modly-image-to-3d

modly/
├── src/                    # Electron + TypeScript frontend
│   ├── main/               # Electron main process
│   ├── renderer/           # React UI (renderer process)
│   └── preload/            # IPC bridge
├── api/                    # Python FastAPI backend
│   ├── generator.py        # Core generation logic
│   └── requirements.txt
├── resources/
│   └── icons/
├── launcher.bat            # Windows quick-start
├── launcher.sh             # Linux quick-start
└── package.json

# Windows
launcher.bat
 
# Linux
chmod +x launcher.sh
./launcher.sh

# 1. Clone
git clone https://github.com/lightningpixel/modly
cd modly
 
# 2. Install JS dependencies
npm install
 
# 3. Set up Python backend
cd api
python -m venv .venv
 
# Activate (Windows)
.venv\Scripts\activate
 
# Activate (Linux/macOS)
source .venv/bin/activate
 
pip install -r requirements.txt
cd ..
 
# 4. Run dev mode (starts Electron + Python backend)
npm run dev

# Build installers for current platform
npm run build
 
# Output goes to dist/

npm run dev        # Start app in development mode (hot reload)
npm run build      # Package app for distribution
npm run lint       # Run ESLint
npm run typecheck  # TypeScript type checking

{
  "name": "My 3D Extension",
  "id": "my-extension-id",
  "description": "Generates 3D models using XYZ model",
  "version": "1.0.0",
  "author": "Your Name",
  "repository": "https://github.com/yourname/my-modly-extension",
  "variants": [
    {
      "id": "model-small",
      "name": "Small (faster)",
      "description": "Lighter variant for faster generation",
      "size_gb": 4.2,
      "vram_gb": 6,
      "files": [
        {
          "url": "https://huggingface.co/yourorg/yourmodel/resolve/main/weights.safetensors",
          "filename": "weights.safetensors",
          "sha256": "abc123..."
        }
      ]
    }
  ]
}

# api/extensions/<extension-id>/generator.py
# Required interface every extension must implement
 
import sys
import json
from pathlib import Path
 
def generate(
    image_path: str,
    output_path: str,
    variant_id: str,
    models_dir: str,
    **kwargs
) -> dict:
    """
    Required entry point for all Modly extensions.
    
    Args:
        image_path:  Path to input image file
        output_path: Path where output .glb/.obj should be saved
        variant_id:  Which model variant to use
        models_dir:  Directory where downloaded model weights live
    
    Returns:
        dict with keys:
            success (bool)
            output_file (str) — path to generated mesh
            error (str, optional)
    """
    try:
        # Load your model weights
        weights = Path(models_dir) / variant_id / "weights.safetensors"
        
        # Run your inference
        mesh = run_inference(str(weights), image_path)
        
        # Save output
        mesh.export(output_path)
        
        return {
            "success": True,
            "output_file": output_path
        }
    except Exception as e:
        return {
            "success": False,
            "error": str(e)
        }

# Typical backend route patterns (api/main.py or similar)
 
# GET /extensions         — list installed extensions
# GET /extensions/{id}    — get extension details + variants
# POST /extensions/install — install extension from GitHub URL
# POST /generate          — trigger 3D generation
# GET /generate/status    — poll generation progress
# GET /models             — list downloaded model variants
# POST /models/download   — download a model variant

// src/preload/index.ts — exposing backend calls to renderer
import { contextBridge, ipcRenderer } from 'electron'
 
contextBridge.exposeInMainWorld('modly', {
  generate: (imagePath: string, extensionId: string, variantId: string) =>
    ipcRenderer.invoke('generate', { imagePath, extensionId, variantId }),
 
  installExtension: (repoUrl: string) =>
    ipcRenderer.invoke('install-extension', { repoUrl }),
 
  listExtensions: () =>
    ipcRenderer.invoke('list-extensions'),
})

// src/main/ipc-handlers.ts — main process handling
import { ipcMain } from 'electron'
 
ipcMain.handle('generate', async (_event, { imagePath, extensionId, variantId }) => {
  const response = await fetch('http://localhost:PORT/generate', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ image_path: imagePath, extension_id: extensionId, variant_id: variantId }),
  })
  return response.json()
})

// src/renderer/components/GenerateButton.tsx — UI usage
declare global {
  interface Window {
    modly: {
      generate: (imagePath: string, extensionId: string, variantId: string) => Promise<{ success: boolean; output_file?: string; error?: string }>
      installExtension: (repoUrl: string) => Promise<{ success: boolean }>
      listExtensions: () => Promise<Extension[]>
    }
  }
}
 
async function handleGenerate(imagePath: string) {
  const result = await window.modly.generate(
    imagePath,
    'modly-hunyuan3d-mini-extension',
    'hunyuan3d-mini-turbo'
  )
 
  if (result.success) {
    console.log('Mesh saved to:', result.output_file)
  } else {
    console.error('Generation failed:', result.error)
  }
}

my-modly-extension/
├── manifest.json
└── generator.py

# generator.py
import torch
from PIL import Image
from pathlib import Path
 
def generate(image_path, output_path, variant_id, models_dir, **kwargs):
    device = "cuda" if torch.cuda.is_available() else "cpu"
    weights_dir = Path(models_dir) / variant_id
 
    try:
        # Load model (example pattern)
        from your_model_lib import ImageTo3DPipeline
        
        pipe = ImageTo3DPipeline.from_pretrained(
            str(weights_dir),
            torch_dtype=torch.float16
        ).to(device)
 
        image = Image.open(image_path).convert("RGB")
        
        with torch.no_grad():
            mesh = pipe(image).mesh
 
        mesh.export(output_path)
 
        return {"success": True, "output_file": output_path}
 
    except Exception as e:
        return {"success": False, "error": str(e)}

package.json          # Electron app metadata, build targets
api/requirements.txt  # Python dependencies for backend

import torch
 
def get_device():
    if torch.cuda.is_available():
        print(f"Using GPU: {torch.cuda.get_device_name(0)}")
        return "cuda"
    print("No GPU found, falling back to CPU (slow)")
    return "cpu"

import sys
import json
 
def report_progress(percent: int, message: str):
    """Write progress to stdout so Modly can display it."""
    print(json.dumps({"progress": percent, "message": message}), flush=True)
 
def generate(image_path, output_path, variant_id, models_dir, **kwargs):
    report_progress(0, "Loading model...")
    # ... load model ...
    report_progress(30, "Processing image...")
    # ... inference ...
    report_progress(90, "Exporting mesh...")
    # ... export ...
    report_progress(100, "Done")
    return {"success": True, "output_file": output_path}

// src/renderer/pages/MyPage.tsx
import React, { useEffect, useState } from 'react'
 
interface Extension {
  id: string
  name: string
  description: string
}
 
export default function MyPage() {
  const [extensions, setExtensions] = useState<Extension[]>([])
 
  useEffect(() => {
    window.modly.listExtensions().then(setExtensions)
  }, [])
 
  return (
    <div>
      <h1>Installed Extensions</h1>
      {extensions.map(ext => (
        <div key={ext.id}>
          <h2>{ext.name}</h2>
          <p>{ext.description}</p>
        </div>
      ))}
    </div>
  )
}

cd api
source .venv/bin/activate   # or .venv\Scripts\activate on Windows
python -c "import torch; print(torch.cuda.is_available(), torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'no GPU')"