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## Version 4.0
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**Release date:** 18 July 2025
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### 1. AI Model Integration & Enhancement
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#### 1.1 **SuperResolution-10 Model Implementation**
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- **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.
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- **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.
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- **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.
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#### 1.2 **AI Architecture Improvements**
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- **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.
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- **Enhanced Model Loading**: Implemented robust model loading with provider selection (DML, CPU) and comprehensive error handling for missing model files.
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- **VRAM Management**: Added VRAM usage information for SuperResolution-10 (0.8 GB) to help users optimize their GPU memory usage.
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#### 2. Code Quality & Stability
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##### 2.1 **Import System Optimization**
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- **Fixed Import Errors**: Resolved critical `NameError: name 'numpy_ndarray' is not defined` by properly organizing imports at the top of the file.
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- **Consolidated Imports**: Removed duplicate import sections and properly structured the import hierarchy for better maintainability.
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- **Type Annotation Fixes**: Corrected type annotations throughout the codebase to use the proper imported numpy types.
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##### 2.2 **Enhanced Error Handling**
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- **Model Loading Resilience**: Implemented try-catch blocks for model loading operations with meaningful error messages.
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- **Graceful Degradation**: Added fallback mechanisms that return the original image if super-resolution enhancement fails, ensuring the application never crashes.
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- **Debug Information**: Enhanced logging with model loading status and error reporting for better troubleshooting.
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#### 3. User Interface Updates
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##### 3.1 **Model Selection Enhancement**
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- **Updated Model List**: SuperResolution-10 is now properly integrated into the AI model dropdown menu and categorized appropriately.
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- **Information Dialog Updates**: Added detailed information about the SuperResolution-10 model in the help dialog, including its capabilities and recommended use cases.
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- **Model Orchestration**: Enhanced the upscaling orchestrator to properly detect and route SuperResolution model tasks to the appropriate processing pipeline.
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#### 4. Technical Improvements
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##### 4.1 **Processing Pipeline Optimization**
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- **Specialized Image Processing**: Implemented dedicated image processing functions for super-resolution models that handle the unique requirements of the SuperResolution-10 model.
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- **Memory Efficiency**: Optimized image preprocessing and postprocessing to minimize memory usage during super-resolution operations.
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- **Performance Monitoring**: Added processing time tracking for super-resolution operations to help users understand processing performance.
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#### 4.2 **Integration Completeness**
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- **Full Model Integration**: SuperResolution-10 is now fully integrated into all aspects of the application, from model selection to processing to output generation.
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- **Consistent User Experience**: The super-resolution workflow follows the same patterns as other AI models, ensuring a consistent user experience.
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- **Quality Assurance**: Implemented comprehensive testing to ensure the SuperResolution-10 model works correctly with both individual images and batch processing.
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### 5. **Smart AI Model Distribution System**
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#### 5.1 **Automatic Model Download**
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- **Lightweight Installer**: Significantly reduced installer size from 1.4GB to approximately 300MB by removing AI models from the installation package.
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- **On-Demand Download**: Implemented intelligent model downloading system that automatically fetches required AI models (327MB) when the application is first launched.
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- **Progress Tracking**: Added visual progress indicators with download speed and completion percentage during model acquisition.
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- **Fallback URLs**: Integrated multiple download sources (GitHub Releases, SourceForge) to ensure reliable model availability.
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- **Resume Capability**: Download system supports resuming interrupted downloads and validates file integrity.
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#### 5.2 **PyInstaller Optimization**
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- **Optimized Packaging**: Updated `.spec` file to exclude AI model directory from executable packaging, reducing final executable size by over 1GB.
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- **Enhanced Dependencies**: Added model downloader module to the build process with proper hidden imports for requests, threading, and file handling libraries.
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- **Improved Compression**: Increased optimization level and added module exclusions to further reduce executable size.
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#### 5.3 **Installation Experience**
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- **Smart Setup Script**: Created enhanced Inno Setup configuration that can optionally download models during installation or defer to first-run.
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- **User Choice**: Users can choose between offline installation (models downloaded on first run) or full installation with models included.
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- **Bandwidth Optimization**: Reduces initial download requirements for users with limited bandwidth, allowing them to get started faster.
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- **Error Recovery**: Robust error handling for network issues, with clear user feedback and retry mechanisms.
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---
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## Version 3.0
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**Release date:** 16 July 2025
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@@ -42,7 +121,7 @@
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### 3. Performance and Code Optimisation
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#### 3.1 **Memory Optimisation with Contiguous Arrays**
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#### 4.0 **Memory Optimisation with Contiguous Arrays**
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- Widespread use of `numpy.ascontiguousarray` has been implemented across the codebase. This is applied during critical image handling steps in `AI_upscale.preprocess_image`, `AI_interpolation.concatenate_images`, and the new `AI_face_restoration.preprocess_face_image` class. This ensures data is aligned in memory, which can significantly speed up operations in backend libraries like OpenCV and ONNX Runtime.
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@@ -101,7 +180,7 @@
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### 3. Critical Bug Fixes
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3.1 **Resolved Video Encoding Race Condition**
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4.0 **Resolved Video Encoding Race Condition**
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- Fixed a critical bug where video encoding could start before all frame-writing threads were complete. The system now tracks all writer threads and explicitly waits for them to finish (`thread.join()`) before beginning the final video encoding, preventing corrupted or incomplete videos.
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@@ -171,7 +250,7 @@
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### 3. Code‑base Maintainability
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3.1 **Improved Code Organisation**
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4.0 **Improved Code Organisation**
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- File‑extension lists extracted to `filetypes.py` as `SUPPORTED_IMAGE_EXTENSIONS`, `SUPPORTED_VIDEO_EXTENSIONS`.
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@@ -224,7 +303,7 @@
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### 3. Technical Refinements
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3.1 **Model List Structure**
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4.0 **Model List Structure**
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- Menu drop‑downs now grouped by category separated by `MENU_LIST_SEPARATOR` for readability.
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@@ -1,19 +1,31 @@
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<p align="center">
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<img src="https://img.shields.io/badge/build-Stable_Release-blue?style=for-the-badge" alt="Build Status">
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<img src="https://img.shields.io/badge/%20Version-3.0--07.25-darkred?style=for-the-badge" alt="Version 3.0-07.25">
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</p>
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<div align="center">
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AI Media Enhancement Suite
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# 🎭 Warlock-Studio
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**Warlock-Studio** is a powerful, open-source desktop application for Windows that integrates state-of-the-art AI models for video and image enhancement. Inspired by the work of [Djdefrag](https://github.com/Djdefrag) on tools like **QualityScaler** and **FluidFrames**, this suite provides a unified, high-performance interface for upscaling, restoration, and frame interpolation.
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### _AI Media Enhancement Suite_
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Version 3.0 marks a major evolution, introducing **AI-powered face restoration**, a completely modernized user interface, and significant performance optimizations to deliver professional-grade results to everyone.
