Inpainting and outpainting in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Inpainting Purpose

What is the main goal of inpainting in image generation?

A To increase the resolution of an image by adding pixels

B To generate a completely new image from random noise

C To fill missing or damaged parts of an image using surrounding context

D To segment an image into different object classes

💡 Hint

Think about fixing or completing parts of an existing image.

Inpainting is about filling missing or damaged areas in an image by using information from the surrounding pixels to create a seamless result.

❓ Predict Output

intermediate

Output of Outpainting Model Code

Given the following pseudocode for an outpainting model, what is the shape of the output image if the input image is 256x256 pixels and the model extends the canvas by 64 pixels on each side?

Prompt Engineering / GenAI

input_image = load_image('input.png')  # shape: (256, 256, 3)
output_image = outpaint_model(input_image, extend=64)
print(output_image.shape)

A (256, 256, 3)

B (256, 320, 3)

C (320, 320, 3)

D (384, 384, 3)

💡 Hint

Outpainting adds pixels around the original image edges.

The model extends the image by 64 pixels on each side, so width and height increase by 128 pixels total: 256 + 128 = 384.

❓ Model Choice

advanced

Best Model Architecture for Inpainting

Which model architecture is best suited for high-quality image inpainting tasks?

A Convolutional Autoencoder with skip connections

B Simple feedforward neural network

C Recurrent Neural Network (RNN)

D K-Nearest Neighbors (KNN) classifier

💡 Hint

Consider models that capture spatial features and details.

Convolutional autoencoders with skip connections preserve spatial details and context, making them ideal for inpainting.

❓ Metrics

advanced

Evaluating Outpainting Quality

Which metric is most appropriate to quantitatively evaluate the visual quality of an outpainted image compared to the original?

A Peak Signal-to-Noise Ratio (PSNR)

B Accuracy of classification labels

C Mean Squared Error (MSE) only on masked region

D Confusion matrix

💡 Hint

Think about measuring similarity between images.

PSNR measures the similarity between images and is commonly used to assess image reconstruction quality including outpainting.

🔧 Debug

expert

Debugging Inpainting Model Output

You trained an inpainting model but the output images have visible sharp edges around the filled regions, making the inpainted area obvious. What is the most likely cause?

A The input images were too large for the model

B The model was trained without using a smooth loss function or perceptual loss

C The model used too many convolutional layers

D The training dataset had too many images

💡 Hint

Consider what helps the model blend filled regions smoothly.

Using only pixel-wise loss can cause sharp edges; adding smooth or perceptual loss helps the model generate seamless transitions.

Practice

1. What is the main difference between inpainting and outpainting in image editing?

easy

A. Both inpainting and outpainting only remove unwanted parts from images.

B. Inpainting adds new areas around an image, outpainting removes parts inside it.

C. Inpainting fills missing parts inside an image, outpainting adds new areas around it.

D. Inpainting and outpainting are the same process with different names.

Solution

1. Step 1: Understand inpainting

   Inpainting is used to fill missing or unwanted parts inside an image, like fixing scratches or holes.

2. Step 2: Understand outpainting

   Outpainting extends the image by adding new content around the edges, making the image bigger.

3. Final Answer:

   Inpainting fills missing parts inside an image, outpainting adds new areas around it. -> Option C

4. Quick Check:

   Inpainting = fill inside, Outpainting = add outside [OK]

Hint: Inpainting fixes inside; outpainting grows outside [OK]

Common Mistakes:

• Confusing inpainting with outpainting
• Thinking both remove parts only
• Believing they are identical

2. Which of the following is the correct way to describe the input for an inpainting model?

easy

A. Only the new areas to add around the image.

B. An image with missing or masked areas to fill.

C. A complete image with no missing parts.

D. A text description of the image content.

Solution

1. Step 1: Identify input for inpainting

   Inpainting models require an image with missing or masked parts that need filling.

2. Step 2: Check other options

   Complete images or text descriptions are not direct inputs for inpainting; new areas relate to outpainting.

3. Final Answer:

   An image with missing or masked areas to fill. -> Option B

4. Quick Check:

   Inpainting input = image with holes [OK]

Hint: Inpainting needs holes in image input [OK]

Common Mistakes:

• Choosing complete images without masks
• Confusing input with outpainting requirements
• Selecting text descriptions as input

3. Given this Python pseudocode for outpainting, what will be the shape of the output image if the input image is 256x256 and the model adds 64 pixels on each side?

input_image = load_image('photo.png')  # shape (256, 256)
output_image = outpaint_model(input_image, border=64)
print(output_image.shape)

medium

A. (256, 256)

B. (192, 192)

C. (320, 320)

D. (384, 384)

Solution

1. Step 1: Calculate added pixels

   The model adds 64 pixels on each side, so total added width = 64 * 2 = 128 pixels.

2. Step 2: Calculate new image size

   Original size 256 + 128 = 384 pixels. This is 256 + 64 + 64 = 384, since 64 pixels on each side means adding 64 left and 64 right.

3. Step 3: Re-check options

   (384, 384) matches calculation. (320, 320) is 256 + 64, adding only one side.

4. Final Answer:

   (384, 384) -> Option D

5. Quick Check:

   256 + 64*2 = 384 [OK]

Hint: Add border pixels twice (both sides) to original size [OK]

Common Mistakes:

• Adding border only once
• Confusing inpainting with outpainting size change
• Ignoring both width and height increase

4. You run an inpainting model but the output image still has visible holes where the mask was applied. What is the most likely cause?

medium

A. The mask was not correctly applied to the input image.

B. The model was trained only for outpainting, not inpainting.

C. The input image was too large for the model to process.

D. The output image format does not support transparency.

Solution

1. Step 1: Check mask application

   If holes remain, the mask likely was not properly set, so the model didn't know where to fill.

2. Step 2: Evaluate other options

   Model type mismatch or image size issues usually cause errors or poor quality, not visible holes. Output format affects display but not hole filling.

3. Final Answer:

   The mask was not correctly applied to the input image. -> Option A

4. Quick Check:

   Mask error = holes remain [OK]

Hint: Check mask covers missing parts fully [OK]

Common Mistakes:

• Ignoring mask correctness
• Blaming model type without checking input
• Assuming output format causes holes

5. You want to create a larger scenic image by extending the edges of a 512x512 photo using outpainting. You also want to remove a small unwanted object inside the photo using inpainting. Which approach correctly combines both tasks?

hard

A. First apply inpainting on the original image to remove the object, then apply outpainting to extend the image edges.

B. Apply outpainting first to extend the image, then apply inpainting on the extended edges to remove the object.

C. Only use outpainting because it can both remove objects and extend images.

D. Only use inpainting because it can extend images and remove objects.

Solution

1. Step 1: Remove unwanted object first

   Inpainting fixes inside the image, so remove the object before changing image size.

2. Step 2: Extend image after cleanup

   Outpainting adds new areas around the cleaned image, so apply it after inpainting.

3. Step 3: Evaluate other options

   Outpainting cannot remove inside objects; inpainting cannot add new edges. Order matters for best results.

4. Final Answer:

   First apply inpainting on the original image to remove the object, then apply outpainting to extend the image edges. -> Option A

5. Quick Check:

   Fix inside first, then grow outside [OK]

Hint: Clean inside first (inpainting), then extend outside (outpainting) [OK]

Common Mistakes:

• Applying outpainting before inpainting
• Thinking one method does both tasks
• Ignoring task order importance