Multimodal AI

🎯 Overview

Go beyond text! Learn to work with Vision-Language Models, Audio AI, and multimodal systems that combine text, images, audio, and video.

Prerequisites:

• ✅ Neural Networks & Transformers (Phase 6)
• ✅ LLMs & Prompt Engineering (Phase 11)
• ✅ Python & PyTorch

Time: 3-4 weeks | 60-80 hours
Outcome: Build AI systems that understand and generate across multiple modalities

This phase is a strong introduction and project-launch point. It is not yet intended to be the repo’s deepest research track for multimodal systems.

📚 What You’ll Learn

Vision-Language Models (VLMs)

• CLIP (Contrastive Language-Image Pretraining)
• LLaVA (Large Language and Vision Assistant)
• GPT-4V capabilities and API
• Gemini Pro Vision
• Image captioning and VQA (Visual Question Answering)
• Zero-shot image classification

Image Generation

• Stable Diffusion architecture
• DALL-E 3 API
• Midjourney concepts
• ControlNet for guided generation
• LoRA for Stable Diffusion
• Prompt engineering for images

Audio & Speech

• Whisper (speech-to-text)
• Text-to-Speech models (Bark, XTTS)
• Audio classification
• Music generation (MusicGen)
• Voice cloning
• Audio embeddings

Video Understanding

• Video captioning
• Action recognition
• Temporal understanding
• Video generation and editing workflows
• Realtime multimodal interaction and live camera/audio agents
• Video-language reasoning over long clips

Multimodal RAG

• Image + text search
• Document understanding (OCR + LLM)
• Multimodal embeddings
• Cross-modal retrieval
• Vision-language reranking and grounded citations

2026 Topics To Add To Your Radar

• Omnimodal models that combine text, image, audio, and video in one runtime
• Video generation systems and edit models for storyboard, marketing, and simulation tasks
• Realtime voice + vision assistants for screen, webcam, and mobile workflows
• Open multimodal stacks such as CLIP, SigLIP, LLaVA-class models, and Flux-style image generators

🗂️ Module Structure

13-multimodal/
├── 01_START_HERE.ipynb                    # Overview & capabilities
├── vision-language/
│   ├── 01_clip_basics.ipynb               # CLIP fundamentals
│   ├── 02_vision_language_models.ipynb    # VLMs (LLaVA, GPT-4V)
│   └── 03_multimodal_rag.ipynb            # Multimodal retrieval
├── image-generation/
│   ├── 01_stable_diffusion.ipynb          # Stable Diffusion basics
│   └── 02_controlnet.ipynb               # Guided generation
├── audio/
│   ├── 01_whisper_speech_recognition.ipynb # Speech-to-text
│   └── 02_text_to_speech.ipynb            # TTS models
└── README.md

🚀 Quick Start

Example 1: CLIP - Zero-Shot Classification

from transformers import CLIPProcessor, CLIPModel
from PIL import Image
 
# Load CLIP
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
 
# Load image
image = Image.open("photo.jpg")
 
# Define categories
labels = ["a cat", "a dog", "a bird", "a car"]
 
# Process
inputs = processor(
    text=labels,
    images=image,
    return_tensors="pt",
    padding=True
)
 
# Get similarities
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image
probs = logits_per_image.softmax(dim=1)
 
# Results
for label, prob in zip(labels, probs[0]):
    print(f"{label}: {prob:.2%}")

Example 2: GPT-4 Vision API

from openai import OpenAI
 
client = OpenAI()
 
response = client.chat.completions.create(
    model="gpt-4-vision-preview",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What's in this image? Describe in detail."},
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://example.com/image.jpg"
                }
            }
        ]
    }],
    max_tokens=300
)
 
print(response.choices[0].message.content)

Example 3: Whisper - Speech to Text

import whisper
 
# Load model (tiny, base, small, medium, large)
model = whisper.load_model("base")
 
# Transcribe
result = model.transcribe("audio.mp3")
 
print(result["text"])
# Also available: word-level timestamps, language detection

Example 4: Stable Diffusion

from diffusers import StableDiffusionPipeline
import torch
 
# Load model
pipe = StableDiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    torch_dtype=torch.float16
).to("cuda")
 
# Generate
prompt = "A beautiful sunset over mountains, oil painting style"
image = pipe(
    prompt,
    negative_prompt="blurry, low quality",
    num_inference_steps=30,
    guidance_scale=7.5
).images[0]
 
image.save("output.png")

📋 Learning Path

Week 1: Vision-Language Basics

• Complete 00_START_HERE.ipynb
• CLIP fundamentals in vision-language/01_clip_basics.ipynb
• Vision-language models in vision-language/02_vision_language_models.ipynb
• Project: Build image classifier

Week 2: Image Generation & Multimodal RAG

• Stable Diffusion in image-generation/01_stable_diffusion.ipynb
• ControlNet in image-generation/02_controlnet.ipynb
• Multimodal RAG in vision-language/03_multimodal_rag.ipynb
• Project: Custom image generator

Week 3: Audio

• Whisper in audio/01_whisper_speech_recognition.ipynb
• TTS in audio/02_text_to_speech.ipynb
• Project: Audio transcription system

