its now jforge

.github/workflows/build.yml

@@ -1,4 +1,4 @@
-name: Build LumenForge
+name: Build JForge
 
 on:
   push:
@@ -26,15 +26,15 @@ jobs:
           - label: "Universal (CPU / macOS CoreML)"
             ort_artifact: onnxruntime
             classifier: "-universal"
-            asset_name: lumenforge-universal.jar
+            asset_name: jforge-universal.jar
 
           # ── NVIDIA GPU (CUDA + TensorRT) ───────────────────────────
           # For Windows and Linux with NVIDIA GPUs.
           # Requires CUDA toolkit + cuDNN on the host system.
           - label: "NVIDIA GPU (CUDA + TensorRT)"
             ort_artifact: onnxruntime_gpu
             classifier: "-nvidia"
-            asset_name: lumenforge-nvidia.jar
+            asset_name: jforge-nvidia.jar
 
     steps:
       - name: Checkout
@@ -55,7 +55,7 @@ jobs:
 
       - name: Rename artifact
         run: |
-          JAR=$(find target -maxdepth 1 -name "lumenforge-*${{ matrix.classifier }}.jar" | head -1)
+          JAR=$(find target -maxdepth 1 -name "jforge-*${{ matrix.classifier }}.jar" | head -1)
           cp "$JAR" "target/${{ matrix.asset_name }}"
           echo "Built: ${{ matrix.asset_name }} ($(du -h target/${{ matrix.asset_name }} | cut -f1))"
 
@@ -87,5 +87,5 @@ jobs:
         with:
           generate_release_notes: true
           files: |
-            artifacts/lumenforge-universal.jar
-            artifacts/lumenforge-nvidia.jar
+            artifacts/jforge-universal.jar
+            artifacts/jforge-nvidia.jar

.vscode/tasks.json

@@ -2,7 +2,7 @@
 	"version": "2.0.0",
 	"tasks": [
 		{
-			"label": "Build LumenForge",
+			"label": "Build JForge",
 			"type": "shell",
 			"command": "mvn",
 			"args": [
@@ -14,7 +14,7 @@
 			"group": "build"
 		},
 		{
-			"label": "Run LumenForge",
+			"label": "Run JForge",
 			"type": "shell",
 			"command": "mvn",
 			"args": [
@@ -30,7 +30,7 @@
 			}
 		},
 		{
-			"label": "Package LumenForge",
+			"label": "Package JForge",
 			"type": "shell",
 			"command": "mvn",
 			"args": [
@@ -41,7 +41,7 @@
 			"isBackground": false
 		},
 		{
-			"label": "Clean LumenForge",
+			"label": "Clean JForge",
 			"type": "shell",
 			"command": "mvn",
 			"args": [

README.md

@@ -1,10 +1,9 @@
-# LumenForge
+# JForge
 
-[![Build LumenForge](https://github.com/palaashatri/lumenforge/actions/workflows/build.yml/badge.svg)](https://github.com/palaashatri/lumenforge/actions/workflows/build.yml)
+[![Build JForge](https://github.com/palaashatri/jforge/actions/workflows/build.yml/badge.svg)](https://github.com/palaashatri/jforge/actions/workflows/build.yml)
 
 Desktop Java Swing application for ONNX Runtime inference with intelligent GPU acceleration across NVIDIA, Apple, Intel, and AMD hardware.
 
-<img width="2552" height="2026" alt="image" src="https://github.com/user-attachments/assets/8b7a8783-c795-4a2f-844c-beccc9d6855d" />
 
 ## Features
 
@@ -33,18 +32,18 @@ Desktop Java Swing application for ONNX Runtime inference with intelligent GPU a
 - **PyTorch → ONNX auto-conversion**: downloading a PyTorch model triggers automatic conversion via managed Python venv
 - Manual ONNX model import
 - Gated model support with HuggingFace token authentication
-- Local model storage in `~/.lumenforge-models`
+- Local model storage in `~/.jforge-models`
 
 ### GPU Acceleration
-Intelligent execution provider selection — LumenForge probes available EPs at runtime and picks the best one:
+Intelligent execution provider selection — JForge probes available EPs at runtime and picks the best one:
 
 | Platform | Priority (highest → lowest) |
 |---|---|
 | **macOS** | CoreML (GPU + ANE + CPU) → CPU |
 | **Windows** | TensorRT-RTX → TensorRT → CUDA → DirectML → OpenVINO → CPU |
 | **Linux** | TensorRT → CUDA → ROCm → OpenVINO → CPU |
 
-Override with `-Dlumenforge.ep=cuda` (or any EP key) to force a specific provider.
+Override with `-Djforge.ep=cuda` (or any EP key) to force a specific provider.
 
