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@@ -1,3 +1,26 @@
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## Version 4.0.1
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**Release date:** 27 July 2025
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### Model Cleanup and Optimization
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#### 1.1 **SuperResolution-10 Model Removal**
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- **Model Deprecation**: Removed the SuperResolution-10 model from the application due to performance and compatibility issues.
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- **Code Cleanup**: Eliminated the dedicated `AI_super_resolution` class and all related processing pipelines.
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- **UI Updates**: Removed SuperResolution-10 from model selection dropdown and information dialogs.
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- **Memory Optimization**: Cleaned up VRAM usage configurations by removing SuperResolution-10 entries (0.8 GB allocation).
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- **Streamlined Processing**: Simplified the upscaling orchestrator by removing SuperResolution-specific routing logic.
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- **Model List Cleanup**: Removed `SuperResolution_models_list` from the main AI models collection.
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#### 1.2 **Performance Improvements**
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- **Reduced Memory Footprint**: Application now uses less memory without the SuperResolution-10 model overhead.
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- **Simplified Code Paths**: Cleaner processing logic with fewer conditional branches for model selection.
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- **Enhanced Stability**: Removed potential failure points associated with the deprecated model.
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---
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## Version 4.0
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**Release date:** 18 July 2025
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@@ -140,8 +163,6 @@
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- The application now explicitly handles images with an alpha channel (4-channel BGRA) when using face restoration models. A new import for `COLOR_BGRA2BGR` was added, and it is used within `preprocess_face_image` to convert images to the 3-channel BGR format expected by the GFPGAN model. This prevents runtime errors and ensures correct processing of PNGs or other images with transparency.
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---
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## Version 2.2
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**Release date:** 7 July 2025
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@@ -219,8 +240,6 @@
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- Core methods in AI classes now include checks for `None` inputs and feature default fallbacks (`case _:`) in `match` statements to prevent unexpected errors with unsupported data.
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---
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## Version 2.1
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**Release date:** 23 June 2025
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@@ -2,13 +2,13 @@
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<div align="center">
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# Warlock-Studio
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# 🎭 Warlock-Studio
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### _AI Media Enhancement Suite_
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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/tag/4.0.1)
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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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@@ -19,11 +19,13 @@ _Transform your media with cutting-edge AI technology_
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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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Version 4.0.1 continues this evolution with enhanced AI architecture and improved code stability for even better performance and reliability. Note: The SuperResolution-10 model has been removed in this version for better performance optimization.
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---
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### ► Download Installer (v4.0)
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### ► Download Installer (v4.0.1) - Now Lightweight
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🚀 **NEW**: Installer size reduced from **1.4GB to ~450MB**! AI models (400MB) 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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@@ -35,7 +37,7 @@ 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/4.0/Warlock-Studio4.0Setup.zip">
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<a href="https://github.com/Ivan-Ayub97/Warlock-Studio/releases/download/4.0.1/Warlock-Studio4.0.1Setup.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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@@ -47,13 +49,13 @@ Get the latest stable release from any of the following platforms:
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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**, **RIFE**, and **SuperResolution-10** for denoising, resolution enhancement, detail restoration, extreme upscaling, and smooth frame interpolation.
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A comprehensive suite including Real-ESRGAN, BSRGAN, IRCNN, **GFPGAN**, and **RIFE** for denoising, resolution enhancement, detail restoration, upscaling, and smooth frame interpolation.
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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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- **High-Quality Upscaling Models**
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Real-ESRGAN and BSRGAN models provide excellent upscaling capabilities for various image types, from anime to photorealistic content.
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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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@@ -72,14 +74,14 @@ Get the latest stable release from any of the following platforms:
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---
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## What's New in Version 4.0
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## What's New in Version 4.0.1
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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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- 🔧 **Model Optimization:** Removed SuperResolution-10 model to improve overall performance and reduce complexity. For extreme upscaling needs, we recommend using Real-ESRGAN or BSRGAN models which provide excellent results.
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- ✅ **Enhanced AI Architecture:** Implemented 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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- ✅ **Streamlined Model Integration:** Optimized model integration in the UI and processing pipeline for better performance.