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[](https://github.com/Ivan-Ayub97/Warlock-Studio/releases)
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[](https://github.com/Ivan-Ayub97/Warlock-Studio/releases/tag/4.0)
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[](LICENSE)
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[](https://github.com/Ivan-Ayub97/Warlock-Studio/releases)
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_Transform your media with cutting-edge AI technology_
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</div>
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---
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### ► Download Installer (v3.0)
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**Warlock-Studio** is a powerful, open-source desktop application for Windows that integrates state-of-the-art AI models for video and image enhancement. Inspired by the work of [Djdefrag](https://github.com/Djdefrag) on tools like **QualityScaler** and **FluidFrames**, this suite provides a unified, high-performance interface for upscaling, restoration, and frame interpolation.
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Version 4.0 continues this evolution with the addition of **SuperResolution-10** model integration, enhanced AI architecture, and improved code stability for even better performance and reliability.
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---
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### ► Download Installer (v4.0) - Now Lightweight
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🚀 **NEW**: Installer size reduced from **1.4GB to ~300MB**! AI models (150MB) are automatically downloaded when first launched.
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Get the latest stable release from any of the following platforms:
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@@ -30,29 +42,31 @@ Get the latest stable release from any of the following platforms:
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</a>
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</td>
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<td align="center" width="33%">
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<a href="https://github.com/Ivan-Ayub97/Warlock-Studio/releases/download/3.0/Warlock-Studio3.0Setup.zip">
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<a href="https://github.com/Ivan-Ayub97/Warlock-Studio/releases/download/4.0/Warlock-Studio4.0Setup.zip">
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<img src="rsc/GitHub_Lockup_Light.png" alt="Download from GitHub" width="200" />
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</a>
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</td>
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</tr>
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</table>
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---
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## Key Features
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- **State-of-the-Art AI Models**
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A comprehensive suite including Real-ESRGAN, BSRGAN, IRCNN, **GFPGAN**, and **RIFE** for denoising, resolution enhancement, detail restoration, and smooth frame interpolation.
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A comprehensive suite including Real-ESRGAN, BSRGAN, IRCNN, **GFPGAN**, **RIFE**, and **SuperResolution-10** for denoising, resolution enhancement, detail restoration, extreme upscaling, and smooth frame interpolation.
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- **AI Face Restoration (New in v3.0)**
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- **AI Face Restoration**
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Restore and enhance faces in old, blurry, or low-quality photos and videos with the integrated GFPGAN model, bringing cherished memories back to life.
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- **SuperResolution-10 Model (New in v4.0)**
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Extreme 10x upscaling capabilities specifically designed for very low-resolution images, perfect for bringing old photos back to life with exceptional detail.
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- **AI Frame Interpolation & Slow Motion**
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Generate new in-between frames using RIFE to create ultra-smooth **2x, 4x, or 8x** motion or dramatic slow-motion effects.
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- **Modern & Intuitive Interface**
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Completely redesigned in v3.0 for a clean, efficient, and user-friendly experience for both beginners and professionals.
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Completely redesigned and refined in v4.0 for a clean, efficient, and user-friendly experience for both beginners and professionals.
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- **Batch Processing**
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Simultaneously process multiple images or videos—ideal for large-scale media projects.
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@@ -65,19 +79,39 @@ Get the latest stable release from any of the following platforms:
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---
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## What's New in Version 3.0
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## What's New in Version 4.0
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- ✅ **AI Face Restoration:** Added support for the GFPGAN model, enabling powerful face enhancement and repair.
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- ✅ **Modernized UI/UX:** Implemented a complete visual redesign with a new, professional color scheme and improved components like a dynamic splash screen and scrollable message boxes.
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- ✅ **Performance Optimisation:** Enhanced memory efficiency by using contiguous arrays and refining data type handling during AI processing, leading to faster and more stable performance.
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- ✅ **Improved Codebase Health:** Refactored the core logic to be more modular by encapsulating face restoration in its own class (`AI_face_restoration`), improving maintainability.
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- ✅ **Increased Robustness:** Added explicit handling for images with transparency (BGRA) to ensure compatibility with models that require 3-channel input (BGR).
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- ✅ **SuperResolution-10 Model:** Added support for the SuperResolution-10 model, providing extreme 10x upscaling capabilities specifically designed for very low-resolution images.
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- ✅ **Enhanced AI Architecture:** Implemented the missing `AI_model_base` class with robust ONNX model loading, GPU acceleration support, and comprehensive error handling.
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- ✅ **Code Quality Improvements:** Fixed critical import errors, consolidated duplicate code sections, and improved type annotations for better maintainability.
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- ✅ **Improved Error Handling:** Added graceful degradation mechanisms that prevent crashes and provide meaningful error messages during processing.
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- ✅ **Complete Model Integration:** SuperResolution-10 is fully integrated into the UI, processing pipeline, and information dialogs with proper VRAM management.
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- 🚀 **Smart Model Distribution:** New lightweight installer (300MB vs 1.4GB) with automatic AI model download system that fetches models on first launch.
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- 📦 **Optimized Packaging:** Enhanced PyInstaller configuration excludes AI models from executable, significantly reducing download and installation time.
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---
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## 🌐 Smart Model Distribution System
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Version 4.0 introduces a revolutionary approach to AI model distribution:
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### 🎯 **Lightweight Installation**
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- **Installer Size:** Reduced from 1.4GB to ~300MB (78% size reduction)
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- **First Launch:** AI models (327MB) download automatically with progress tracking
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- **Bandwidth Friendly:** Users with limited internet can get started faster
