted via 0F
        'bark': BarkTTSProvider,          # Self-hosted via 0F
        'piper': PiperTTSProvider,        # Self-hosted via 0F
    }

    def generate(self, text, voice_id, provider='openai'):
        """
        Generate voiceover audio.
        Returns {audio_path, duration, timestamps: [{word, start, end}]}
        """
        pass

    def preview(self, text, voice_id, provider='openai', max_chars=200):
        """Generate short preview clip."""
        pass

    def list_voices(self, provider='openai'):
        """List available voices for provider."""
        pass
```

### 3.7 Video Composition Service

**Location:** `igny8_core/business/video_composition.py`

```python
class VideoCompositionService:
    """
    Composes video from script + audio + visual assets using FFmpeg/MoviePy.
    """

    PRESETS = {
        'youtube_long': {'width': 1920, 'height': 1080, 'min_dur': 180, 'max_dur': 900},
        'youtube_short': {'width': 1080, 'height': 1920, 'min_dur': 30, 'max_dur': 60},
        'instagram_reel': {'width': 1080, 'height': 1920, 'min_dur': 30, 'max_dur': 90},
        'tiktok': {'width': 1080, 'height': 1920, 'min_dur': 30, 'max_dur': 180},
    }

    def compose(self, project_id, preset):
        """
        Full composition:
        1. Load script + audio + visual assets
        2. Create visual timeline from script sections
        3. Assign visuals to sections
        4. Add text overlays at timestamps
        5. Mix voiceover + background music (20% volume)
        6. Apply transitions
        7. Render to preset resolution/format
        8. Generate SRT subtitles from TTS timestamps
        9. Generate thumbnail
        10. Create RenderedVideo record
        Returns RenderedVideo instance.
        """
        pass

    def _apply_ken_burns(self, image_clip, duration):
        """Apply zoom/pan animation to still image."""
        pass

    def _generate_subtitles(self, timestamps, output_path):
        """Generate SRT file from word-level timestamps."""
        pass

    def _generate_thumbnail(self, project, output_path):
        """Create thumbnail: hero image + title text overlay."""
        pass
```

### 3.8 Video Publisher Service

**Location:** `igny8_core/business/video_publisher.py`

```python
class VideoPublisherService:
    """
    Publishes rendered videos to platforms via their APIs.
    Reuses SocialAccount OAuth from 02H.
    """

    def publish(self, rendered_video_id, social_account_id):
        """
        Upload and publish video to platform.
        1. Load RenderedVideo + SocialAccount
        2. Decrypt OAuth tokens
        3. Upload video file via platform API
        4. Set metadata (title, description, tags, thumbnail)
        5. Create PublishedVideo record
        """
        pass
```

### 3.9 Celery Tasks

**Location:** `igny8_core/tasks/video_tasks.py`

All video tasks run on the dedicated `video` queue:

```python
@shared_task(name='generate_video_script', queue='video')
def generate_video_script_task(project_id):
    """AI script generation from content."""
    pass

@shared_task(name='generate_voiceover', queue='video')
def generate_voiceover_task(project_id):
    """TTS audio generation."""
    pass

@shared_task(name='render_video', queue='video')
def render_video_task(project_id, preset):
    """
    FFmpeg/MoviePy video composition. Long-running (5-30 min).
    Updates RenderedVideo.status through lifecycle.
    """
    pass

@shared_task(name='generate_thumbnail', queue='video')
def generate_thumbnail_task(project_id):
    """AI thumbnail creation with title overlay."""
    pass

@shared_task(name='generate_subtitles', queue='video')
def generate_subtitles_task(project_id):
    """SRT generation from TTS timestamps."""
    pass

@shared_task(name='publish_video', queue='video')
def publish_video_task(rendered_video_id, social_account_id):
    """Upload video to platform API."""
    pass

@shared_task(name='fetch_video_engagement')
def fetch_video_engagement_task():
    """Periodic metric fetch for published videos. Runs on default queue."""
    pass

