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

📚 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/
├── 00_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

• 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

        flowchart TD
    A[Text] --> B[CLIP Encoder]
    B --> C[Text Embedding]
    C --> D[U-Net - denoising]
    D --> E[VAE Decoder]
    E --> F[Image]
    

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!