Key models overview (GPT, DALL-E, Stable Diffusion) in Prompt Engineering / GenAI - ML Experiment: Train & Evaluate

Experiment - Key models overview (GPT, DALL-E, Stable Diffusion)

Problem:You want to understand how three popular AI models--GPT, DALL-E, and Stable Diffusion--work and what they are best at.

Current Metrics:No current metrics because this is a conceptual overview.

Issue:The learner finds it hard to grasp the differences and purposes of these models.

Your Task

Explain in simple terms what GPT, DALL-E, and Stable Diffusion do, how they differ, and give examples of their outputs.

Use no technical jargon.

Use real-life examples to explain each model.

Keep explanations short and clear.

Hint 1

Hint 2

Hint 3

Solution

Prompt Engineering / GenAI

No code needed for this conceptual overview.

Used simple language to describe each model.

Connected each model to a relatable real-life example.

Focused on what each model produces and how it works in a friendly way.

Results Interpretation

Before: Confused about what GPT, DALL-E, and Stable Diffusion do.

After: Clear understanding that GPT writes text, DALL-E creates images from text prompts, and Stable Diffusion generates images gradually from noise.

Each AI model has a special skill: GPT is for writing, DALL-E is for creative image generation from text, and Stable Diffusion is for detailed image creation using a step-by-step process.

Bonus Experiment

Try using a simple text prompt with GPT to generate a short story, then use DALL-E or Stable Diffusion to create an image based on that story.

💡 Hint

Start with a fun sentence like 'A cat flying a spaceship' and see how each model brings it to life differently.

Practice

1. Which model is mainly used to generate human-like text?

easy

A. GPT

B. DALL-E

C. Stable Diffusion

D. None of the above

Solution

1. Step 1: Understand GPT's purpose

   GPT is designed to generate and understand human-like text.

2. Step 2: Compare with other models

   DALL-E and Stable Diffusion create images, not text.

3. Final Answer:

   GPT -> Option A

4. Quick Check:

   Text generation = GPT [OK]

Hint: Text output? Think GPT first. [OK]

Common Mistakes:

• Confusing DALL-E as text model
• Thinking Stable Diffusion generates text
• Choosing 'None of the above'

2. Which of the following is the correct way to describe DALL-E's function?

easy

A. It generates text based on images.

B. It compresses images for storage.

C. It creates images from text descriptions.

D. It translates text from one language to another.

Solution

1. Step 1: Identify DALL-E's main function

   DALL-E creates images from text prompts given by users.

2. Step 2: Eliminate incorrect options

   It does not generate text, translate languages, or compress images.

3. Final Answer:

   It creates images from text descriptions. -> Option C

4. Quick Check:

   Text to image = DALL-E [OK]

Hint: DALL-E = text to image creator. [OK]

Common Mistakes:

• Thinking DALL-E generates text
• Confusing with translation models
• Assuming it compresses images

3. Given the following code snippet using a model, what type of output should you expect?

model = 'Stable Diffusion'
input_text = 'A sunny beach with palm trees'
output = model.generate(input_text)

medium

A. A photo-realistic image of a sunny beach

B. A summary of the text input

C. A written story about a beach

D. An error because Stable Diffusion cannot generate output

Solution

1. Step 1: Identify Stable Diffusion's output type

   Stable Diffusion generates images from text prompts.

2. Step 2: Match input and output

   Input is a text description; output will be an image matching that description.

3. Final Answer:

   A photo-realistic image of a sunny beach -> Option A

4. Quick Check:

   Text input + Stable Diffusion = Image output [OK]

Hint: Stable Diffusion turns words into pictures. [OK]

Common Mistakes:

• Expecting text output
• Thinking it summarizes text
• Assuming it causes an error

4. You tried to use GPT to create an image by running this code:

model = 'GPT'
input_text = 'A cat sitting on a sofa'
output = model.generate_image(input_text)

What is the main problem here?

medium

A. The input text is too short for GPT to understand.

B. GPT cannot generate images; it only generates text.

C. The method name should be generate_text, not generate_image.

D. There is no problem; the code will work fine.

Solution

1. Step 1: Understand GPT's capabilities

   GPT is designed to generate text, not images.

2. Step 2: Analyze the method call

   Calling generate_image on GPT is invalid because GPT lacks image generation ability.

3. Final Answer:

   GPT cannot generate images; it only generates text. -> Option B

4. Quick Check:

   GPT = text only, no images [OK]

Hint: GPT does text, not images. [OK]

Common Mistakes:

• Thinking GPT can create images
• Believing method name is wrong only
• Ignoring model capability limits

5. You want to build an app that lets users type a prompt to generate a story and then see an image illustrating it. Which combination of models should you use?

hard

A. Use GPT for image generation and DALL-E for text generation.

B. Use DALL-E to generate the story and GPT to create the image.

C. Use Stable Diffusion for both story and image generation.

D. Use GPT to generate the story and Stable Diffusion to create the image.

Solution

1. Step 1: Identify model roles for text and image

   GPT is best for generating human-like text stories.

2. Step 2: Identify model for image creation

   Stable Diffusion creates images from text descriptions, perfect for illustrating stories.

3. Final Answer:

   Use GPT to generate the story and Stable Diffusion to create the image. -> Option D

4. Quick Check:

   Text by GPT + Image by Stable Diffusion = App [OK]

Hint: Text with GPT, images with Stable Diffusion. [OK]

Common Mistakes:

• Swapping roles of GPT and DALL-E
• Using one model for both tasks
• Confusing image and text generation roles