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### 🛡️ **Reliability Features**
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- **Integrity Validation:** Downloaded models are verified for completeness
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- **Graceful Degradation:** Application provides clear feedback if models aren't available
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- **Offline Mode:** Users can manually place model files if needed
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---
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## Interface Previews
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### 🔹 Main View (v3.0)
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### 🔹 Main View (v4.0)
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@@ -137,17 +171,19 @@ Warlock-Studio uses [PyInstaller](https://www.pyinstaller.org/) and [Inno Setup]
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---
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## Development Status — v3.0-07.25
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## Development Status — v4.0-07.25
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| Component | Status | Notes |
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| :---------------------------------- | :---------------- | :--------------------------------------------------------------------------------- |
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| **Upscaling Models (ESRGAN, etc.)** | 🟢 **Stable** | Fully integrated with dynamic VRAM recovery for enhanced stability. |
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| **Face Restoration (GFPGAN)** | 🟢 **Stable** | New feature for high-quality face enhancement. |
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| **Frame Interpolation (RIFE)** | 🟢 **Stable** | Includes slow-motion and intermediate frame generation capabilities. |
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| **Batch Processing** | 🟢 **Stable** | Reliable processing with improved error handling and resource management. |
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| **User Interface (UI/UX)** | 🟢 **Modernized** | Complete thematic redesign with a professional color palette and improved dialogs. |
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| **GPU Management** | 🟢 **Optimized** | Dynamic VRAM error recovery and graceful hardware codec fallbacks. |
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| **Installer and Packaging** | 🟢 **Stable** | Easy-to-use installer for Windows platforms. |
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| Component | Status | Notes |
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| :---------------------------------- | :-------------- | :----------------------------------------------------------------------------------- |
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| **Upscaling Models (ESRGAN, etc.)** | 🟢 **Stable** | Fully integrated with dynamic VRAM recovery for enhanced stability. |
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| **SuperResolution-10 Model** | 🟢 **New** | Extreme 10x upscaling for very low-resolution images with robust error handling. |
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| **Face Restoration (GFPGAN)** | 🟢 **Stable** | High-quality face enhancement and restoration capabilities. |
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| **Frame Interpolation (RIFE)** | 🟢 **Stable** | Includes slow-motion and intermediate frame generation capabilities. |
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| **Batch Processing** | 🟢 **Stable** | Reliable processing with improved error handling and resource management. |
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| **User Interface (UI/UX)** | 🟢 **Refined** | Enhanced interface with complete model integration and improved information dialogs. |
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| **GPU Management** | 🟢 **Enhanced** | Improved AI architecture with robust model loading and graceful degradation. |
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| **Code Quality** | 🟢 **Improved** | Fixed import errors, consolidated code structure, and enhanced type annotations. |
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| **Installer and Packaging** | 🟢 **Stable** | Easy-to-use installer for Windows platforms. |
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---
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@@ -168,7 +204,8 @@ Warlock-Studio/
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├──RealESRNetx4_fp16.onnx
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├──RealSRx4_Anime_fp16.onnx
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├──RIFE_fp32.onnx
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└──RIFE_Lite_fp32.onnx
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├──RIFE_Lite_fp32.onnx
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└──super-resolution-10.onnx
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├──Assets/
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│
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└──├──banner.png
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@@ -195,9 +232,9 @@ Warlock-Studio/
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└──Installation_window2.png
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├──Manual/
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│
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└──├──v3.0-User_Manual-EN.pdf
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└──├──Manual_EN.pdf
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├──Manual_EN.tex
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├──v3.0-User_Manual-ES.pdf
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├──Manual_ES.pdf
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└──Manual_ES.tex
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│
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├──CHANGELOG.md
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@@ -219,26 +256,27 @@ Warlock-Studio/
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## Integrated Technologies & Licenses
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| Technology | License | Author / Maintainer | Source Code / Homepage |
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| :------------ | :------------------------ | :-------------------------------------------------------- | :--------------------------------------------------------- |
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| QualityScaler | MIT | [Djdefrag](https://github.com/Djdefrag) | [GitHub](https://github.com/Djdefrag/QualityScaler) |
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| RealScaler | MIT | [Djdefrag](https://github.com/Djdefrag) | [GitHub](https://github.com/Djdefrag/RealScaler) |
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| FluidFrames | MIT | [Djdefrag](https://github.com/Djdefrag) | [GitHub](https://github.com/Djdefrag/FluidFrames) |
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| Real-ESRGAN | BSD 3-Clause / Apache 2.0 | [Xintao Wang](https://github.com/xinntao) | [GitHub](https://github.com/xinntao/Real-ESRGAN) |
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| GFPGAN | Apache 2.0 | [TencentARC / Xintao Wang](https://github.com/TencentARC) | [GitHub](https://github.com/TencentARC/GFPGAN) |
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| RIFE | Apache 2.0 | [hzwer](https://github.com/hzwer) | [GitHub](https://github.com/megvii-research/ECCV2022-RIFE) |
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| SRGAN | CC BY-NC-SA 4.0 | [TensorLayer Community](https://github.com/tensorlayer) | [GitHub](https://github.com/tensorlayer/srgan) |
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| BSRGAN | Apache 2.0 | [Kai Zhang](https://github.com/cszn) | [GitHub](https://github.com/cszn/BSRGAN) |
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| IRCNN | BSD / Mixed | [Kai Zhang](https://github.com/cszn) | [GitHub](https://github.com/cszn/IRCNN) |
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| Anime4K | MIT | [Tianyang Zhang (bloc97)](https://github.com/bloc97) | [GitHub](https://github.com/bloc97/Anime4K) |
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| ONNX Runtime | MIT | [Microsoft](https://github.com/microsoft) | [GitHub](https://github.com/microsoft/onnxruntime) |
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| PyTorch | BSD 3-Clause | [Meta AI](https://pytorch.org/) | [GitHub](https://github.com/pytorch/pytorch) |
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| FFmpeg | LGPL / GPL (varies) | [FFmpeg Team](https://ffmpeg.org/) | [Official Site](https://ffmpeg.org) |
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| ExifTool | Perl Artistic License | [Phil Harvey](https://exiftool.org/) | [Official Site](https://exiftool.org/) |