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- 🚀 **Smart Model Distribution:** Lightweight installer (~450MB) 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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@@ -90,8 +92,8 @@ 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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- **Installer Size:** Reduced from 1.4GB to ~450MB (68% size reduction)
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- **First Launch:** AI models (~400MB) 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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@@ -104,7 +106,7 @@ Version 4.0 introduces a revolutionary approach to AI model distribution:
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## Interface Previews
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### 🔹 Main View (v4.0)
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### 🔹 Main View (v4.0.1)
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@@ -168,12 +170,12 @@ Warlock-Studio uses [PyInstaller](https://www.pyinstaller.org/) and [Inno Setup]
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---
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## Development Status — v4.0-07.25
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## Development Status — v4.0.1-07.25
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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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| **SuperResolution-10 Model** | 🟢 **New** | Extreme 10x upscaling for very low-resolution images with robust error handling. |
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| **Optimized Model Suite** | 🟢 **Enhanced** | Streamlined AI models for optimal performance and reliability. |
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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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@@ -202,7 +204,6 @@ Warlock-Studio/
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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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└──super-resolution-10.onnx
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├──Assets/
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│
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└──├──banner.png
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@@ -248,37 +249,31 @@ Warlock-Studio/
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├──Warlock-Studio.py # Main
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└──Warlock-Studio.spec
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```
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## AI models Workflow
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---
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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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| 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) |
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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) |
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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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---
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@@ -1,9 +1,9 @@
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; ===================================================================
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; Warlock-Studio 4.0 - Inno Setup Script with Online Download
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; Warlock-Studio 4.0.1 - Inno Setup Script with Online Download
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; ===================================================================
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#define AppName "Warlock-Studio"
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#define AppVersion "4.0"
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#define AppVersion "4.0.1"
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#define AppPublisher "Iván Eduardo Chavez Ayub"
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#define AppURL "https://github.com/Ivan-Ayub97/Warlock-Studio"
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#define AppExeName "Warlock-Studio.exe"
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+108
-134
@@ -107,8 +107,24 @@ def find_by_relative_path(relative_path: str) -> str:
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return os_path_join(base_path, relative_path)
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def image_read(path: str) -> numpy_ndarray:
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"""Read an image file and return it as a numpy array."""
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try:
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if not os_path_exists(path):
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raise FileNotFoundError(f"Image file not found: {path}")
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image = opencv_imdecode(numpy_frombuffer(open(path, 'rb').read(), dtype=uint8), IMREAD_UNCHANGED)
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if image is None:
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raise ValueError(f"Failed to read image: {path}")
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return image
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except Exception as e:
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error_msg = f"Error reading image {path}: {str(e)}"
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log_and_report_error(error_msg)
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raise RuntimeError(error_msg)
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app_name = "Warlock-Studio"
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version = "4.0-07.25"
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version = "4.0.1-07.25"
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# AI Model Base Class
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@@ -128,75 +144,23 @@ class AI_model_base:
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f"Model file not found: {self.model_path}")
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# Set up providers for GPU acceleration
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providers = ['DmlExecutionProvider', 'CPUExecutionProvider']
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# Use available GPU or CPU if no compatible GPU found
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providers = ['CUDAExecutionProvider', 'DmlExecutionProvider', 'CPUExecutionProvider']
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self.inferenceSession = InferenceSession(
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self.model_path,
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providers=providers
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)
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print(
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f"[AI] Successfully loaded model: {os_path_basename(self.model_path)}")
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except FileNotFoundError as fnf_error:
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print(f"[AI ERROR] Model file not found: {fnf_error}")
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# Handle specific file not found error
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except Exception as e:
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print(
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f"[AI ERROR] Failed to load model {self.model_path}: {str(e)}")
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print(f"[AI ERROR] Failed to load model due to unexpected error: {str(e)}")
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# Log the error and avoid crashing the application
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self.inferenceSession = None # Reset inference session on error
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raise
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# AI Super Resolution Implementation
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class AI_super_resolution(AI_model_base):
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def __init__(self, model_path: str, device: str = "CPU"):
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super().__init__(model_path, device)
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self.model_name = "SuperResolution-10"
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self.upscale_factor = 10 # Based on the model name
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def preprocess_super_resolution_image(self, image: numpy_ndarray) -> numpy_ndarray:
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"""
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Preprocess image for super resolution model input.
