- Fix async/sync mismatch in TaskQueue worker implementation
- Use threading.Thread with asyncio.run() as originally designed
- Remove incorrect async task approach that caused blocking issues
- TaskQueue now properly manages its own thread lifecycle
- Resolves WebSocket message delivery and task processing issues
- Add tj-actions/changed-files to detect modified files in PR
- Only run OpenAPI validation if openapi.yaml was changed
- Only run Python linting on changed Python files (excluding legacy/)
- Remove incorrect "pip install ast" dependency
- Remove non-standard AST parsing and import checks
- Makes CI more efficient and prevents unrelated failures
- Updated generated_models.py to reflect OpenAPI 3.1 nullable format changes
- Models now use Optional[type] instead of nullable: true
- All affected models regenerated with datamodel-codegen
- Syntax and linting checks pass
- Convert all nullable: true to OpenAPI 3.1 format using type: [type, 'null']
- Fix invalid array schema definition in ManagerMappings using oneOf
- Add default security: [] configuration to satisfy security-defined rule
- All 41 validation errors resolved, spec now passes with 0 errors
- 141 warnings remain (mostly missing operationId and example validation)
- Add proper async worker management to TaskQueue class
- Remove redundant task_worker_thread and task_worker_lock global variables
- Replace manual threading with async task management
- Update is_processing() logic to use TaskQueue state instead of thread status
- Implement automatic worker cleanup when queue processing completes
- Simplify queue start endpoint to use TaskQueue.start_worker()
- Eliminate TaskQueue.ExecutionStatus NamedTuple in favor of generated TaskExecutionStatus Pydantic model
- Remove manual conversion logic between NamedTuple and Pydantic model
- Use single source of truth for task execution status
- Clean up unused imports (Literal, NamedTuple)
- Maintain consistent data model usage throughout TaskQueue
- Replace deprecated openapi-spec-validator with @redocly/cli
- Remove fragile custom regex-based route alignment script
- Use industry-standard OpenAPI validation tooling
- Switch from Python to Node.js for validation pipeline
- New validation catches 41 errors and 141 warnings that old validator missed
- Updated all POST endpoints to use proper Pydantic model validation:
- `/v2/manager/queue/task` - validates QueueTaskItem
- `/v2/manager/queue/install_model` - validates ModelMetadata
- `/v2/manager/queue/reinstall` - validates InstallPackParams
- `/v2/customnode/import_fail_info` - validates cnr_id/url fields
- Added proper error handling with ValidationError for detailed error messages
- Updated TaskQueue.put() to handle both dict and Pydantic model inputs
- Added missing imports: InstallPackParams, ModelMetadata, ValidationError
Benefits:
- Early validation catches invalid data at API boundaries
- Better error messages for clients with specific validation failures
- Type safety throughout the request processing pipeline
- Consistent validation behavior across all endpoints
All ruff checks pass and validation is now enabled by default.
Enhances ComfyUI Manager with robust batch execution tracking and unified data model architecture:
- Implemented automatic batch history serialization with before/after system state snapshots
- Added comprehensive state management capturing installed nodes, models, and ComfyUI version info
- Enhanced task queue with proper client ID handling and WebSocket notifications
- Migrated all data models to OpenAPI-generated Pydantic models for consistency
- Added documentation for new TaskQueue methods (done_count, total_count, finalize)
- Fixed 64 linting errors with proper imports and code cleanup
Technical improvements:
- All models now auto-generated from openapi.yaml ensuring API/implementation consistency
- Batch tracking captures complete system state at operation start and completion
- Enhanced REST endpoints with comprehensive documentation
- Removed manual model files in favor of single source of truth
- Added helper methods for system state capture and batch lifecycle management
- Add client_id field to QueueTaskItem and TaskHistoryItem models
- Implement client-specific WebSocket message routing
- Add client filtering to queue status and history endpoints
- Follow ComfyUI patterns for session management
- Create data_models package for better code organization