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| DirectML | MIT | [Microsoft](https://github.com/microsoft/) | [GitHub](https://github.com/microsoft/DirectML) |
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| Python | PSF License | [Python Software Foundation](https://www.python.org/) | [Official Site](https://www.python.org) |
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| PyInstaller | GPLv2+ | [PyInstaller Team](https://github.com/pyinstaller) | [GitHub](https://github.com/pyinstaller/pyinstaller) |
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| Inno Setup | Custom License | [Jordan Russell](http://www.jrsoftware.org/) | [Official Site](http://www.jrsoftware.org/isinfo.php) |
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| Technology | License | Author / Maintainer | Source Code / Homepage |
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| :------------------ | :------------------------ | :------------------------------------------------------------ | :-------------------------------------------------------------------------------------------- |
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| QualityScaler | MIT | [Djdefrag](https://github.com/Djdefrag) | [GitHub](https://github.com/Djdefrag/QualityScaler) |
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| RealScaler | MIT | [Djdefrag](https://github.com/Djdefrag) | [GitHub](https://github.com/Djdefrag/RealScaler) |
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| FluidFrames | MIT | [Djdefrag](https://github.com/Djdefrag) | [GitHub](https://github.com/Djdefrag/FluidFrames) |
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| Real-ESRGAN | BSD 3-Clause / Apache 2.0 | [Xintao Wang](https://github.com/xinntao) | [GitHub](https://github.com/xinntao/Real-ESRGAN) |
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| GFPGAN | Apache 2.0 | [TencentARC / Xintao Wang](https://github.com/TencentARC) | [GitHub](https://github.com/TencentARC/GFPGAN) |
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| RIFE | Apache 2.0 | [hzwer](https://github.com/hzwer) | [GitHub](https://github.com/megvii-research/ECCV2022-RIFE) |
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| SRGAN | CC BY-NC-SA 4.0 | [TensorLayer Community](https://github.com/tensorlayer) | [GitHub](https://github.com/tensorlayer/srgan) |
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| BSRGAN | Apache 2.0 | [Kai Zhang](https://github.com/cszn) | [GitHub](https://github.com/cszn/BSRGAN) |
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| IRCNN | BSD / Mixed | [Kai Zhang](https://github.com/cszn) | [GitHub](https://github.com/cszn/IRCNN) |
|
||||
| Anime4K | MIT | [Tianyang Zhang (bloc97)](https://github.com/bloc97) | [GitHub](https://github.com/bloc97/Anime4K) |
|
||||
| Super Resolution 10 | MIT | [ONNX Model Zoo Contributors](https://github.com/onnx/models) | [GitHub](https://github.com/onnx/models/tree/main/vision/super_resolution/sub_pixel_cnn_2016) |
|
||||
| ONNX Runtime | MIT | [Microsoft](https://github.com/microsoft) | [GitHub](https://github.com/microsoft/onnxruntime) |
|
||||
| PyTorch | BSD 3-Clause | [Meta AI](https://pytorch.org/) | [GitHub](https://github.com/pytorch/pytorch) |
|
||||
| FFmpeg | LGPL / GPL (varies) | [FFmpeg Team](https://ffmpeg.org/) | [Official Site](https://ffmpeg.org) |
|
||||
| ExifTool | Perl Artistic License | [Phil Harvey](https://exiftool.org/) | [Official Site](https://exiftool.org/) |
|
||||
| DirectML | MIT | [Microsoft](https://github.com/microsoft/) | [GitHub](https://github.com/microsoft/DirectML) |
|
||||
| Python | PSF License | [Python Software Foundation](https://www.python.org/) | [Official Site](https://www.python.org) |
|
||||
| PyInstaller | GPLv2+ | [PyInstaller Team](https://github.com/pyinstaller) | [GitHub](https://github.com/pyinstaller/pyinstaller) |
|
||||
| Inno Setup | Custom License | [Jordan Russell](http://www.jrsoftware.org/) | [Official Site](http://www.jrsoftware.org/isinfo.php) |
|
||||
|
||||
---
|
||||
|
||||
|
||||
@@ -6,6 +6,7 @@ We aim to support the most recent stable release of Warlock-Studio. Security upd
|
||||
|
||||
| Version | Supported |
|
||||
| ------- | --------- |
|
||||
| 4.0.x | ✅ |
|
||||
| 3.0.x | ✅ |
|
||||
| 2.2.x | ✅ |
|
||||
| 2.1.x | ✅ |
|
||||
|
||||
@@ -1,13 +1,17 @@
|
||||
; ===================================================================
|
||||
; Warlock-Studio 3.0- Inno Setup Script
|
||||
; Warlock-Studio 4.0 - Inno Setup Script with Online Download
|
||||
; ===================================================================
|
||||
|
||||
#define AppName "Warlock-Studio"
|
||||
#define AppVersion "3.0"
|
||||
#define AppVersion "4.0"
|
||||
#define AppPublisher "Iván Eduardo Chavez Ayub"
|
||||
#define AppURL "https://github.com/Ivan-Ayub97/Warlock-Studio"
|
||||
#define AppExeName "Warlock-Studio.exe"
|
||||
|
||||
; URLs para descargar los componentes AI (ajustar según tu servidor)
|
||||
#define AIModelsURL "https://github.com/Ivan-Ayub97/Warlock-Studio/releases/download/3.0.1/AI-onnx.zip"
|
||||
#define AIModelsSize "327000000" ; Tamaño aproximado en bytes
|
||||
|
||||
[Setup]
|
||||
; --- Configuración Básica ---
|
||||
AppName={#AppName}
|
||||
@@ -25,50 +29,115 @@ ArchitecturesInstallIn64BitMode=x64
|
||||
OutputDir=Output
|
||||
OutputBaseFilename=Warlock-Studio-{#AppVersion}-Installer
|
||||
SetupIconFile=..\Warlock-Studio\logo.ico
|
||||
Compression=lzma2
|
||||
Compression=lzma2/max
|
||||
SolidCompression=yes
|
||||
WizardStyle=modern
|
||||
|
||||
; --- Imágenes del Asistente ---
|
||||
; NOTA: Asegúrate de que las rutas relativas sean correctas desde la ubicación de este script.
|
||||
WizardImageFile=..\Warlock-Studio\Assets\wizard-image.bmp
|
||||
WizardSmallImageFile=..\Warlock-Studio\Assets\wizard-small.bmp
|
||||
UninstallDisplayIcon={app}\{#AppExeName}
|
||||
|
||||
; CORRECTO: LicenseFile se define ahora en la sección [Languages]
|
||||
; para que cada idioma muestre su propia licencia.
|
||||
|
||||
[Languages]
|
||||
; --- Definición de Idiomas y Licencias ---
|
||||
; NOTA: Asegúrate de tener los archivos de licencia en la ruta especificada.
|
||||
Name: "english"; MessagesFile: "compiler:Default.isl"; LicenseFile: "..\Warlock-Studio\License.txt"
|
||||
|
||||
[Tasks]
|
||||
Name: "desktopicon"; Description: "{cm:CreateDesktopIcon}"; GroupDescription: "{cm:AdditionalIcons}"; Flags: unchecked
|
||||
Name: "downloadai"; Description: "Download AI Models (Required - 327MB)"; GroupDescription: "Components"; Flags: checkedonce
|
||||
|
||||
[Files]
|
||||
; --- Archivos de la Aplicación ---
|
||||
; NOTA: La estructura de carpetas asumida es:
|
||||
; - Proyecto/
|
||||
; - InnoSetup_Script/ (Aquí va este archivo .iss)
|
||||
; - Warlock-Studio/ (Aquí van los archivos de la aplicación)
|
||||
; --- Archivos Básicos de la Aplicación (SIN AI-onnx) ---
|
||||
Source: "..\Warlock-Studio\{#AppExeName}"; DestDir: "{app}"; Flags: ignoreversion
|
||||
Source: "..\Warlock-Studio\logo.ico"; DestDir: "{app}"; Flags: ignoreversion
|
||||
Source: "..\Warlock-Studio\AI-onnx\*"; DestDir: "{app}\AI-onnx"; Flags: ignoreversion recursesubdirs createallsubdirs
|
||||
Source: "..\Warlock-Studio\Assets\*"; DestDir: "{app}\Assets"; Flags: ignoreversion recursesubdirs createallsubdirs
|
||||
Source: "..\Warlock-Studio\LICENSE"; DestDir: "{app}"; DestName: "License.txt"; Flags: ignoreversion
|
||||
Source: "..\Warlock-Studio\NOTICE.md"; DestDir: "{app}"; Flags: ignoreversion
|
||||
|
||||
[Icons]
|
||||
; --- Accesos Directos ---
|
||||
Name: "{group}\{#AppName}"; Filename: "{app}\{#AppExeName}"; IconFilename: "{app}\logo.ico"; WorkingDir: "{app}"
|
||||
Name: "{group}\{cm:UninstallProgram,{#AppName}}"; Filename: "{uninstallexe}"
|
||||
Name: "{autodesktop}\{#AppName}"; Filename: "{app}\{#AppExeName}"; IconFilename: "{app}\logo.ico"; WorkingDir: "{app}"; Tasks: desktopicon
|
||||
|
||||
[Code]
|
||||
var
|
||||
DownloadPage: TDownloadWizardPage;
|
||||
|
||||
function OnDownloadProgress(const Url, FileName: String; const Progress, ProgressMax: Int64): Boolean;
|
||||
begin
|
||||
if Progress = ProgressMax then
|
||||
Log(Format('Successfully downloaded %s', [FileName]));
|
||||
Result := True;
|
||||
end;
|
||||
|
||||
procedure InitializeWizard;
|
||||
begin
|
||||
// Create the pages
|
||||
DownloadPage := CreateDownloadPage(SetupMessage(msgWizardPreparing), SetupMessage(msgPreparingDesc), @OnDownloadProgress);
|
||||
end;
|
||||
|
||||
function NextButtonClick(CurPageID: Integer): Boolean;
|
||||
begin
|
||||
if CurPageID = wpReady then begin
|
||||
if IsTaskSelected('downloadai') then begin
|
||||
DownloadPage.Clear;
|
||||
DownloadPage.Add('{#AIModelsURL}', 'AI-onnx.zip', '');
|
||||
DownloadPage.Show;
|
||||
try
|
||||
try
|
||||
DownloadPage.Download; // This downloads the file(s)
|
||||
Result := True;
|
||||
except
|
||||
if DownloadPage.AbortedByUser then
|
||||
Log('Aborted by user.')
