Multi-Step GenAI Workflow for Architectural Design on Google Cloud
Google Cloud AI Studio is a cloud-native Streamlit application demonstrating advanced GenAI workflow orchestration using Google Vertex AI. It implements a multi-step generation pipeline that transforms simple text descriptions into photorealistic architectural renders through three stages: text enhancement, sketch generation, and photorealistic rendering.
The system uses Gemini 2.0 Flash Lite for text enhancement and Gemini 2.5 Flash Image for both sketch generation and V-Ray style rendering. Built specifically for Google Cloud Run deployment, it showcases production-ready GenAI application architecture with containerized deployment and Vertex AI integration.
This project serves as a proof of concept for advanced GenAI orchestration within the Google Cloud ecosystem, demonstrating how to chain multiple AI models to create complex creative workflows.
Building production GenAI applications requires orchestrating multiple AI models in sequence, managing cloud infrastructure, and creating user-friendly interfaces. Developers need examples of how to structure multi-step AI workflows with proper error handling and cloud deployment.
GenAI applications often require chaining multiple models (text-to-text, text-to-image, image-to-image) to achieve complex creative outputs. Cloud deployment adds complexity with authentication, containerization, and scalability requirements.
A three-stage pipeline architecture: (1) Text Enhancement using Gemini 2.0 Flash Lite to expand simple descriptions, (2) Sketch Generation using Gemini 2.5 Flash Image to create architectural line drawings, (3) Photorealistic Rendering using Gemini 2.5 Flash Image to transform sketches into V-Ray style renders. All stages orchestrated in Streamlit with Google Cloud Run deployment.
Uses Gemini 2.0 Flash Lite to expand simple user descriptions into detailed architectural specifications. Prepares enriched context for downstream image generation stages.
Uses Gemini 2.5 Flash Image to generate architectural line drawings from enhanced text descriptions. Creates intermediate visual representations for rendering pipeline.
Uses Gemini 2.5 Flash Image to transform sketches into V-Ray style photorealistic renders. Applies advanced prompting techniques for realistic architectural visualization.
Containerized Streamlit application deployed on Google Cloud Run with Vertex AI integration. Uses Google Cloud authentication for secure API access.
Gemini 2.0 Flash Lite provides fast, cost-effective text enhancement. Gemini 2.5 Flash Image offers high-quality image generation for both sketches and renders. Streamlit enables rapid UI development. Cloud Run provides serverless scaling and easy deployment. Docker ensures consistent containerization.
Deep dive into the technical implementation with annotated code examples
View Technical DetailsOrchestrating multiple AI models with proper error handling and state management
Implemented sequential pipeline with explicit stage transitions. Each stage validates inputs and handles errors gracefully, allowing partial completion with user feedback.
Managing Google Cloud authentication and Vertex AI API integration
Uses Google Cloud application-default credentials with environment variable configuration. Properly configured project and region settings for Vertex AI access.
Containerizing Streamlit application for Cloud Run deployment
Created Dockerfile with Debian Slim base image, optimized for Cloud Run resource constraints. Configured proper port binding and health checks.
Successfully deployed production GenAI application on Google Cloud Run with live public access. Demonstrates complete workflow from text input to photorealistic architectural renders. Serves as reference implementation for multi-step GenAI orchestration on Google Cloud.