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"""
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# Convert image to float32 and normalize
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image = (image.astype(float32) / 255.0)
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# Convert image to CHW format (channels, height, width)
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image = numpy_transpose(image, (2, 0, 1))
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# Add batch dimension
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image = numpy_expand_dims(image, axis=0)
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return image
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def postprocess_super_resolution_image(self, output: numpy_ndarray) -> numpy_ndarray:
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"""
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Postprocess model output to an image.
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"""
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# Remove batch dimension and convert back to HWC format
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output = numpy_squeeze(output, axis=0)
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output = numpy_transpose(output, (1, 2, 0))
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# Clip values and convert to uint8
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output = numpy_clip(output * 255.0, 0, 255).astype(uint8)
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return output
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def enhance_image(self, image: numpy_ndarray) -> numpy_ndarray:
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"""
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Enhance image using the super resolution model.
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"""
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input_image = self.preprocess_super_resolution_image(image)
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input_name = self.inferenceSession.get_inputs()[0].name
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output_name = self.inferenceSession.get_outputs()[0].name
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result = self.inferenceSession.run(
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[output_name], {input_name: input_image})[0]
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enhanced_image = self.postprocess_super_resolution_image(result)
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return enhanced_image
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def AI_orchestration(self, image: numpy_ndarray) -> numpy_ndarray:
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"""
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Main orchestration function for super resolution processing.
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"""
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try:
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return self.enhance_image(image)
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except Exception as e:
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print(f"[SUPER RESOLUTION ERROR] {str(e)}")
|
||||
# Return original image if enhancement fails
|
||||
return image
|
||||
|
||||
|
||||
# Esquema de colores mejorado - Rojo, Gris, Amarillo, Negro, Blanco
|
||||
@@ -223,7 +187,6 @@ VRAM_model_usage = {
|
||||
'IRCNN_Mx1': 4,
|
||||
'IRCNN_Lx1': 4,
|
||||
'GFPGAN': 1.8,
|
||||
'SuperResolution-10': 0.8,
|
||||
}
|
||||
|
||||
MENU_LIST_SEPARATOR = ["----"]
|
||||
@@ -232,11 +195,10 @@ 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 + SuperResolution_models_list + MENU_LIST_SEPARATOR + RIFE_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"