 ### UI
 - Native look-and-feel: system-native on macOS, FlatLaf with dark/light detection on Windows/Linux
@@ -54,19 +53,19 @@ Override with `-Dlumenforge.ep=cuda` (or any EP key) to force a specific provide
 
 ## Downloads
 
-Pre-built fat JARs are available from [GitHub Releases](https://github.com/palaashatri/lumenforge/releases):
+Pre-built fat JARs are available from [GitHub Releases](https://github.com/palaashatri/jforge/releases):
 
 | JAR | GPU Support | Use When |
 |---|---|---|
-| `lumenforge-universal.jar` | macOS CoreML (M-series GPU/ANE), CPU everywhere | macOS, or Windows/Linux without NVIDIA GPU |
-| `lumenforge-nvidia.jar` | CUDA + TensorRT (Windows/Linux) | Windows/Linux with NVIDIA GPU + CUDA installed |
+| `jforge-universal.jar` | macOS CoreML (M-series GPU/ANE), CPU everywhere | macOS, or Windows/Linux without NVIDIA GPU |
+| `jforge-nvidia.jar` | CUDA + TensorRT (Windows/Linux) | Windows/Linux with NVIDIA GPU + CUDA installed |
 
 > **Note**: DirectML (AMD/Intel on Windows), OpenVINO (Intel), and ROCm (AMD on Linux) are auto-detected at runtime if the native libraries are installed on the system. The universal JAR handles this automatically.
 
 ```bash
 # Run any variant
-java -jar lumenforge-universal.jar
-java -jar lumenforge-nvidia.jar
+java -jar jforge-universal.jar
+java -jar jforge-nvidia.jar
 ```
 
 ## Build from Source
@@ -112,6 +111,6 @@ See [.github/workflows/build.yml](.github/workflows/build.yml) for details.
 ## Notes
 
 - Runtime is pure Java — ONNX Runtime execution with GPU fallback. No Python bridge needed at inference time.
-- Override EP order via JVM property: `-Dlumenforge.ep=cpu|coreml|cuda|tensorrt|directml|openvino|rocm`
+- Override EP order via JVM property: `-Djforge.ep=cpu|coreml|cuda|tensorrt|directml|openvino|rocm`
 - Task tabs show preview images when output is generated, with **Open Output** to launch the file.
 - If a model requires external tensor files (e.g. `weights.pb`), import the complete ONNX bundle into the model directory.

pom.xml

@@ -3,10 +3,10 @@
          xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
     <modelVersion>4.0.0</modelVersion>
 
-    <groupId>atri.palaash.lumenforge</groupId>
-    <artifactId>lumenforge</artifactId>
+    <groupId>atri.palaash.jforge</groupId>
+    <artifactId>jforge</artifactId>
     <version>1.0.0-SNAPSHOT</version>
-    <name>LumenForge</name>
+    <name>JForge</name>
 
     <properties>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
@@ -59,7 +59,7 @@
                 <configuration>
                     <archive>
                         <manifest>
-                            <mainClass>atri.palaash.lumenforge.app.LumenForgeApp</mainClass>
+                            <mainClass>atri.palaash.jforge.app.JForgeApp</mainClass>
                         </manifest>
                     </archive>
                 </configuration>
@@ -77,7 +77,7 @@
                             <finalName>${project.artifactId}-${project.version}${jar.classifier}</finalName>
                             <transformers>
                                 <transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
-                                    <mainClass>atri.palaash.lumenforge.app.LumenForgeApp</mainClass>
+                                    <mainClass>atri.palaash.jforge.app.JForgeApp</mainClass>
                                 </transformer>
                                 <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
                             </transformers>
@@ -100,7 +100,7 @@
                 <artifactId>exec-maven-plugin</artifactId>
                 <version>3.5.0</version>
                 <configuration>
-                    <mainClass>atri.palaash.lumenforge.app.LumenForgeApp</mainClass>
+                    <mainClass>atri.palaash.jforge.app.JForgeApp</mainClass>
                 </configuration>
             </plugin>
             <plugin>

scripts/convert_pytorch_to_onnx.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-PyTorch → ONNX converter for LumenForge.
+PyTorch → ONNX converter for JForge.
 
 Converts HuggingFace diffusers pipelines (Stable Diffusion, SDXL, etc.)
 or generic PyTorch models (.pt / .pth) to ONNX format for inference
@@ -9,7 +9,7 @@
 Usage — diffusers pipeline (recommended for SD models):
     python convert_pytorch_to_onnx.py \
         --model_id runwayml/stable-diffusion-v1-5 \
-        --output_dir ~/.lumenforge-models/text-image/converted/sd-v15
+        --output_dir ~/.jforge-models/text-image/converted/sd-v15
 
 Usage — generic PyTorch model (per Microsoft tutorial):
     python convert_pytorch_to_onnx.py \
@@ -193,7 +193,7 @@ def convert_generic(model_path, output_dir, input_shape_str):
 
 def main():
     ap = argparse.ArgumentParser(
-        description="LumenForge PyTorch → ONNX converter"
+        description="JForge PyTorch → ONNX converter"
     )
     ap.add_argument(
         "--model_id", required=True,

scripts/export_torchscript.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-Export Stable Diffusion v1.5 components to TorchScript format for LumenForge's
+Export Stable Diffusion v1.5 components to TorchScript format for JForge's
 DJL/PyTorch backend.
 