@shared_task(name='video_pipeline_stage9', queue='video')
def video_pipeline_stage9(content_id):
    """
    Full pipeline: script → voice → render → publish.
    Triggered after Stage 8 (social) or directly after Stage 7 (publish).
    """
    pass
```

**Beat Schedule Additions:**

| Task | Schedule | Notes |
|------|----------|-------|
| `fetch_video_engagement` | Every 12 hours | Fetches engagement metrics for published videos |

**Docker Configuration:**
```yaml
# Add to docker-compose.app.yml:
celery_video_worker:
  build: ./backend
  command: celery -A igny8_core worker -Q video --concurrency=1 --loglevel=info
  # Requires FFmpeg installed in Docker image
```

**Dockerfile Addition:**
```dockerfile
# Add to backend/Dockerfile:
RUN apt-get update && apt-get install -y ffmpeg
```

---

## 4. IMPLEMENTATION STEPS

### Step 1: Create Video App
1. Create `igny8_core/modules/video/` directory with `__init__.py` and `apps.py`
2. Add `video` to `INSTALLED_APPS` in settings.py
3. Create 6 models: VideoProject, VideoScript, VideoAsset, RenderedVideo, PublishedVideo, VideoEngagement

### Step 2: Migration
1. Create migration for 6 new models
2. Run migration

### Step 3: System Dependencies
1. Add FFmpeg to Docker image (`apt-get install -y ffmpeg`)
2. Add to `requirements.txt`: `moviepy`, `pydub`, `Pillow` (already present), `pysrt`
3. Add docker-compose service for `celery_video_worker` with `-Q video --concurrency=1`

### Step 4: AI Function
1. Implement `GenerateVideoScriptFunction` in `igny8_core/ai/functions/generate_video_script.py`
2. Register `generate_video_script` in `igny8_core/ai/registry.py`

### Step 5: Services
1. Implement `TTSService` in `igny8_core/business/tts_service.py` (cloud + self-hosted providers)
2. Implement `VideoCompositionService` in `igny8_core/business/video_composition.py`
3. Implement `VideoPublisherService` in `igny8_core/business/video_publisher.py`

### Step 6: Pipeline Integration
Add Stage 9 trigger:

