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Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]

Added

  • Audience Segmentation System: Comprehensive audience segmentation framework (#49)
    • Segment model with three categories: DEMOGRAPHIC, BEHAVIORAL, PSYCHOGRAPHIC
    • 3,457 predefined segments based on industry frameworks (VALS, Rogers' Innovation Adoption Curve, AIO variables)
    • Persona model for combining geographic and non-geographic segments
    • JSON schemas for segments, personas, and persona-segment mappings
    • Import/export management commands (import_segments, export_segments, import_personas, export_personas)
    • Complete user documentation (docs/user/segmentation.rst) with theoretical frameworks and best practices
    • Developer schema reference (docs/developer/data-schemas.rst) with validation rules and examples
  • CONTRIBUTING.md with contribution guidelines and code style (#13)
  • CHANGELOG.md for version tracking (#14)
  • Multi-Model Diffusion Support: 7 models with varying architectures
    • Z-Image Turbo (zimage)
    • Flux.1-dev (flux1)
    • Flux 2 Klein (flux2)
    • Qwen-Image-2512 (qwen)
    • SDXL Turbo (sdxl)
    • DreamShaper XL Lightning (sdxl)
    • Juggernaut XL v9 (sdxl)
    • Realistic Vision v5.1 (sd15)
  • Model Architecture Refactoring: Template Method Pattern for DRY code
    • BaseModel abstract base with concrete template methods
    • CompelPromptMixin for long prompt handling (>77 tokens) and prompt weighting
    • CLIPTokenLimitMixin for legacy 77-token truncation
    • DebugLoggingMixin for debug output
    • Minimal concrete implementations (20-70 lines per model)
    • Configuration-driven behavior via data/presets.json
  • Compel Prompt Features: Advanced prompt handling for CLIP-based models (SDXL, SD15)
    • Automatic prompt chunking for >77 tokens
    • Prompt weighting syntax: (word:weight)
    • LoRA trigger word integration without truncation
  • LoRA Management: Dynamic LoRA loading with theme-based filtering
    • Auto-download from CivitAI by AIR URN
    • Theme categorization (anime, photorealistic, fantasy)
    • Base architecture compatibility checking
  • Prompt Enhancement: Three enhancement strategies
    • Rule-based (PromptEnhancer)
    • Local LLM (HFPromptEnhancer using Qwen2.5-3B)
    • Anthropic API (LLMPromptEnhancer)
  • Jinja2 Prompt Templates: Template-based LLM prompts for adaptation pipeline
    • Structured prompt composition
    • Variable interpolation
    • Reusable prompt components
  • Django Admin Interface: Django Unfold-based UI
    • Prompt and job management
    • Image previews and downloads
    • Model and LoRA configuration
    • Storyboard inline frame previews
  • Celery Task Processing: Async image generation and prompt enhancement
    • Model warming and caching
    • Sequential task execution
    • Task result persistence
  • Sphinx Documentation: Comprehensive project documentation
    • Architecture overview
    • API reference
    • Development guides
    • HTML documentation build with make html
  • Src Layout: Proper Python packaging structure
    • src/cw/ package directory
    • Clean separation of source and project root
    • Improved import resolution
  • Configuration Management: JSON-based configuration system
    • data/presets.json for models and LoRAs
    • import_presets/export_presets commands
    • Environment variable support via .env
  • Docker Compose Setup: Containerized dependencies
    • PostgreSQL 17 (port 5435)
    • Valkey/Redis (port 6379)
    • Grafana (port 3000)
    • Loki (port 3100)
  • Development Tooling:
    • uv for fast dependency management
    • honcho for process orchestration
    • Procfile for service definitions
    • ./start.sh for ordered startup
  • TV Spot Adaptation System: Multi-agent pipeline for culturally adapting TV commercials
    • Campaign→VideoAdUnit domain model architecture
    • Region→Country→Language reference data hierarchy
    • LangGraph-based adaptation pipeline with concept extraction, cultural research, script writing, and evaluation agents
    • Format/language compliance evaluation node for script validation
    • Storyboard generation from adapted scripts
  • Tailwind CSS 4 Build Pipeline: Structured insights editor widget with modern CSS tooling
  • Flower Task Monitor: Real-time Celery task monitoring on port 5555
  • Grafana + Loki Integration: Log aggregation and search via Grafana UI (port 3000)
    • Alloy-based log collection from logs/*.log
    • Automatic Loki datasource provisioning
  • Reference Data Management: Separate JSON files for regions, countries, languages, LLM models
    • export_reference_data command with --dir and --dry-run support
    • import_reference_data command with dependency-aware import ordering
  • Homepage Links: Navigation shortcuts in admin interface
  • Django Unfold Admin: Modern admin interface with custom templates

Changed

  • Celery Queue Architecture: Consolidated to single default queue with solo pool
    • Sequential task execution prevents concurrent model loading
    • Natural task batching for storyboard generation
    • Improved GPU memory efficiency
  • TV Spot Model Refactoring: Migrated from separate Origin/Adaptation models to unified VideoAdUnit with polymorphic AdUnit base (#46)
    • Adaptation chain via source_ad_unit FK
    • Pipeline integration with status tracking and JSON brief storage
    • Multi-table inheritance for extensibility
  • Logging Architecture: Structured JSON logging with per-worker log files
    • logs/tasks.log - All task execution
    • logs/worker_default.log - Image generation worker
    • logs/django.log - Django server
    • logs/celery.log - Celery general logs
  • Documentation Updates: CLAUDE.md updated to match refactored architecture
  • Device Optimization: Platform-specific optimizations
    • Apple Silicon: MPS backend with sequential CPU offload
    • CUDA: Configurable CPU offload strategies
    • Automatic attention slicing
    • Model caching for warm restarts
  • Precision: All models use torch.bfloat16 for efficiency

Fixed

  • CUDA OOM errors on storyboard generation via sequential model loading
  • CSS path references in VideoAdUnit admin
  • Pipeline nodes compatibility with VideoAdUnit model
  • Language models API endpoint and template variables
  • Missing format_evaluation status in admin UI

Removed

  • Separate enhancement worker queue (consolidated into default queue)
  • AdaptationMarket model (replaced by Region→Country→Language)
  • Origin/Adaptation separate models (unified into VideoAdUnit)

Migration Notes

Model Architecture Refactoring

Before: Each model had duplicate code for loading, generation, LoRA management (~200-300 lines per model).

After: Models inherit from BaseModel and mixins, only overriding specific hooks (~20-70 lines per model).

Migration: No user-facing changes. Model behavior remains the same, but new models are much easier to add.

Src Layout Adoption

Before (0.0.x): Code lived directly in repository root. After (0.1.0): Code lives in src/cw/ package. Migration: If importing directly, update imports from cw.module to cw.module (no change needed if using Django app imports).

Compel Prompt Support

Before (0.0.x): SDXL/SD15 models truncated prompts at 77 tokens. After (0.1.0): Automatic chunking and concatenation for unlimited prompt length, plus (word:weight) syntax support. Migration: Existing prompts work unchanged. Long prompts will no longer be truncated. Optionally use weighting syntax for emphasis.

Version History

  • [Unreleased] - In development