Key models overview (GPT, DALL-E, Stable Diffusion) in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a GPT model instance.

Prompt Engineering / GenAI

model = GPT([1])

Drag options to blanks, or click blank then click option'

Alatent_dim=512

Bimage_size=256

Cnum_steps=1000

Dnum_layers=12

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing image_size or latent_dim which are for image models.

GPT models are built with layers, so specifying num_layers is correct.

2fill in blank

medium

Complete the code to generate an image using DALL-E.

Prompt Engineering / GenAI

image = dalle.generate(prompt, [1]=256)

Drag options to blanks, or click blank then click option'

Anum_layers

Bimage_size

Cnum_steps

Dlatent_dim

Attempts:

3 left

💡 Hint

Common Mistakes

Using num_layers which is for model architecture, not output size.

DALL-E generates images, so image_size sets the output size.

3fill in blank

hard

Fix the error in the Stable Diffusion sampling code.

Prompt Engineering / GenAI

sample = diffusion.sample(num_steps=[1])

Drag options to blanks, or click blank then click option'

A1000

B512

C256

D12

Attempts:

3 left

💡 Hint

Common Mistakes

Using layer counts or image sizes as step counts.

Stable Diffusion typically uses many steps like 1000 for good quality sampling.

4fill in blank

hard

Fill both blanks to define a GPT model with 12 layers and 768 hidden size.

Prompt Engineering / GenAI

model = GPT(num_layers=[1], hidden_size=[2])

Drag options to blanks, or click blank then click option'

A12

B768

C256

D1000

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up hidden size with image size or step count.

GPT models use num_layers and hidden_size to define architecture.

5fill in blank

hard

Fill all three blanks to generate an image with Stable Diffusion using 1000 steps and 512 latent dimension.

Prompt Engineering / GenAI

output = diffusion.generate(steps=[1], latent_dim=[2], image_size=[3])

Drag options to blanks, or click blank then click option'

A1000

B512

C256

D12

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing latent_dim with number of layers or image size.

Stable Diffusion uses many steps (1000), a latent dimension (512), and image size (256) for generation.

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