Project 6: Multi-Modal Content Generator

Duration: 2-3 weeks Points: 100 Prerequisites: Complete Lessons 13 (Diffusion), 17 (Multi-Modal AI) Difficulty: Advanced

---

Project Overview

In this project, you'll build a complete multi-modal content generation system that combines text-to-image, image-to-text, and image-text retrieval capabilities. You'll implement CLIP for vision-language understanding and integrate Stable Diffusion for high-quality image generation.

Why This Matters: Multi-modal AI is the frontier of modern AI, powering Google Lens, DALL-E, and GPT-4 Vision.

What You'll Build:

• CLIP-based image-text understanding system
• Stable Diffusion integration for text-to-image generation
• Image captioning with encoder-decoder architecture
• Interactive web application for all capabilities

---

Learning Objectives

By completing this project, you will:

1. Implement CLIP architecture with contrastive learning
2. Integrate Stable Diffusion for text-guided image generation
3. Build image captioning system with cross-attention
4. Create image-text retrieval system for semantic search
5. Deploy multi-modal web application with real-time inference
6. Assess zero-shot classification and retrieval performance

---

Requirements

Functional Requirements

Your Multi-Modal Content Generator must:

• Text-to-Image: Generate 512x512 images from text prompts with high quality
• Image-to-Text: Generate accurate captions (BLEU-``4 >= 0.25`` on COCO)
• Image Retrieval: Search image database using text queries (R@``5 >= 70``%)
• Zero-Shot Classification: Classify images into new categories without retraining
• Interactive Interface: User-friendly web app with all 4 capabilities

Technical Requirements

Your implementation must include:

• CLIP Model: Dual encoders (vision + text) trained with contrastive loss
• Stable Diffusion Pipeline: Text conditioning with classifier-free guidance
• Captioning Model: Encoder-decoder with cross-attention mechanisms
• Vector Database: FAISS for efficient similarity search
• Web Interface: Gradio or Streamlit with tabbed interface

---

Grading Rubric

Criterion	Points	Description
CLIP Implementation	25	Correct dual encoders with contrastive loss
Text-to-Image	20	High-quality Stable Diffusion generation
Image Captioning	20	Accurate captions (BLEU-``4 >= 0.25``)
Image Retrieval	15	Semantic search with R@``5 >= 70``%
Web Interface	15	Interactive, real-time, user-friendly
Documentation	5	Clear README with examples
Total	100

Bonus Points (+10 each):

• Implement image editing (inpainting, style transfer)
• Add Visual Question Answering (VQA) capability
• Implement zero-shot object detection with CLIP
• Deploy to public demo (Hugging Face Spaces)

---

Milestones

Week One: CLIP and Image Retrieval

• Days 1-3: CLIP training/loading
• Days 4-5: Build FAISS retrieval system
• Days 6-7: Initial metrics

Week 2: Image Generation and Captioning

• Days 8-10: Stable Diffusion integration
• Days 11-13: Image captioning model
• Day 14: Multi-modal integration

Week 3: Web Application

• Days 15-17: Interactive interface
• Days 18-19: Optimization
• Days 20-21: Documentation and demo

---

Resources

Documentation

• HuggingFace CLIP
• Stable Diffusion
• FAISS

  facebookresearch/faiss📋

  View on GitHub

Research Papers

• CLIP (Radford et al., 2021): Learning Transferable Visual Models
• Stable Diffusion (Rombach et al., 2022): High-Resolution Image Synthesis

Datasets

• MS-COCO: 200K images with 5 captions each
• Flickr30k: 31K images with captions

---

Submission Guidelines

Required Deliverables:

1. Code repository with all components
2. Trained/fine-tuned models
3. Web application (deployed or local)
4. Metrics report
5. Demo video (5 minutes)

Deadline: 2-3 weeks from project start

---

Portfolio Presentation

Demo Website:

• Title: "Multi-Modal Content Generator - Vision + Language AI"
• Live interactive demo
• Gallery showcasing results
• Technical highlights

LinkedIn/Resume:

"Built production-ready multi-modal AI system combining CLIP and Stable Diffusion for text-to-image generation, image captioning, and semantic search. Achieved 72% Recall@5 on image retrieval and 0.28 BLEU-4 on captioning."

---

Good luck! Multi-modal AI is the future of AI systems.

Related Projects:

• Project 5 - Text Generation with RLHF <- (Text-only alignment)
• Project 7 - Capstone AI Agent Ecosystem -> (Integrate all techniques)