Week 4: Realtime Multimodal and Video

• Study video understanding and generation patterns

What Comes Next

• Continue to ../20-real-time-streaming/README.md for realtime interaction patterns.
• Continue to ../24-advanced-deep-learning/README.md for deeper architecture work.
• Continue to ../28-practical-data-science/README.md if you want applied project work.
• Compare image-first, audio-first, and omni-model workflows
• Project: Build a multimodal assistant that can interpret image + speech input

🛠️ Technologies You’ll Use

Vision-Language Models:

• CLIP (OpenAI)
• SigLIP / SigLIP 2
• LLaVA (open-source)
• GPT-4V (OpenAI)
• Gemini Pro Vision (Google)
• BLIP-2, InstructBLIP

Image Generation:

• Stable Diffusion (open-source)
• FLUX and ControlNet-style guided pipelines
• DALL-E 3 (OpenAI)
• Midjourney (via API)
• ControlNet, T2I-Adapter
• IP-Adapter

Audio Models:

• Whisper (OpenAI)
• Bark (Suno AI)
• XTTS (Coqui)
• MusicGen (Meta)
• AudioCraft

Frameworks:

• Hugging Face Transformers
• Diffusers
• OpenCV
• torchaudio
• librosa

📊 Key Concepts

CLIP Architecture

Image → Vision Transformer → Image Embedding
Text → Text Transformer → Text Embedding

Training: Maximize similarity of matching pairs,
          minimize similarity of non-matching pairs

Applications:

• Zero-shot classification
• Image search by text
• Content moderation
• Feature extraction

Stable Diffusion Pipeline

Key Parameters:

• num_inference_steps: Quality vs speed (20-50)
• guidance_scale: Prompt adherence (7-15)
• negative_prompt: What to avoid
• seed: Reproducibility

Multimodal Embeddings

# Same embedding space for text and images!
text_embedding = clip.encode_text("a red car")
image_embedding = clip.encode_image(car_image)
 
# Compute similarity
similarity = cosine_similarity(text_embedding, image_embedding)

🎯 Projects

1. Visual Chatbot

Chat with images using GPT-4V or LLaVA.

Skills: VLM integration, conversation memory

2. Image Generator App

Stable Diffusion with custom UI and parameters.

Skills: Diffusion models, prompt engineering, UI

3. Meeting Transcriber

Record, transcribe, summarize with Whisper + LLM.

Skills: Audio processing, LLM integration

4. Visual Search Engine

Search image library by text description.

Skills: CLIP embeddings, vector search, multimodal RAG

5. Document QA System

Answer questions about PDFs with images/charts.

Skills: OCR, vision models, RAG

💡 Best Practices

Vision-Language

DO ✅

• Use specific, detailed prompts
• Provide image context
• Chain vision → reasoning → action
• Handle image quality issues
• Validate outputs

DON’T ❌

• Assume perfect OCR
• Ignore image resolution
• Skip error handling
• Trust all outputs blindly

Image Generation

DO ✅

• Use negative prompts
• Iterate on prompts
• Control with ControlNet
• Use appropriate steps (30-50)
• Set random seed for consistency

DON’T ❌

• Use default prompts only
• Expect perfection first try
• Ignore quality settings
• Generate at max resolution always (slow!)

Audio Processing

DO ✅

• Preprocess audio (denoise)
• Use appropriate model size
• Check language detection
• Validate transcriptions
• Handle silence/noise

DON’T ❌

• Process very long files without chunking
• Ignore audio quality
• Skip timestamp alignment

🔗 Resources

Courses

• Hugging Face Diffusion Models Course 
• DeepLearning.AI - Vision Transformers 
• Fast.ai - Stable Diffusion 

Papers

• CLIP: Learning Transferable Visual Models 
• Stable Diffusion 
• LLaVA: Visual Instruction Tuning 
• Whisper: Robust Speech Recognition 

Tools & APIs

• Hugging Face Diffusers 
• AUTOMATIC1111 Stable Diffusion WebUI 
• ComfyUI 
• OpenAI Vision API 

Models

• CLIP Models 
• Stable Diffusion Models 
• Whisper Models 
• LLaVA Models 

✅ Completion Checklist

Before moving forward, you should be able to:

• Use CLIP for zero-shot classification
• Build image captioning systems
• Generate images with Stable Diffusion
• Optimize image prompts
• Transcribe audio with Whisper
• Understand VLM architectures
• Build multimodal RAG systems
• Combine text and visual search
• Deploy multimodal applications
• Handle edge cases (quality, errors)

🎓 What’s Next?

Phase 15: AI Agents →

• Agents with vision capabilities
• Tool use with multimodal inputs
• Autonomous systems

Phase 12: LLM Fine-tuning →

• Fine-tune vision-language models
• Custom image generation models
• Specialized multimodal systems

Real-World Applications →

• Accessibility tools
• Content moderation
• Visual search
• Creative tools

Ready to go multimodal? → Start with 00_START_HERE.ipynb

Questions? → Check the notebooks for complete examples

🎨 Remember: A picture is worth a thousand tokens!