 Usage:
@@ -9,7 +9,7 @@
 
 Defaults:
     --model_id   stabilityai/stable-diffusion-2-1  (or runwayml/stable-diffusion-v1-5)
-    --output_dir ~/.lumenforge-models/text-image/sd-pytorch
+    --output_dir ~/.jforge-models/text-image/sd-pytorch
 
 Produces:
     clip_model.pt          — traced CLIP text encoder
@@ -28,15 +28,15 @@
 
 
 def main():
-    parser = argparse.ArgumentParser(description="Export SD to TorchScript for LumenForge")
+    parser = argparse.ArgumentParser(description="Export SD to TorchScript for JForge")
     parser.add_argument(
         "--model_id",
         default="runwayml/stable-diffusion-v1-5",
         help="HuggingFace model ID (default: runwayml/stable-diffusion-v1-5)"
     )
     parser.add_argument(
         "--output_dir",
-        default=os.path.expanduser("~/.lumenforge-models/text-image/sd-pytorch"),
+        default=os.path.expanduser("~/.jforge-models/text-image/sd-pytorch"),
         help="Output directory for TorchScript files"
     )
     parser.add_argument(
@@ -146,7 +146,7 @@ def main():
     print(f"   Output directory: {output}")
     print()
     print("Next steps:")
-    print("  1. Build LumenForge with DJL:  mvn compile -Ddjl=true")
+    print("  1. Build JForge with DJL:  mvn compile -Ddjl=true")
     print("  2. Run:                        mvn exec:java -Ddjl=true")
     print('  3. Select "SD v1.5 PyTorch (DJL)" from the model dropdown')
     print()

src/main/java/atri/palaash/lumenforge/app/LumenForgeApp.java → src/main/java/atri/palaash/jforge/app/JForgeApp.java

@@ -1,13 +1,13 @@
-package atri.palaash.lumenforge.app;
+package atri.palaash.jforge.app;
 
-import atri.palaash.lumenforge.inference.InferenceService;
-import atri.palaash.lumenforge.inference.ServiceFactory;
-import atri.palaash.lumenforge.model.ModelRegistry;
-import atri.palaash.lumenforge.model.TaskType;
-import atri.palaash.lumenforge.storage.ModelDownloader;
-import atri.palaash.lumenforge.storage.ModelStorage;
-import atri.palaash.lumenforge.ui.MainFrame;
-import atri.palaash.lumenforge.ui.NativeLookAndFeel;
+import atri.palaash.jforge.inference.InferenceService;
+import atri.palaash.jforge.inference.ServiceFactory;
+import atri.palaash.jforge.model.ModelRegistry;
+import atri.palaash.jforge.model.TaskType;
+import atri.palaash.jforge.storage.ModelDownloader;
+import atri.palaash.jforge.storage.ModelStorage;
+import atri.palaash.jforge.ui.MainFrame;
+import atri.palaash.jforge.ui.NativeLookAndFeel;
 
 import javax.swing.SwingUtilities;
 import java.net.http.HttpClient;
@@ -17,14 +17,14 @@
 import java.util.concurrent.ExecutorService;
 import java.util.concurrent.Executors;
 
-public class LumenForgeApp {
+public class JForgeApp {
 
     public static void main(String[] args) {
         configureDesktopIntegration();
 
-        System.out.println("[LumenForge] Starting \u2014 Java " + Runtime.version()
+        System.out.println("[JForge] Starting \u2014 Java " + Runtime.version()
                 + ", " + System.getProperty("os.name") + " " + System.getProperty("os.arch"));
-        System.out.println("[LumenForge] Max heap: " + (Runtime.getRuntime().maxMemory() / (1024 * 1024)) + " MB"
+        System.out.println("[JForge] Max heap: " + (Runtime.getRuntime().maxMemory() / (1024 * 1024)) + " MB"
                 + ", CPUs: " + Runtime.getRuntime().availableProcessors());
 
         ExecutorService workerPool = Executors.newVirtualThreadPerTaskExecutor();
@@ -54,7 +54,7 @@ private static void configureDesktopIntegration() {
         String osName = System.getProperty("os.name", "").toLowerCase();
         if (osName.contains("mac")) {
             System.setProperty("apple.laf.useScreenMenuBar", "true");
-            System.setProperty("apple.awt.application.name", "LumenForge");
+            System.setProperty("apple.awt.application.name", "JForge");
             System.setProperty("apple.awt.application.appearance", "system");
             // Enable FlatLaf embedded title bar on macOS for a unified toolbar look
             System.setProperty("flatlaf.menuBarEmbedded", "true");