|
||||
@@ -277,32 +239,49 @@ else:
|
||||
|
||||
if os_path_exists(USER_PREFERENCE_PATH):
|
||||
print(f"[{app_name}] Preference file exist")
|
||||
with open(USER_PREFERENCE_PATH, "r") as json_file:
|
||||
json_data = json_load(json_file)
|
||||
default_AI_model = json_data.get(
|
||||
"default_AI_model", AI_models_list[0])
|
||||
default_AI_multithreading = json_data.get(
|
||||
"default_AI_multithreading", AI_multithreading_list[0])
|
||||
default_gpu = json_data.get(
|
||||
"default_gpu", gpus_list[0])
|
||||
default_keep_frames = json_data.get(
|
||||
"default_keep_frames", keep_frames_list[1])
|
||||
default_image_extension = json_data.get(
|
||||
"default_image_extension", image_extension_list[0])
|
||||
default_video_extension = json_data.get(
|
||||
"default_video_extension", video_extension_list[0])
|
||||
default_video_codec = json_data.get(
|
||||
"default_video_codec", video_codec_list[0])
|
||||
default_blending = json_data.get(
|
||||
"default_blending", blending_list[1])
|
||||
default_output_path = json_data.get(
|
||||
"default_output_path", OUTPUT_PATH_CODED)
|
||||
default_input_resize_factor = json_data.get(
|
||||
"default_input_resize_factor", str(50))
|
||||
default_output_resize_factor = json_data.get(
|
||||
"default_output_resize_factor", str(100))
|
||||
default_VRAM_limiter = json_data.get(
|
||||
"default_VRAM_limiter", str(4))
|
||||
try:
|
||||
with open(USER_PREFERENCE_PATH, "r") as json_file:
|
||||
json_data = json_load(json_file)
|
||||
default_AI_model = json_data.get(
|
||||
"default_AI_model", AI_models_list[0])
|
||||
default_AI_multithreading = json_data.get(
|
||||
"default_AI_multithreading", AI_multithreading_list[0])
|
||||
default_gpu = json_data.get(
|
||||
"default_gpu", gpus_list[0])
|
||||
default_keep_frames = json_data.get(
|
||||
"default_keep_frames", keep_frames_list[1])
|
||||
default_image_extension = json_data.get(
|
||||
"default_image_extension", image_extension_list[0])
|
||||
default_video_extension = json_data.get(
|
||||
"default_video_extension", video_extension_list[0])
|
||||
default_video_codec = json_data.get(
|
||||
"default_video_codec", video_codec_list[0])
|
||||
default_blending = json_data.get(
|
||||
"default_blending", blending_list[1])
|
||||
default_output_path = json_data.get(
|
||||
"default_output_path", OUTPUT_PATH_CODED)
|
||||
default_input_resize_factor = json_data.get(
|
||||
"default_input_resize_factor", str(50))
|
||||
default_output_resize_factor = json_data.get(
|
||||
"default_output_resize_factor", str(100))
|
||||
default_VRAM_limiter = json_data.get(
|
||||
"default_VRAM_limiter", str(4))
|
||||
except (json.JSONDecodeError, FileNotFoundError, PermissionError) as e:
|
||||
print(f"[{app_name} ERROR] Failed to load preferences file: {str(e)}")
|
||||
print(f"[{app_name}] Using default coded values instead")
|
||||
# Fall back to default values
|
||||
default_AI_model = AI_models_list[0]
|
||||
default_AI_multithreading = AI_multithreading_list[0]
|
||||
default_gpu = gpus_list[0]
|
||||
default_keep_frames = keep_frames_list[1]
|
||||
default_image_extension = image_extension_list[0]
|
||||
default_video_extension = video_extension_list[0]
|
||||
default_video_codec = video_codec_list[0]
|
||||
default_blending = blending_list[1]
|
||||
default_output_path = OUTPUT_PATH_CODED
|
||||
default_input_resize_factor = str(50)
|
||||
default_output_resize_factor = str(100)
|
||||
default_VRAM_limiter = str(4)
|
||||
|
||||
else:
|
||||
print(f"[{app_name}] Preference file does not exist, using default coded value")
|
||||
@@ -386,6 +365,9 @@ class AI_upscale:
|
||||
return 4
|
||||
|
||||
def _load_inferenceSession(self) -> None:
|
||||
if self.inferenceSession is not None:
|
||||
print(f"[AI] Model {self.AI_model_name} is already loaded.")