```python
# After Stage 8 (social posts) or Stage 7 (publish):
def post_social_or_publish(content_id):
    site = content.site
    config = AutomationConfig.objects.get(site=site)
    if config.settings.get('video_enabled'):
        video_pipeline_stage9.delay(content_id)
```

### Step 7: API Endpoints
1. Create `igny8_core/urls/video.py` with project, script, voiceover, asset, render, publish, analytics endpoints
2. Create views extending `SiteSectorModelViewSet`
3. Register URL patterns under `/api/v1/video/`

### Step 8: Celery Tasks
1. Implement 8 tasks in `igny8_core/tasks/video_tasks.py`
2. Add `fetch_video_engagement` to beat schedule
3. Ensure render tasks target `video` queue

### Step 9: Serializers & Admin
1. Create DRF serializers for all 6 models
2. Register models in Django admin

### Step 10: Credit Cost Configuration
Add to `CreditCostConfig` (billing app):

| operation_type | default_cost | description |
|---------------|-------------|-------------|
| `video_script_generation` | 5 | AI script generation from content |
| `video_tts_standard` | 10/min | Cloud TTS (OpenAI) — per minute of audio |
| `video_tts_selfhosted` | 2/min | Self-hosted TTS (Coqui/Piper via 0F) |
| `video_tts_hd` | 20/min | HD TTS (ElevenLabs) — per minute |
| `video_visual_generation` | 15-50 | AI visual asset generation (varies by count) |
| `video_thumbnail` | 3-10 | AI thumbnail creation |
| `video_composition` | 5 | FFmpeg render |
| `video_seo_metadata` | 1 | SEO metadata per platform |
| `video_short_total` | 40-80 | Total for short-form video |
| `video_long_total` | 100-250 | Total for long-form video |

---

## 5. ACCEPTANCE CRITERIA

### Script Generation
- [ ] AI generates structured script from content with hook, intro, body points, CTA
- [ ] Script includes chapter markers with timestamps
- [ ] Platform-specific SEO metadata generated (title, description, tags)
- [ ] Script duration estimates match project_type constraints
- [ ] User can edit script before proceeding

### Voiceover
- [ ] OpenAI TTS generates audio with voice selection
- [ ] ElevenLabs TTS works as premium option
- [ ] Self-hosted TTS (Coqui XTTS-v2) works via 0F GPU
- [ ] Word-level timestamps generated for subtitle sync
- [ ] Voice preview endpoint allows testing before full generation

### Visual Assets
- [ ] Article images from Images model used as visual assets
- [ ] Ken Burns effect applied to still images
- [ ] Text overlay frames rendered via Pillow
- [ ] Transitions applied between scenes
- [ ] User can swap assets before rendering

### Rendering
- [ ] FFmpeg/MoviePy composition produces correct resolution per preset
- [ ] Audio mix: voiceover at 100% + background music at 20%
- [ ] SRT subtitle file generated from TTS timestamps
- [ ] AI thumbnail generated with title text overlay
- [ ] Render runs on dedicated `video` Celery queue with concurrency=1
- [ ] Render progress trackable via status endpoint

### Publishing
- [ ] Video uploads to YouTube via API (reusing 02H SocialAccount)
- [ ] Video uploads to Instagram Reels
- [ ] Video uploads to TikTok
- [ ] Platform video ID and URL stored on PublishedVideo
- [ ] Engagement metrics fetched every 12 hours

### Pipeline Integration
- [ ] Stage 9 triggers automatically when video_enabled in AutomationConfig
- [ ] Full pipeline (script → voice → render → publish) runs as single Celery chain
- [ ] VideoObject schema (02G) generated for published video content

---

## 6. CLAUDE CODE INSTRUCTIONS

### File Locations
```
igny8_core/
├── modules/
│   └── video/
│       ├── __init__.py
│       ├── apps.py                    # app_label = 'video'
│       └── models.py                  # 6 models
├── ai/
│   └── functions/
│       └── generate_video_script.py   # GenerateVideoScriptFunction
├── business/
│   ├── tts_service.py                 # TTSService (cloud + self-hosted)
│   ├── video_composition.py           # VideoCompositionService
│   └── video_publisher.py            # VideoPublisherService
├── tasks/
│   └── video_tasks.py                 # Celery tasks (video queue)
├── urls/
│   └── video.py                       # Video endpoints
└── migrations/
    └── XXXX_add_video_models.py
```

### Conventions
- **PKs:** BigAutoField (integer) — do NOT use UUIDs
- **Table prefix:** `igny8_` on all new tables
- **App label:** `video` (new app)
- **Celery app name:** `igny8_core`
- **Celery queue:** `video` for all render/composition tasks (default queue for engagement fetch)
- **URL pattern:** `/api/v1/video/...`
- **Permissions:** Use `SiteSectorModelViewSet` permission pattern
- **Docker:** FFmpeg must be installed in Docker image; dedicated `celery_video_worker` service
- **AI functions:** Extend `BaseAIFunction`; register as `generate_video_script`
- **Frontend:** `.tsx` files with Zustand stores

### Cross-References
| Doc | Relationship |
|-----|-------------|
| **02H** | Socializer provides SocialAccount model + OAuth for YouTube/Instagram/TikTok publishing |
| **0F** | Self-hosted AI infrastructure provides GPU for TTS + image generation |
| **01E** | Pipeline Stage 9 integration — hooks after Stage 8 (social) or Stage 7 (publish) |
| **02G** | VideoObject schema generated for content with published video |
| **04A** | Managed services may include video creation as premium tier |

### Key Decisions
1. **New `video` app** — Separate app because video has 6 models and complex pipeline logic distinct from social posting
2. **Dedicated Celery queue** — Video rendering is CPU/GPU intensive (5-30 min); isolated `video` queue with concurrency=1 prevents blocking other tasks
3. **VideoScript, VideoAsset as plain models.Model** — Not SiteSectorBaseModel because they're children of VideoProject which carries the site/sector context
4. **Multiple RenderedVideo per project** — One project can target multiple platforms; each gets its own render at the correct resolution
5. **Reuse 02H OAuth** — PublishedVideo references SocialAccount from 02H; no duplicate OAuth infrastructure for video platforms
6. **Temp file cleanup** — Intermediate files (raw audio, image frames, non-final renders) cleaned up after successful publish to manage disk space

### System Requirements
- FFmpeg installed on server (add to Docker image via `apt-get install -y ffmpeg`)
- Python packages: `moviepy`, `pydub`, `Pillow`, `pysrt`
- Sufficient disk space for video temp files (cleanup after publish)
- Self-hosted GPU (from 0F) for TTS + AI image generation (optional — cloud fallback available)
- Dedicated Celery worker for `video` queue: `celery -A igny8_core worker -Q video --concurrency=1`