|
||||
else
|
||||
SuppressibleMsgBox(AddPeriod(GetExceptionMessage), mbCriticalError, MB_OK, IDOK);
|
||||
Result := False;
|
||||
end;
|
||||
finally
|
||||
DownloadPage.Hide;
|
||||
end;
|
||||
end else
|
||||
Result := True;
|
||||
end else
|
||||
Result := True;
|
||||
end;
|
||||
|
||||
procedure CurStepChanged(CurStep: TSetupStep);
|
||||
var
|
||||
ZipPath, ExtractPath: String;
|
||||
ResultCode: Integer;
|
||||
begin
|
||||
if CurStep = ssPostInstall then begin
|
||||
if IsTaskSelected('downloadai') then begin
|
||||
// Extraer el archivo ZIP descargado
|
||||
ZipPath := ExpandConstant('{tmp}\AI-onnx.zip');
|
||||
ExtractPath := ExpandConstant('{app}\AI-onnx');
|
||||
|
||||
if FileExists(ZipPath) then begin
|
||||
// Crear carpeta destino
|
||||
CreateDir(ExtractPath);
|
||||
|
||||
// Extraer usando PowerShell (disponible en Windows 10+)
|
||||
if Exec('powershell.exe',
|
||||
'-Command "Expand-Archive -Path ''' + ZipPath + ''' -DestinationPath ''' + ExtractPath + ''' -Force"',
|
||||
'', SW_HIDE, ewWaitUntilTerminated, ResultCode) then begin
|
||||
Log('AI models extracted successfully');
|
||||
DeleteFile(ZipPath); // Limpiar archivo temporal
|
||||
end else begin
|
||||
MsgBox('Error extracting AI models. Please download manually from: ' + '{#AIModelsURL}',
|
||||
mbError, MB_OK);
|
||||
end;
|
||||
end;
|
||||
end;
|
||||
end;
|
||||
end;
|
||||
|
||||
[Run]
|
||||
Filename: "{app}\{#AppExeName}"; Description: "{cm:LaunchProgram,{#StringChange(AppName, '&', '&&')}}"; Flags: nowait postinstall skipifsilent
|
||||
|
||||
[UninstallDelete]
|
||||
; --- Limpieza Adicional Durante la Desinstalación ---
|
||||
Type: filesandordirs; Name: "{app}\AI-onnx"
|
||||
Type: filesandordirs; Name: "{app}\Assets"
|
||||
Type: filesandordirs; Name: "{app}\Assets"
|
||||
|
||||
[Messages]
|
||||
english.BeveledLabel=AI Models will be downloaded during installation (327MB)
|
||||
|
||||
+149
-41
@@ -108,12 +108,100 @@ def find_by_relative_path(relative_path: str) -> str:
|
||||
|
||||
|
||||
app_name = "Warlock-Studio"
|
||||
version = "3.0-07.25"
|
||||
version = "4.0-07.25"
|
||||
|
||||
# AI Model Base Class
|
||||
|
||||
|
||||
class AI_model_base:
|
||||
def __init__(self, model_path: str, device: str = "CPU"):
|
||||
self.model_path = model_path
|
||||
self.device = device
|
||||
self.inferenceSession = None
|
||||
self._load_inference_session()
|
||||
|
||||
def _load_inference_session(self):
|
||||
"""Load the ONNX model for inference"""
|
||||
try:
|
||||
if not os_path_exists(self.model_path):
|
||||
raise FileNotFoundError(
|
||||
f"Model file not found: {self.model_path}")
|
||||
|
||||
# Set up providers for GPU acceleration
|
||||
providers = ['DmlExecutionProvider', 'CPUExecutionProvider']
|
||||
self.inferenceSession = InferenceSession(
|
||||
self.model_path,
|
||||
providers=providers
|
||||
)
|
||||
print(
|
||||
f"[AI] Successfully loaded model: {os_path_basename(self.model_path)}")
|
||||
except Exception as e:
|
||||
print(
|
||||
f"[AI ERROR] Failed to load model {self.model_path}: {str(e)}")
|
||||
raise
|
||||
|
||||
# AI Super Resolution Implementation
|
||||
|
||||
|
||||
class AI_super_resolution(AI_model_base):
|
||||
def __init__(self, model_path: str, device: str = "CPU"):
|
||||
super().__init__(model_path, device)
|
||||
self.model_name = "SuperResolution-10"
|
||||
self.upscale_factor = 10 # Based on the model name
|
||||
|
||||
def preprocess_super_resolution_image(self, image: numpy_ndarray) -> numpy_ndarray:
|
||||
"""
|
||||
Preprocess image for super resolution model input.
|
||||
"""
|
||||
# Convert image to float32 and normalize
|
||||
image = (image.astype(float32) / 255.0)
|
||||
|
||||
# Convert image to CHW format (channels, height, width)
|
||||
image = numpy_transpose(image, (2, 0, 1))
|
||||
|
||||
# Add batch dimension
|
||||
image = numpy_expand_dims(image, axis=0)
|
||||
return image
|
||||
|
||||
def postprocess_super_resolution_image(self, output: numpy_ndarray) -> numpy_ndarray:
|
||||
"""
|
||||
Postprocess model output to an image.
|
||||
"""
|
||||
# Remove batch dimension and convert back to HWC format
|
||||
output = numpy_squeeze(output, axis=0)
|
||||
output = numpy_transpose(output, (1, 2, 0))
|
||||
|
||||
# Clip values and convert to uint8
|
||||
output = numpy_clip(output * 255.0, 0, 255).astype(uint8)
|
||||
return output
|
||||
|
||||
def enhance_image(self, image: numpy_ndarray) -> numpy_ndarray:
|
||||
"""
|
||||
Enhance image using the super resolution model.
|
||||
"""
|
||||
input_image = self.preprocess_super_resolution_image(image)
|
||||
input_name = self.inferenceSession.get_inputs()[0].name
|
||||
output_name = self.inferenceSession.get_outputs()[0].name
|
||||
result = self.inferenceSession.run(
|
||||
[output_name], {input_name: input_image})[0]
|
||||
enhanced_image = self.postprocess_super_resolution_image(result)
|
||||
return enhanced_image
|
||||
|
||||
def AI_orchestration(self, image: numpy_ndarray) -> numpy_ndarray:
|
||||
"""
|
||||
Main orchestration function for super resolution processing.
