Visionary Lab

Create high-quality visual content with GPT-Image-1.5, FLUX Kontext Pro, and Sora 2 on Azure AI Foundry — tailored for professional use cases.

Key Features

Video Generation (Sora 2)

• Create videos from text prompts with the Sora 2 model
• Generate videos from text + images (image-to-video)
• Audio automatically included in all generated videos
• Support for multiple resolutions: 720p and 1080p (landscape and portrait)
• Durations: 4s, 8s, or 12s

Image Generation (GPT-Image-1.5 · FLUX Kontext Pro · GPT-Image-1-Mini)

• Generate polished image assets from text prompts, input images, or both
• GPT-Image-1.5: OpenAI's latest image model with face preservation and high-quality editing
• FLUX Kontext Pro: Black Forest Labs model for fast, high-fidelity image generation
• GPT-Image-1-Mini: Lightweight model for rapid prototyping
• Refine prompts using AI best practices to ensure high-impact visuals
• Analyze outputs with AI for quality control, metadata tagging, and asset optimization
• Guardrails for content showing brand products (brand protection)

Asset Management

• Manage your content in an organized asset library with folder support
• Automatic video analysis and metadata tagging

You can also get started with our notebooks to explore the models and APIs:

• Image generation: gpt-image-1.ipynb
• Video generation: sora-api-starter.ipynb

Architecture

Visionary Lab uses Azure AI Foundry as a single unified AI resource with all model deployments, and managed identity for all service connections (no API keys).

Component	Service	Auth
AI Models	Azure AI Foundry (AIServices)	Managed Identity
Image/Video Storage	Azure Blob Storage	Managed Identity
Metadata	Azure Cosmos DB	Managed Identity
Hosting	Azure Container Apps	SystemAssigned MI

Supported Model Deployments

Deployment	Model	Purpose
gpt-4o	GPT-4o	LLM for prompt enhancement and analysis
gpt-image-1.5	GPT-Image-1.5	Primary image generation
gpt-image-1-mini	GPT-Image-1-Mini	Fast image generation
flux-kontext-pro	FLUX.1-Kontext-pro	Alternative image generation
sora-2	Sora 2	Video generation

Prerequisites

Azure resources:

• Azure AI Foundry resource with deployed models (see table above)
• Azure Storage Account with Blob Containers for images and videos
• Azure Cosmos DB account

Compute environment:

• Python 3.12+
• Node.js 19+ and npm
• Git
• uv package manager
• Azure CLI (az login required for local development)

Step 1: Installation (One-time)

Option A: Quick Start with GitHub Codespaces

Wait for the Codespace to initialize, then continue with Step 2: Configure Resources.

Option B: Local Installation

1. Clone the Repository

git clone https://github.com/Azure-Samples/visionary-lab

2. Backend Setup

2.1 Install UV Package Manager

Mac/Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows (PowerShell):

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

2.2 Copy environment file template

cp .env.example .env

3. Frontend Setup

cd frontend
npm install --legacy-peer-deps

Step 2: Configure Resources

1. Login to Azure (required for managed identity authentication):

   az login

2. Configure environment variables in .env:

   code .env

   Setting	Description
   AI_FOUNDRY_ENDPOINT	Your AI Foundry endpoint (e.g., https://your-foundry.cognitiveservices.azure.com/)
   LLM_DEPLOYMENT	LLM deployment name (e.g., gpt-4o)
   IMAGEGEN_DEPLOYMENT	Primary image model deployment (e.g., gpt-image-1.5)
   IMAGEGEN_1_MINI_DEPLOYMENT	Mini image model deployment (e.g., gpt-image-1-mini)
   FLUX_KONTEXT_DEPLOYMENT	FLUX model deployment (e.g., flux-kontext-pro)
   SORA_DEPLOYMENT	Video model deployment (e.g., sora-2)
   AZURE_BLOB_SERVICE_URL	Blob Storage URL
   AZURE_STORAGE_ACCOUNT_NAME	Storage account name
   AZURE_COSMOS_DB_ENDPOINT	Cosmos DB endpoint URL

   No API keys needed. All services authenticate via DefaultAzureCredential which uses your az login session locally and managed identity in Azure.

Note: For the best experience, deploy all models. The app works with a subset — unavailable models are gracefully skipped.

Sora 2 Specifications

Supported Resolutions:

• 1280×720 (16:9 landscape)
• 720×1280 (9:16 portrait)
• 1792×1024 (16:9 landscape, high quality)
• 1024×1792 (9:16 portrait, high quality)

Supported Durations: 4s, 8s, or 12s

Audio: All generated videos automatically include synchronized audio

Step 3: Running the Application

1. Start the backend:

   cd backend
   uv run fastapi dev

   The backend server will start on http://localhost:8000.

   Note: If you encounter: ImportError: libGL.so.1: cannot open shared object file, install:

   sudo apt update && sudo apt install libgl1-mesa-glx

2. Open a new terminal to start the frontend:

   cd frontend
   npm run build
   npm start

   The frontend will be available at http://localhost:3000.

🚀 Deploy to Azure

For production deployment, use Azure Developer CLI:

Prerequisites: Azure Developer CLI (azd)

git clone https://github.com/Azure-Samples/visionary-lab
cd visionary-lab

azd auth login
azd up

During azd up, you'll be prompted for:

• AI Foundry name: Globally unique name for your AI Foundry resource
• Model deployment names: Which models to deploy (gpt-4o, gpt-image-1.5, sora-2, etc.)

✨ That's it! Your Visionary Lab will be running on Azure Container Apps with:

• Azure AI Foundry with all model deployments
• Managed identity for all service connections (no API keys)
• Azure Storage and Cosmos DB for content management
• RBAC role assignments auto-configured
• Optional Entra ID authentication (configurable per deployment)

📖 For detailed deployment instructions, see DEPLOYMENT.md