Image-to-image transformation in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main goal of image-to-image transformation models?

Choose the best description of what image-to-image transformation models do.

A Classify images into categories based on their content.

B Detect objects and draw bounding boxes on images.

C Generate images from random noise without any input image.

D Convert an input image into another image with desired changes, like style or content.

💡 Hint

Think about models that change one image into another.

Image-to-image transformation models take an input image and produce a new image that has been changed in some way, such as changing style, color, or adding effects.

❓ Model Choice

intermediate

Which model architecture is commonly used for image-to-image transformation tasks?

Select the model architecture best suited for image-to-image transformation.

A Convolutional Autoencoder with skip connections (U-Net)

B Recurrent Neural Network (RNN)

C Transformer for text generation

D Fully connected feedforward network

💡 Hint

Look for a model that preserves spatial details and can reconstruct images.

U-Net is a convolutional autoencoder with skip connections that helps preserve image details, making it ideal for image-to-image tasks.

❓ Metrics

advanced

Which metric best measures the quality of generated images in image-to-image transformation?

Choose the metric that evaluates how close the generated image is to the target image in terms of pixel-level similarity.

A Mean Squared Error (MSE)

B Accuracy

C Perplexity

D BLEU score

💡 Hint

Think about a metric that calculates average squared differences between pixels.

Mean Squared Error measures the average squared difference between pixels of generated and target images, indicating similarity.

🔧 Debug

advanced

What error will this image-to-image transformation training code raise?

Consider this Python snippet for training a model. What error occurs when running it? ```python import torch from torch import nn model = nn.Sequential( nn.Conv2d(3, 64, 3, padding=1), nn.ReLU(), nn.Conv2d(64, 3, 3, padding=1) ) input_image = torch.randn(1, 3, 256, 256) output = model(input_image) loss_fn = nn.MSELoss() # Target image has wrong shape target_image = torch.randn(1, 3, 128, 128) loss = loss_fn(output, target_image) ```

A TypeError: loss_fn() missing 1 required positional argument

B SyntaxError: invalid syntax in model definition

C RuntimeError: The size of tensor a (256) must match the size of tensor b (128) at non-singleton dimension 2

D No error, code runs successfully

💡 Hint

Check if output and target images have the same shape before computing loss.

The output image shape is (1,3,256,256) but target image shape is (1,3,128,128). MSELoss requires both inputs to have the same shape, causing a RuntimeError.

❓ Hyperparameter

expert

Which hyperparameter adjustment is most likely to improve image sharpness in a GAN-based image-to-image model?

You notice generated images are blurry. Which change is most effective to improve sharpness?

A Decrease the learning rate of the generator

B Increase the discriminator capacity or depth

C Reduce the batch size drastically

D Remove batch normalization layers from the generator

💡 Hint

Sharper images often come from a stronger discriminator that pushes the generator harder.

Increasing discriminator capacity helps it better distinguish real from fake images, forcing the generator to produce sharper images.

Practice

1.

What is the main goal of image-to-image transformation in AI?

easy

A. To change an input image into a different output image automatically

B. To classify images into categories

C. To detect objects inside an image

D. To generate text from an image

Solution

1. Step 1: Understand the purpose of image-to-image transformation

   This technique changes one image into another, like coloring or style transfer.

2. Step 2: Compare with other image tasks

   Classification, detection, and text generation are different tasks, not image transformation.

3. Final Answer:

   To change an input image into a different output image automatically -> Option A

4. Quick Check:

   Image-to-image transformation = change image [OK]

Hint: Image-to-image means input image changes to output image [OK]

Common Mistakes:

• Confusing transformation with classification
• Thinking it detects objects instead of changing images
• Mixing it up with text generation from images

2.

Which of the following is the correct way to describe an image-to-image model's input and output?

Input: ?
Output: ?

easy

A. Input: Image, Output: Image

B. Input: Text, Output: Image

C. Input: Image, Output: Text

D. Input: Number, Output: Image

Solution

1. Step 1: Identify input type for image-to-image models

   These models take an image as input to transform it.

2. Step 2: Identify output type for image-to-image models

   The output is also an image, changed in style, color, or content.

3. Final Answer:

   Input: Image, Output: Image -> Option A

4. Quick Check:

   Input and output both images [OK]

Hint: Both input and output are images in image-to-image tasks [OK]

Common Mistakes:

• Confusing input as text or numbers
• Thinking output is text instead of image
• Mixing input/output types

3.

Consider this simplified Python code using a model for image-to-image transformation:

input_image = load_image('sketch.png')
output_image = model.transform(input_image)
save_image(output_image, 'colorized.png')
print(type(output_image))

What will be printed?

medium

A. <class 'str'>

B. <class 'numpy.ndarray'>

C. <class 'PIL.Image.Image'>

D. Error: model.transform is not defined

Solution

1. Step 1: Understand typical output type of image-to-image models

   Most models output images as numpy arrays representing pixel data.

2. Step 2: Check code for output type

   Since model.transform returns an image, it is usually a numpy.ndarray, not a PIL Image or string.

3. Final Answer:

   <class 'numpy.ndarray'> -> Option B

4. Quick Check:

   Model output image = numpy array [OK]

Hint: Model outputs image arrays, not strings or PIL objects [OK]

Common Mistakes:

• Assuming output is a string filename
• Confusing PIL Image with numpy array
• Expecting error without context

4.

Look at this code snippet for image-to-image transformation:

def transform_image(model, img_path):
    img = load_image(img_path)
    result = model.transform(img)
    return result

output = transform_image(my_model, 12345)
print(type(output))

What is the main error here?

medium

A. The function returns None instead of an image

B. The model.transform method does not exist

C. The image path should be a string, not a number

D. The print statement is missing parentheses

Solution

1. Step 1: Check the argument passed to load_image

   load_image expects a file path string, but 12345 is a number, causing an error.

2. Step 2: Verify other code parts

   model.transform and print syntax are correct; function returns result properly.

3. Final Answer:

   The image path should be a string, not a number -> Option C

4. Quick Check:

   Image path must be string [OK]

Hint: File paths must be strings, not numbers [OK]

Common Mistakes:

• Thinking model.transform is missing
• Ignoring argument type for image path
• Confusing print syntax in Python 3

5.

You want to build an image-to-image model that converts black-and-white sketches into colored images. Which approach is best?

A dataset has pairs of sketches and their colored versions.

hard

A. Train a text-to-image model with sketch descriptions

B. Use unsupervised clustering on sketches only

C. Apply image classification on sketches

D. Train a supervised model using paired sketch and color images

Solution

1. Step 1: Identify the task type

   Converting sketches to colored images is a paired image-to-image translation task.

2. Step 2: Choose the right training method

   Supervised learning with paired data (sketch and color image) is best to learn direct mapping.

3. Step 3: Evaluate other options

   Unsupervised clustering, text-to-image, and classification do not fit this paired transformation task.

4. Final Answer:

   Train a supervised model using paired sketch and color images -> Option D

5. Quick Check:

   Paired data needs supervised training [OK]

Hint: Use paired images for supervised training in image-to-image tasks [OK]

Common Mistakes:

• Choosing unsupervised methods without paired data
• Confusing text-to-image with image-to-image
• Using classification instead of transformation