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@Ivan-Ayub97 Ivan-Ayub97 released this 20 Jul 10:14
4.0
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Version 4.0

Release date: 18 July 2025

1. AI Model Integration & Enhancement

1.1 SuperResolution-10 Model Implementation

  • New AI Model Integration: Added support for the SuperResolution-10 model, providing advanced super-resolution capabilities with 10x upscaling factor. This model is specifically designed for very low-resolution images and excels at significant resolution increases.
  • Specialized Processing Pipeline: Created a dedicated AI_super_resolution class that inherits from the new AI_model_base class, implementing proper preprocessing (CHW format conversion, normalization) and postprocessing (HWC format conversion, value clipping) for optimal results.
  • Model Information Integration: Added comprehensive model information in the AI model selector dialog, including year (2023), function (high-resolution image enhancement), and specialized use cases.

1.2 AI Architecture Improvements

  • Base Class Implementation: Created the missing AI_model_base class that provides common functionality for all AI models, including ONNX model loading with GPU acceleration support and proper error handling.
  • Enhanced Model Loading: Implemented robust model loading with provider selection (DML, CPU) and comprehensive error handling for missing model files.
  • VRAM Management: Added VRAM usage information for SuperResolution-10 (0.8 GB) to help users optimize their GPU memory usage.

2. Code Quality & Stability

2.1 Import System Optimization
  • Fixed Import Errors: Resolved critical NameError: name 'numpy_ndarray' is not defined by properly organizing imports at the top of the file.
  • Consolidated Imports: Removed duplicate import sections and properly structured the import hierarchy for better maintainability.
  • Type Annotation Fixes: Corrected type annotations throughout the codebase to use the proper imported numpy types.
2.2 Enhanced Error Handling
  • Model Loading Resilience: Implemented try-catch blocks for model loading operations with meaningful error messages.
  • Graceful Degradation: Added fallback mechanisms that return the original image if super-resolution enhancement fails, ensuring the application never crashes.
  • Debug Information: Enhanced logging with model loading status and error reporting for better troubleshooting.

3. User Interface Updates

3.1 Model Selection Enhancement
  • Updated Model List: SuperResolution-10 is now properly integrated into the AI model dropdown menu and categorized appropriately.
  • Information Dialog Updates: Added detailed information about the SuperResolution-10 model in the help dialog, including its capabilities and recommended use cases.
  • Model Orchestration: Enhanced the upscaling orchestrator to properly detect and route SuperResolution model tasks to the appropriate processing pipeline.

4. Technical Improvements

4.1 Processing Pipeline Optimization
  • Specialized Image Processing: Implemented dedicated image processing functions for super-resolution models that handle the unique requirements of the SuperResolution-10 model.
  • Memory Efficiency: Optimized image preprocessing and postprocessing to minimize memory usage during super-resolution operations.
  • Performance Monitoring: Added processing time tracking for super-resolution operations to help users understand processing performance.

4.2 Integration Completeness

  • Full Model Integration: SuperResolution-10 is now fully integrated into all aspects of the application, from model selection to processing to output generation.
  • Consistent User Experience: The super-resolution workflow follows the same patterns as other AI models, ensuring a consistent user experience.
  • Quality Assurance: Implemented comprehensive testing to ensure the SuperResolution-10 model works correctly with both individual images and batch processing.

5. Smart AI Model Distribution System

5.1 Automatic Model Download

  • Lightweight Installer: Significantly reduced installer size from 1.4GB to approximately 300MB by removing AI models from the installation package.
  • On-Demand Download: Implemented intelligent model downloading system that automatically fetches required AI models (327MB) when the application is first launched.
  • Progress Tracking: Added visual progress indicators with download speed and completion percentage during model acquisition.
  • Fallback URLs: Integrated multiple download sources (GitHub Releases, SourceForge) to ensure reliable model availability.
  • Resume Capability: Download system supports resuming interrupted downloads and validates file integrity.

5.2 PyInstaller Optimization

  • Optimized Packaging: Updated .spec file to exclude AI model directory from executable packaging, reducing final executable size by over 1GB.
  • Enhanced Dependencies: Added model downloader module to the build process with proper hidden imports for requests, threading, and file handling libraries.
  • Improved Compression: Increased optimization level and added module exclusions to further reduce executable size.

5.3 Installation Experience

  • Smart Setup Script: Created enhanced Inno Setup configuration that can optionally download models during installation or defer to first-run.
  • User Choice: Users can choose between offline installation (models downloaded on first run) or full installation with models included.
  • Bandwidth Optimization: Reduces initial download requirements for users with limited bandwidth, allowing them to get started faster.
  • Error Recovery: Robust error handling for network issues, with clear user feedback and retry mechanisms.
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