|
||||
return
|
||||
try:
|
||||
# Check if model file exists
|
||||
if not os_path_exists(self.AI_model_path):
|
||||
@@ -400,6 +382,7 @@ class AI_upscale:
|
||||
case 'GPU 2': provider_options = [{"device_id": "1"}]
|
||||
case 'GPU 3': provider_options = [{"device_id": "2"}]
|
||||
case 'GPU 4': provider_options = [{"device_id": "3"}]
|
||||
case _: provider_options = [{"device_id": "0"}] # Default case
|
||||
|
||||
inference_session = InferenceSession(
|
||||
path_or_bytes=self.AI_model_path,
|
||||
@@ -411,10 +394,13 @@ class AI_upscale:
|
||||
print(
|
||||
f"[AI] Successfully loaded model: {os_path_basename(self.AI_model_path)}")
|
||||
|
||||
except FileNotFoundError as fnf_error:
|
||||
print(f"[AI ERROR] AI model file not found: {fnf_error}")
|
||||
# Graceful handling of file not found
|
||||
except Exception as e:
|
||||
error_msg = f"Failed to load AI model {os_path_basename(self.AI_model_path)}: {str(e)}"
|
||||
print(f"[AI ERROR] {error_msg}")
|
||||
raise RuntimeError(error_msg)
|
||||
print(f"[AI ERROR] Unexpected error loading AI model: {str(e)}")
|
||||
# Reset inference session to None upon error
|
||||
self.inferenceSession = None
|
||||
|
||||
# INTERNAL CLASS FUNCTIONS
|
||||
|
||||
@@ -450,8 +436,9 @@ class AI_upscale:
|
||||
|
||||
old_height, old_width = self.get_image_resolution(image)
|
||||
|
||||
new_width = int(old_width * self.input_resize_factor)
|
||||
new_height = int(old_height * self.input_resize_factor)
|
||||
scale = self.input_resize_factor / 100.0
|
||||
new_width = int(old_width * scale)
|
||||
new_height = int(old_height * scale)
|
||||
|
||||
new_width = new_width if new_width % 2 == 0 else new_width + 1
|
||||
new_height = new_height if new_height % 2 == 0 else new_height + 1
|
||||
@@ -467,15 +454,16 @@ class AI_upscale:
|
||||
|
||||
old_height, old_width = self.get_image_resolution(image)
|
||||
|
||||
new_width = int(old_width * self.output_resize_factor)
|
||||
new_height = int(old_height * self.output_resize_factor)
|
||||
scale = self.output_resize_factor / 100.0
|
||||
new_width = int(old_width * scale)
|
||||
new_height = int(old_height * scale)
|
||||
|
||||
new_width = new_width if new_width % 2 == 0 else new_width + 1
|
||||
new_height = new_height if new_height % 2 == 0 else new_height + 1
|
||||
|
||||
if self.output_resize_factor > 1:
|
||||
if scale > 1.0:
|
||||
return opencv_resize(image, (new_width, new_height), interpolation=INTER_CUBIC)
|
||||
elif self.output_resize_factor < 1:
|
||||
elif scale < 1.0:
|
||||
return opencv_resize(image, (new_width, new_height), interpolation=INTER_AREA)
|
||||
else:
|
||||
return image
|
||||
@@ -621,7 +609,7 @@ class AI_upscale:
|
||||
# Default fallback to 255
|
||||
case _: return (onnx_output * 255).astype(uint8)
|
||||
|
||||
def AI_upscale(self, image: numpy_ndarray) -> numpy_ndarray:
|
||||
def run_upscaling(self, image: numpy_ndarray) -> numpy_ndarray:
|
||||
# Optimización: Usar memoria contigua antes de procesar
|
||||
image = numpy_ascontiguousarray(image, dtype=float32)
|
||||
image_mode = self.get_image_mode(image)
|
||||
@@ -682,7 +670,7 @@ class AI_upscale:
|
||||
t_height, t_width = self.calculate_target_resolution(image)
|
||||
tiles_x, tiles_y = self.calculate_tiles_number(image)
|
||||
tiles_list = self.split_image_into_tiles(image, tiles_x, tiles_y)
|
||||
tiles_list = [self.AI_upscale(tile) for tile in tiles_list]
|
||||
tiles_list = [self.run_upscaling(tile) for tile in tiles_list]
|
||||
|
||||
return self.combine_tiles_into_image(image, tiles_list, t_height, t_width, tiles_x)
|
||||
|
||||
@@ -698,7 +686,7 @@ class AI_upscale:
|
||||
if self.image_need_tilling(resized_image):
|
||||
upscaled_image = self.AI_upscale_with_tilling(resized_image)
|
||||
else:
|
||||
upscaled_image = self.AI_upscale(resized_image)
|
||||
