ai-fashion-house 0.1.1

AI Fashion House: A platform for AI-driven fashion design and analysis

AI Fashion House

A project built for the ADK Hackathon with Google Cloud, AI Fashion House is a multi-agent system designed to assist with design inspiration, fashion image generation, and cinematic runway video creation.

What is AI Fashion House?

AI Fashion House is an AI-powered fashion design assistant that transforms expressive or abstract user prompts into rich visual content. Built on a modular, multi-agent architecture, it automates the entire creative pipeline—from concept interpretation to high-fidelity visual generation—by coordinating a set of intelligent, specialized agents.

How It Works

The system relies on a multi-agent framework, where each agent handles a specific step in the creative process. These agents operate asynchronously, enabling a flexible and dynamic design workflow:

1. Input Analysis Interprets user input to identify themes, fashion concepts, and stylistic cues.

2. Visual Reference Retrieval The met_rag_agent agent searches the Metropolitan Museum of Art's open-access archive (over 500,000 images) to retrieve relevant historical references.

   • BigQuery RAG: Performs semantic retrieval using Retrieval-Augmented Generation with BigQuery.
   • GenAI Embeddings: Embeds captions using the text-embedding-005 model for similarity comparison.
   • Gemini Multimodal Analysis: Processes both images and text to extract stylistic and structural fashion details.

3. Internet Search Expansion The search_agent agent uses Google Search Grounding to retrieve contemporary fashion references from the web.

4. Style Prompt Generation The promp_writer_agent & fashion_design agents organize visual data using a sequential pattern and combines it via an aggregator assistant to produce a detailed, fashion-specific prompt.

5. Artifact Creation and Orchestration The marketing_agent agent uses the style prompt to generate visual outputs:

   • Imagen 3 is used to produce high-quality fashion images.
   • Veo 3 generates stylized runway videos.

Target Audience

AI Fashion House is designed for:

• Fashion designers exploring new creative directions
• Educators and students in fashion design programs
• Archivists and curators seeking to combine history with generative AI
• Creators and developers interested in visual storytelling and AI-powered prototyping

Technology Stack

This project integrates:

• Google Cloud (Vertex AI, BigQuery, Cloud Storage)
• Gemini API and GenAI text/image embedding models
• Imagen 3 and Veo 3 for advanced image and video synthesis
• A modular, multi-agent orchestration system

Multi-Agent Architecture

Each step of the workflow is managed by a dedicated agent:

1. Input Analysis
2. Visual Reference Retrieval (met_rag agent)
   • BigQuery-based semantic search
   • Embedding generation and filtering
   • Multimodal image analysis
3. Web Search (research_agent agent)
4. Prompt Generation (fashion_design agent and aggregator)
5. Visual and Video Generation (marketing_agent agent using Imagen 3 and Veo 4)

Installation

Setting Up MET BigQuery RAG

See the met_rage README for full setup instructions.

Create and Activate Python Environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install Dependencies

pip install ai-fashion-house

Configure Environment Variables

Create a .env file in the root directory with the following content:

GOOGLE_GENAI_USE_VERTEXAI=1
GOOGLE_API_KEY=<YOUR_GOOGLE_API_KEY>
GOOGLE_CLOUD_PROJECT=<YOUR_GOOGLE_CLOUD_PROJECT>
GOOGLE_CLOUD_LOCATION=<YOUR_GOOGLE_CLOUD_LOCATION>

BIGQUERY_DATASET_ID=met_data
BIGQUERY_EMBEDDINGS_MODEL_ID=embeddings_model
BIGQUERY_EMBEDDINGS_TABLE_ID=fashion_ai_outputs_embeddings

VEO2_OUTPUT_GCS_URI=gs://myfiles2025
VEO2_MODEL_ID=veo-3.0-generate-preview
IMAGEN_MODEL_ID=imagen-4.0-generate-preview-06-06

Run the Application

ai-fashion-house start

Open your browser and navigate to:

http://localhost:8080

to access the AI Fashion House interface.