0
0OpenAI APIs1Gemini2Claude3Ollama4Open Source Models5Prompt Templates6Output Parsers7Streaming8Image Models9Vision Models10Audio Models11Multimodal
LLM Engineering & APIs
Phase 2Module 9 of 12

Image Models

Visual content creation is expensive and slow. Image models generate custom illustrations, product mockups, marketing assets, and UI concepts in seconds from text prompts — democratizing visual design.

A skilled illustrator who can draw anything you describe in words — 'a cat wearing a spacesuit on Mars, watercolor style' — and delivers in 10 seconds instead of 10 hours.

Visual Workflows

Start here — study each diagram, then use + / − to zoom if needed.

100%
Loading diagram...

Scroll inside the frame · use + / − to zoom

Key Takeaways

  • 1.Image generation models (DALL-E 3, Stable Diffusion, Midjourney) create images from text descriptions — enabling AI-powered design, illustration, and visual content creation. Architectures: Diffusion models (Stable Diffusion — iterative denoising from noise to image) and autoregressive (DALL-E — generates image tokens).
  • 2.DALL-E 3 via OpenAI API: prompt → image URL/base64. Stable Diffusion: open-source, runs locally, customizable with LoRA styles.
  • 3.Key parameters: prompt (description), size (1024x1024), quality (standard/hd), style (vivid/natural). Prompt engineering matters: be specific about style, composition, lighting.
  • 4.Negative prompts (SD): what to exclude. ControlNet (SD): guide generation with edge maps, poses.

Real Example

Scenario

Marketing team needs a blog header: 'Minimalist illustration of a robot and human shaking hands, blue and white color scheme, flat design'. DALL-E 3 generates 1024x1024 image in ~15 seconds. No designer needed.

What you would do

In LLM Engineering & APIs, apply Image Models to this scenario: Marketing team needs a blog header: 'Minimalist illustration of a robot and human shaking hands, blue and white color scheme, flat design'. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Practice Task

Open the Code Walkthrough below and run it locally. Change one parameter related to Image Models (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Code Walkthrough

Highlighted lines show where Image Models happens in the code.

Image Models
1from openai import OpenAI  # import dependencies2
3client = OpenAI()  # create API client4
5response = client.images.generate(  # key line for Image Models6    model="dall-e-3",7    prompt="A minimalist flat illustration of an AI brain connected to code, blue and purple gradient, white background",8    size="1024x1024",9    quality="hd",10    n=1,11)12
13image_url = response.data[0].url  # key line for Image Models14print(f"Generated image: {image_url}")  # show output for debugging15
16# Stable Diffusion via diffusers (local)17# from diffusers import StableDiffusionPipeline18# pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1")19# image = pipe("a cat in space, watercolor").images[0]

Cheat Sheet

Quick recap

quick ref
  • •client.images.generate()
  • •model: dall-e-3
  • •size: 1024x1024
  • •quality: standard or hd
  • •SD: diffusers pipeline
  • •Specific prompts = better images

Common Mistakes

  • ✕Vague prompts — 'a dog' instead of specific description
  • ✕Not specifying style — unpredictable artistic choices
  • ✕Ignoring content policy filters — prompts get rejected
  • ✕Using DALL-E for high-volume generation — cost adds up