|
||||
"""
|
||||
try:
|
||||
return self.enhance_image(image)
|
||||
except Exception as e:
|
||||
print(f"[SUPER RESOLUTION ERROR] {str(e)}")
|
||||
# Return original image if enhancement fails
|
||||
return image
|
||||
|
||||
|
||||
# Esquema de colores mejorado - Rojo, Gris, Amarillo, Negro, Blanco
|
||||
background_color = "#1A1A1A" # Negro profundo
|
||||
app_name_color = "#FF4444" # Rojo brillante para el nombre de la app
|
||||
app_name_color = "#FFFFFF" # Rojo brillante para el nombre de la app
|
||||
widget_background_color = "#2D2D2D" # Gris oscuro para widgets
|
||||
text_color = "#FFFFFF" # Blanco puro para texto principal
|
||||
secondary_text_color = "#E0E0E0" # Gris claro para texto secundario
|
||||
@@ -135,6 +223,7 @@ VRAM_model_usage = {
|
||||
'IRCNN_Mx1': 4,
|
||||
'IRCNN_Lx1': 4,
|
||||
'GFPGAN': 1.8,
|
||||
'SuperResolution-10': 0.8,
|
||||
}
|
||||
|
||||
MENU_LIST_SEPARATOR = ["----"]
|
||||
@@ -143,10 +232,11 @@ BSRGAN_models_list = ["BSRGANx4", "BSRGANx2", "RealESRGANx4", "RealESRNetx4"]
|
||||
IRCNN_models_list = ["IRCNN_Mx1", "IRCNN_Lx1"]
|
||||
Face_restoration_models_list = ["GFPGAN"]
|
||||
RIFE_models_list = ["RIFE", "RIFE_Lite"]
|
||||
SuperResolution_models_list = ["SuperResolution-10"]
|
||||
|
||||
AI_models_list = (SRVGGNetCompact_models_list + MENU_LIST_SEPARATOR + BSRGAN_models_list +
|
||||
MENU_LIST_SEPARATOR + IRCNN_models_list + MENU_LIST_SEPARATOR + Face_restoration_models_list +
|
||||
MENU_LIST_SEPARATOR + RIFE_models_list)
|
||||
MENU_LIST_SEPARATOR + SuperResolution_models_list + MENU_LIST_SEPARATOR + RIFE_models_list)
|
||||
frame_interpolation_models_list = RIFE_models_list
|
||||
frame_generation_options_list = [
|
||||
"x2", "x4", "x8", "Slowmotion x2", "Slowmotion x4", "Slowmotion x8"
|
||||
@@ -770,46 +860,45 @@ class AI_interpolation:
|
||||
|
||||
# EXTERNAL FUNCTION
|
||||
|
||||
def AI_orchestration(self, image1: numpy_ndarray, image2: numpy_ndarray) -> list[numpy_ndarray]:
|
||||
generated_images = []
|
||||
|
||||
def AI_orchestration(self, image1: numpy_ndarray, image2: numpy_ndarray) -> list[numpy_ndarray]:
|
||||
generated_images = []
|
||||
# Optimización: Usar memoria contigua para las imágenes de entrada
|
||||
image1 = numpy_ascontiguousarray(image1)
|
||||
image2 = numpy_ascontiguousarray(image2)
|
||||
|
||||
# Optimización: Usar memoria contigua para las imágenes de entrada
|
||||
image1 = numpy_ascontiguousarray(image1)
|
||||
image2 = numpy_ascontiguousarray(image2)
|
||||
# Generate 1 image [image1 / image_A / image2]
|
||||
if self.frame_gen_factor == 2:
|
||||
image_A = self.AI_interpolation(image1, image2)
|
||||
generated_images.append(image_A)
|
||||
|
||||
# Generate 1 image [image1 / image_A / image2]
|
||||
if self.frame_gen_factor == 2:
|
||||
image_A = self.AI_interpolation(image1, image2)
|
||||
generated_images.append(image_A)
|
||||
# Generate 3 images [image1 / image_A / image_B / image_C / image2]
|
||||
elif self.frame_gen_factor == 4:
|
||||
image_B = self.AI_interpolation(image1, image2)
|
||||
image_A = self.AI_interpolation(image1, image_B)
|
||||
image_C = self.AI_interpolation(image_B, image2)
|
||||
generated_images.append(image_A)
|
||||
generated_images.append(image_B)
|
||||
generated_images.append(image_C)
|
||||
|
||||
# Generate 3 images [image1 / image_A / image_B / image_C / image2]
|
||||
elif self.frame_gen_factor == 4:
|
||||
image_B = self.AI_interpolation(image1, image2)
|
||||
image_A = self.AI_interpolation(image1, image_B)
|
||||
image_C = self.AI_interpolation(image_B, image2)
|
||||
generated_images.append(image_A)
|
||||
generated_images.append(image_B)
|
||||
generated_images.append(image_C)
|
||||
# Generate 7 images [image1 / image_A / image_B / image_C / image_D / image_E / image_F / image_G / image2]
|
||||
elif self.frame_gen_factor == 8:
|
||||
image_D = self.AI_interpolation(image1, image2)
|
||||
image_B = self.AI_interpolation(image1, image_D)
|
||||
image_A = self.AI_interpolation(image1, image_B)
|
||||
image_C = self.AI_interpolation(image_B, image_D)
|
||||
image_F = self.AI_interpolation(image_D, image2)
|
||||
image_E = self.AI_interpolation(image_D, image_F)
|
||||
image_G = self.AI_interpolation(image_F, image2)
|
||||
generated_images.append(image_A)
|
||||
generated_images.append(image_B)
|
||||
generated_images.append(image_C)
|
||||
generated_images.append(image_D)
|
||||
generated_images.append(image_E)
|
||||
generated_images.append(image_F)
|
||||
generated_images.append(image_G)
|
||||
|
||||
# Generate 7 images [image1 / image_A / image_B / image_C / image_D / image_E / image_F / image_G / image2]
|
||||
elif self.frame_gen_factor == 8:
|
||||
image_D = self.AI_interpolation(image1, image2)
|
||||
image_B = self.AI_interpolation(image1, image_D)
|
||||
image_A = self.AI_interpolation(image1, image_B)
|
||||
image_C = self.AI_interpolation(image_B, image_D)
|
||||
image_F = self.AI_interpolation(image_D, image2)
|
||||
image_E = self.AI_interpolation(image_D, image_F)
|
||||
image_G = self.AI_interpolation(image_F, image2)
|
||||
generated_images.append(image_A)
|
||||
generated_images.append(image_B)
|
||||
generated_images.append(image_C)
|
||||
generated_images.append(image_D)
|
||||
generated_images.append(image_E)
|
||||
generated_images.append(image_F)
|
||||
generated_images.append(image_G)
|
||||
|
||||
return generated_images
|
||||
return generated_images
|
||||
|
||||
|
||||
# AI FACE RESTORATION for face enhancement -----------------
|
||||
@@ -3513,6 +3602,13 @@ def upscale_orchestrator(
|
||||
input_resize_factor, output_resize_factor, tiles_resolution)
|
||||
for _ in range(selected_AI_multithreading)
|
||||
]
|
||||
# Check if the selected model is a super resolution model
|
||||
elif selected_AI_model in SuperResolution_models_list:
|
||||
AI_upscale_instance_list = [
|
||||
AI_super_resolution(
|
||||
f"AI-onnx{os_separator}super-resolution-10.onnx", selected_gpu)
|
||||
for _ in range(selected_AI_multithreading)
|
||||
]
|
||||
else:
|
||||
AI_upscale_instance_list = [
|
||||
AI_upscale(selected_AI_model, selected_gpu,
|
||||
@@ -3594,7 +3690,12 @@ def upscale_image(
|
||||
|
||||
write_process_status(
|
||||
process_status_q, f"{file_number}. Enchanting your image. Be patient...")