upscaled_image = self.run_upscaling(resized_image)
|
||||
|
||||
return self.resize_with_output_factor(upscaled_image)
|
||||
|
||||
@@ -788,15 +776,16 @@ class AI_interpolation:
|
||||
|
||||
old_height, old_width = self.get_image_resolution(image)
|
||||
|
||||
new_width = int(old_width * self.input_resize_factor)
|
||||
new_height = int(old_height * self.input_resize_factor)
|
||||
scale = self.input_resize_factor / 100.0
|
||||
new_width = int(old_width * scale)
|
||||
new_height = int(old_height * scale)
|
||||
|
||||
new_width = new_width if new_width % 2 == 0 else new_width + 1
|
||||
new_height = new_height if new_height % 2 == 0 else new_height + 1
|
||||
|
||||
if self.input_resize_factor > 1:
|
||||
if scale > 1:
|
||||
return opencv_resize(image, (new_width, new_height), interpolation=INTER_CUBIC)
|
||||
elif self.input_resize_factor < 1:
|
||||
elif scale < 1:
|
||||
return opencv_resize(image, (new_width, new_height), interpolation=INTER_AREA)
|
||||
else:
|
||||
return image
|
||||
@@ -805,15 +794,16 @@ class AI_interpolation:
|
||||
|
||||
old_height, old_width = self.get_image_resolution(image)
|
||||
|
||||
new_width = int(old_width * self.output_resize_factor)
|
||||
new_height = int(old_height * self.output_resize_factor)
|
||||
scale = self.output_resize_factor / 100.0
|
||||
new_width = int(old_width * scale)
|
||||
new_height = int(old_height * scale)
|
||||
|
||||
new_width = new_width if new_width % 2 == 0 else new_width + 1
|
||||
new_height = new_height if new_height % 2 == 0 else new_height + 1
|
||||
|
||||
if self.output_resize_factor > 1:
|
||||
if scale > 1:
|
||||
return opencv_resize(image, (new_width, new_height), interpolation=INTER_CUBIC)
|
||||
elif self.output_resize_factor < 1:
|
||||
elif scale < 1:
|
||||
return opencv_resize(image, (new_width, new_height), interpolation=INTER_AREA)
|
||||
else:
|
||||
return image
|
||||
@@ -1006,15 +996,16 @@ class AI_face_restoration:
|
||||
|
||||
def resize_with_input_factor(self, image: numpy_ndarray) -> numpy_ndarray:
|
||||
old_height, old_width = self.get_image_resolution(image)
|
||||
new_width = int(old_width * self.input_resize_factor)
|
||||
new_height = int(old_height * self.input_resize_factor)
|
||||
scale = self.input_resize_factor / 100.0
|
||||
new_width = int(old_width * scale)
|
||||
new_height = int(old_height * scale)
|
||||
|
||||
new_width = new_width if new_width % 2 == 0 else new_width + 1
|
||||
new_height = new_height if new_height % 2 == 0 else new_height + 1
|
||||
|
||||
if self.input_resize_factor > 1:
|
||||
if scale > 1:
|
||||
return opencv_resize(image, (new_width, new_height), interpolation=INTER_CUBIC)
|
||||
elif self.input_resize_factor < 1:
|
||||
elif scale < 1:
|
||||
return opencv_resize(image, (new_width, new_height), interpolation=INTER_AREA)
|
||||
else:
|
||||
return image
|
||||
@@ -1868,8 +1859,8 @@ def cleanup_on_exit():
|
||||
for temp_file in temp_files:
|
||||
try:
|
||||
os_remove(temp_file)
|
||||
except Exception:
|
||||
pass
|
||||
except Exception as e:
|
||||
print(f"[ERROR] Could not remove temporary file {temp_file}: {str(e)}")
|
||||
|
||||
# Stop any running processes
|
||||
stop_upscale_process()
|
||||
@@ -3602,13 +3593,6 @@ 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,
|
||||
@@ -3691,11 +3675,7 @@ def upscale_image(
|
||||
write_process_status(
|
||||
process_status_q, f"{file_number}. Enchanting your image. Be patient...")
|
||||
|
||||
# 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)
|
||||
upscaled_image = AI_instance.AI_orchestration(starting_image)
|
||||
|
||||
if selected_blending_factor > 0:
|
||||
blend_images_and_save(
|
||||
@@ -4435,12 +4415,6 @@ def place_AI_menu():
|
||||
" • 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(
|
||||
|
||||
Reference in New Issue
Block a user