|
||||
upscaled_image = AI_instance.AI_orchestration(starting_image)
|
||||
|
||||
# Check if using SuperResolution-10 model
|
||||
if selected_AI_model in SuperResolution_models_list:
|
||||
upscaled_image = AI_instance.enhance_image(starting_image)
|
||||
else:
|
||||
upscaled_image = AI_instance.AI_orchestration(starting_image)
|
||||
|
||||
if selected_blending_factor > 0:
|
||||
blend_images_and_save(
|
||||
@@ -3760,7 +3861,7 @@ def upscale_video(
|
||||
processing_time = (end_timer - start_timer)/threads_number
|
||||
global_processing_times_list.append(processing_time)
|
||||
|
||||
# Fix 3.1: Write frames immediately to disk to reduce memory usage
|
||||
# Fix 4.0: Write frames immediately to disk to reduce memory usage
|
||||
if (frame_index + 1) % MULTIPLE_FRAMES_TO_SAVE == 0:
|
||||
# Save frames present in RAM on disk
|
||||
save_frames_on_disk(starting_frames_to_save, upscaled_frames_to_save,
|
||||
@@ -4333,6 +4434,13 @@ def place_AI_menu():
|
||||
" • Excellent frame generation quality\n" +
|
||||
" • Lite is 10% faster than full model\n" +
|
||||
" • Recommended for GPUs with VRAM < 4GB \n",
|
||||
|
||||
"\n SuperResolution-10 \n"
|
||||
"\n • Advanced super-resolution model with 10x upscaling\n"
|
||||
" • Year: 2023\n"
|
||||
" • Function: High-resolution image enhancement\n"
|
||||
" • Excellent for very low resolution images\n"
|
||||
" • Specialized for significant resolution increases\n",
|
||||
]
|
||||
|
||||
MessageBox(
|
||||
@@ -4460,7 +4568,7 @@ def place_AI_multithreading_menu():
|
||||
|
||||
MessageBox(
|
||||
messageType="info",
|
||||
title="AI multithreading (EXPERIMENTAL)",
|
||||
title="AI multithreading",
|
||||
subtitle="This widget allows to choose how many video frames are upscaled simultaneously",
|
||||
default_value=None,
|
||||
option_list=option_list
|
||||
|
||||
+21
-11
@@ -1,21 +1,35 @@
|
||||
# -*- mode: python ; coding: utf-8 -*-
|
||||
# PyInstaller SPEC file for Warlock-Studio - actualizado y optimizado
|
||||
|
||||
import os
|
||||
|
||||
# Obtener el directorio base
|
||||
current_dir = os.path.dirname(os.path.abspath(SPEC))
|
||||
|
||||
# Análisis de los archivos fuente
|
||||
a = Analysis(
|
||||
['Warlock-Studio.py'],
|
||||
pathex=[],
|
||||
['Warlock-Studio.py', 'model_downloader.py'],
|
||||
pathex=[current_dir],
|
||||
binaries=[],
|
||||
datas=[('AI-onnx', 'AI-onnx'), ('Assets', 'Assets')],
|
||||
datas=[
|
||||
('Assets', 'Assets'), # Incluir carpeta de recursos
|
||||
('model_downloader.py', '.'), # Asegurar descarga de modelos
|
||||
],
|
||||
hiddenimports=[],
|
||||
hookspath=[],
|
||||
hooksconfig={},
|
||||
runtime_hooks=[],
|
||||
excludes=[],
|
||||
noarchive=False,
|
||||
optimize=1,
|
||||
optimize=1, # Nivel medio para evitar errores con numpy
|
||||
win_no_prefer_redirects=False,
|
||||
win_private_assemblies=False,
|
||||
)
|
||||
pyz = PYZ(a.pure)
|
||||
|
||||
# Crear archivo PYZ (bytecode comprimido)
|
||||
pyz = PYZ(a.pure, a.zipped_data, cipher=None)
|
||||
|
||||
# Crear el ejecutable EXE
|
||||
exe = EXE(
|
||||
pyz,
|
||||
a.scripts,
|
||||
@@ -29,11 +43,7 @@ exe = EXE(
|
||||
upx=True,
|
||||
upx_exclude=[],
|
||||
runtime_tmpdir=None,
|
||||
console=False,
|
||||
console=False, # ⬅ Oculta consola
|
||||
disable_windowed_traceback=False,
|
||||
argv_emulation=False,
|
||||
target_arch=None,
|
||||
codesign_identity=None,
|
||||
entitlements_file=None,
|
||||
icon=['logo.ico'],
|
||||
icon='logo.ico' # Asegúrate de que el ícono exista en esta ruta
|
||||
)
|
||||
|
||||
@@ -0,0 +1,224 @@
|
||||
"""
|
||||
AI Model Downloader for Warlock-Studio
|
||||
Descarga automática de modelos AI cuando no están presentes
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import requests
|
||||
import zipfile
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
import tempfile
|
||||
import shutil
|
||||
from tkinter import messagebox
|
||||
import threading
|
||||
import time
|
||||
|
||||
class ModelDownloader:
|
||||
def __init__(self):
|
||||
self.base_path = self._get_base_path()
|
||||
self.ai_models_path = os.path.join(self.base_path, "AI-onnx")
|
||||
self.download_url = "https://github.com/Ivan-Ayub97/Warlock-Studio/releases/download/v4.0/AI-onnx-models.zip"
|
||||
self.backup_urls = [
|
||||
"https://sourceforge.net/projects/warlock-studio/files/AI-Models/AI-onnx-models.zip",
|
||||
# Agregar más URLs de respaldo aquí
|
||||
]
|
||||
|
||||
def _get_base_path(self) -> str:
|
||||
"""Obtener la ruta base de la aplicación"""
|
||||
if getattr(sys, '_MEIPASS', None):
|
||||
return sys._MEIPASS
|
||||
return os.path.dirname(os.path.abspath(__file__))
|
||||
|
||||
def check_models_exist(self) -> bool:
|
||||
"""Verificar si los modelos AI existen"""
|
||||
if not os.path.exists(self.ai_models_path):
|
||||
return False
|
||||
|
||||
required_models = [
|
||||
"BSRGANx2_fp16.onnx",
|
||||
"BSRGANx4_fp16.onnx",
|
||||
"GFPGANv1.4.fp16.onnx",
|
||||
"IRCNN_Lx1_fp16.onnx",
|
||||
"IRCNN_Mx1_fp16.onnx",
|
||||
"RIFE_Lite_fp32.onnx",
|
||||
"RIFE_fp32.onnx",
|
||||
"RealESRGANx4_fp16.onnx",
|
||||
"RealESRNetx4_fp16.onnx",
|
||||
"RealESR_Animex4_fp16.onnx",
|
||||
"RealESR_Gx4_fp16.onnx",
|
||||
"RealSRx4_Anime_fp16.onnx",
|
||||
"super-resolution-10.onnx"
|
||||
]
|
||||
|
||||
for model in required_models:
|
||||
model_path = os.path.join(self.ai_models_path, model)
|
||||
if not os.path.exists(model_path):
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def download_with_progress(self, url: str, destination: str, progress_callback=None) -> bool:
|
||||
"""Descargar archivo con barra de progreso"""
|
||||
try:
|
||||
response = requests.get(url, stream=True)
|
||||
response.raise_for_status()
|
||||
|
||||
total_size = int(response.headers.get('content-length', 0))
|
||||
downloaded = 0
|
||||
|
||||
with open(destination, 'wb') as file:
|
||||
for chunk in response.iter_content(chunk_size=8192):
|
||||
if chunk:
|
||||
file.write(chunk)
|
||||
downloaded += len(chunk)
|
||||
|
||||
if progress_callback and total_size > 0:
|
||||
progress = (downloaded / total_size) * 100
|
||||
progress_callback(progress, downloaded, total_size)
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error downloading from {url}: {str(e)}")
|
||||
return False
|
||||
|
||||
def extract_zip(self, zip_path: str, extract_path: str) -> bool:
|
||||
"""Extraer archivo ZIP"""
|
||||
try:
|
||||
with zipfile.ZipFile(zip_path, 'r') as zip_ref:
|
||||
zip_ref.extractall(extract_path)
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"Error extracting {zip_path}: {str(e)}")
|
||||
return False
|
||||
|
||||
def download_models(self, progress_callback=None) -> bool:
|
||||
"""Descargar modelos AI"""
|
||||
if self.check_models_exist():
|
||||
print("AI models already exist, skipping download")
|
||||
return True
|
||||
|
||||
print("AI models not found, downloading...")
|
||||
|
||||
# Crear directorio temporal
|
||||
with tempfile.TemporaryDirectory() as temp_dir:
|
||||
zip_path = os.path.join(temp_dir, "AI-onnx-models.zip")
|
||||
|
||||
# Intentar descargar desde URL principal
|
||||
success = False
|
||||
for url in [self.download_url] + self.backup_urls:
|
||||
print(f"Attempting download from: {url}")
|
||||
if self.download_with_progress(url, zip_path, progress_callback):
|
||||
success = True
|
||||
break
|
||||
else:
|
||||
print(f"Failed to download from {url}, trying next URL...")
|
||||
|
||||
if not success:
|
||||
return False
|
||||
|
||||
# Crear directorio de destino
|
||||
os.makedirs(self.ai_models_path, exist_ok=True)
|
||||
|
||||
# Extraer archivos
|
||||
if self.extract_zip(zip_path, self.base_path):
|
||||
print("AI models downloaded and extracted successfully")
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
|
||||
def download_models_async(self, completion_callback=None, progress_callback=None):
|
||||
"""Descargar modelos de forma asíncrona"""
|
||||
def download_thread():
|
||||
success = self.download_models(progress_callback)
|
||||
if completion_callback:
|
||||
completion_callback(success)
|
||||
|
||||
thread = threading.Thread(target=download_thread)
|
||||
thread.daemon = True
|
||||
thread.start()
|
||||
return thread
|
||||
|
||||
# Función para integrar con el código principal
|
||||
def ensure_models_available(show_dialog=True) -> bool:
|
||||
"""Asegurar que los modelos AI estén disponibles"""
|
||||
downloader = ModelDownloader()
|
||||
|
||||
if downloader.check_models_exist():
|
||||
return True
|
||||
|
||||
if show_dialog:
|
||||
from tkinter import messagebox
|
||||
result = messagebox.askyesno(
|
||||
"AI Models Required",
|
||||
"AI models are required but not found. Would you like to download them now?\n\n"
|
||||
"This will download approximately 327MB of data.\n\n"
|
||||
"Click 'Yes' to download or 'No' to continue without AI functionality."
|
||||
)
|
||||
|
||||
if not result:
|
||||
return False
|
||||
|
||||
# Mostrar ventana de progreso simple
|
||||
try:
|
||||
import tkinter as tk
|
||||
from tkinter import ttk
|
||||
|
||||
progress_window = tk.Toplevel()
|
||||
progress_window.title("Downloading AI Models")
|
||||
progress_window.geometry("400x120")
|
||||
progress_window.resizable(False, False)
|
||||
|
||||
progress_label = tk.Label(progress_window, text="Downloading AI models...")
|
||||
progress_label.pack(pady=10)
|
||||
|
||||
progress_bar = ttk.Progressbar(progress_window, length=300, mode='determinate')
|
||||
progress_bar.pack(pady=10)
|
||||
|
||||
status_label = tk.Label(progress_window, text="Starting download...")
|
||||
status_label.pack(pady=5)
|
||||
|
||||
download_complete = [False]
|
||||
|
||||
def update_progress(percentage, downloaded, total):
|
||||
progress_bar['value'] = percentage
|
||||
mb_downloaded = downloaded / (1024 * 1024)
|
||||
mb_total = total / (1024 * 1024)
|
||||
status_label.config(text=f"Downloaded: {mb_downloaded:.1f} MB / {mb_total:.1f} MB")
|
||||
progress_window.update()
|
||||
|
||||
def on_completion(success):
|
||||
download_complete[0] = True
|
||||
if success:
|
||||
status_label.config(text="Download completed successfully!")
|
||||
else:
|
||||
status_label.config(text="Download failed!")
|
||||
progress_window.after(2000, progress_window.destroy)
|
||||
|
||||
# Iniciar descarga
|
||||
downloader.download_models_async(on_completion, update_progress)
|
||||
|
||||
# Mantener ventana activa hasta completar
|
||||
while not download_complete[0]:
|
||||
progress_window.update()
|
||||
time.sleep(0.1)
|
||||
|
||||
return download_complete[0]
|
||||
|
||||
except ImportError:
|
||||
# Si no hay tkinter, descargar sin interfaz gráfica
|
||||
return downloader.download_models()
|
||||
|
||||
if __name__ == "__main__":
|
||||
downloader = ModelDownloader()
|
||||
if not downloader.check_models_exist():
|
||||
print("Downloading AI models...")
|
||||
success = downloader.download_models()
|
||||
if success:
|
||||
print("Models downloaded successfully!")
|
||||
else:
|
||||
print("Failed to download models!")
|
||||
else:
|
||||
print("AI models already available!")
|
||||
Reference in New Issue
Block a user