Stable Diffusion overview in Prompt Engineering / GenAI - ML Experiment: Train & Evaluate

Experiment - Stable Diffusion overview

Problem:You want to generate clear images from text descriptions using Stable Diffusion, but the model sometimes creates blurry or unclear images.

Current Metrics:Image clarity score: 65/100, Text-image alignment score: 70/100

Issue:The model produces images that are not sharp enough and sometimes do not match the text description well.

Your Task

Improve the image clarity and text-image alignment scores to at least 85/100 while keeping generation time under 10 seconds per image.

You cannot change the text input descriptions.

You must use the Stable Diffusion model architecture.

You can adjust model parameters and inference settings only.

Hint 1

Hint 2

Hint 3

Hint 4

Solution

Prompt Engineering / GenAI

from diffusers import StableDiffusionPipeline
import torch

# Load the Stable Diffusion model
pipe = StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5', torch_dtype=torch.float16)
pipe = pipe.to('cuda')

# Set parameters to improve image clarity and alignment
prompt = "A beautiful sunset over a mountain lake"
num_inference_steps = 50  # Increased steps for better quality
guidance_scale = 7.5      # Higher guidance for better text alignment

# Generate image
image = pipe(prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale).images[0]

# Save or display the image
image.save('output.png')

Increased the number of inference steps to 50 to improve image clarity.

Increased guidance scale to 7.5 to better align images with text prompts.

Used half precision (float16) and GPU to keep generation time under 10 seconds.

Results Interpretation

Before: Image clarity 65/100, Text-image alignment 70/100

After: Image clarity 88/100, Text-image alignment 90/100

Increasing diffusion steps and guidance scale helps the model generate sharper images that better match the text description, demonstrating how tuning inference parameters can reduce image blurriness and improve relevance.

Bonus Experiment

Try using a different scheduler like DDIM or LMS to see if image quality improves further without increasing generation time.

💡 Hint

Change the scheduler parameter in the pipeline and compare results visually and with clarity/alignment scores.

Practice

1. What is the main purpose of Stable Diffusion in AI?

easy

A. To translate languages automatically

B. To analyze financial data

C. To create images from text descriptions

D. To detect spam emails

Solution

1. Step 1: Understand Stable Diffusion's function

   Stable Diffusion is designed to generate images based on text prompts.

2. Step 2: Compare with other options

   Other options describe different AI tasks unrelated to image generation.

3. Final Answer:

   To create images from text descriptions -> Option C

4. Quick Check:

   Stable Diffusion = image generation from text [OK]

Hint: Remember: Stable Diffusion = text to image [OK]

Common Mistakes:

• Confusing Stable Diffusion with language translation
• Thinking it analyzes data instead of creating images
• Mixing it up with spam detection tools

2. Which of the following is the correct way to give a prompt to Stable Diffusion?

easy

A. "A sunny beach with palm trees"

B. generate_image(sunny beach palm trees)

C. image.create('sunny beach')

D. createImage: sunny beach, palm trees

Solution

1. Step 1: Identify proper prompt format

   Stable Diffusion accepts text prompts as strings describing the image.

2. Step 2: Check options for correct syntax

   Only "A sunny beach with palm trees" uses a simple text string suitable as a prompt.

3. Final Answer:

   "A sunny beach with palm trees" -> Option A

4. Quick Check:

   Prompt = plain text string [OK]

Hint: Prompts are plain text descriptions in quotes [OK]

Common Mistakes:

• Using code-like syntax instead of plain text
• Omitting quotes around the prompt
• Mixing function calls with prompt text

3. Given the prompt "A cat sitting on a red chair", what kind of output should Stable Diffusion produce?

medium

A. A text description of a cat on a chair

B. An image showing a cat sitting on a red chair

C. A list of cat breeds

D. A video of a cat on a chair

Solution

1. Step 1: Understand prompt to output relation

   Stable Diffusion generates images based on text prompts.

2. Step 2: Match prompt to output type

   The prompt describes a scene; the output is an image of that scene.

3. Final Answer:

   An image showing a cat sitting on a red chair -> Option B

4. Quick Check:

   Text prompt -> image output [OK]

Hint: Text prompt means image output, not text or video [OK]

Common Mistakes:

• Expecting text output instead of image
• Confusing image generation with video creation
• Thinking it lists information instead of creating visuals

4. You gave the prompt "A futuristic cityscape at night" but the output image is blurry and unclear. What is a likely cause?

medium

A. The input text was too long

B. The model does not support night scenes

C. Stable Diffusion only creates black and white images

D. The prompt was too simple or vague

Solution

1. Step 1: Analyze prompt clarity impact

   Simple or vague prompts can cause unclear images because the model lacks detail to generate sharp visuals.

2. Step 2: Evaluate other options

   Stable Diffusion supports night scenes and color images; prompt length is not the main issue here.

3. Final Answer:

   The prompt was too simple or vague -> Option D

4. Quick Check:

   Clear prompts = better images [OK]

Hint: Use detailed prompts for clear images [OK]

Common Mistakes:

• Assuming model can't create night scenes
• Thinking Stable Diffusion only makes black and white images
• Blaming prompt length instead of prompt detail

5. You want to create an image of a "red apple on a wooden table" but the generated image shows a green apple. What should you do to fix this?

hard

A. Add more detail to the prompt like "a bright red apple on a rustic wooden table"

B. Use a shorter prompt like "apple table"

C. Change the model to one that only creates fruit images

D. Remove color words from the prompt

Solution

1. Step 1: Understand prompt specificity effect

   Adding more descriptive details helps the model focus on the correct colors and objects.

2. Step 2: Evaluate other options

   Shorter or vague prompts reduce clarity; changing models unnecessarily or removing color words won't fix the color issue.

3. Final Answer:

   Add more detail to the prompt like "a bright red apple on a rustic wooden table" -> Option A

4. Quick Check:

   Detailed prompts improve image accuracy [OK]

Hint: Make prompts detailed to get correct colors [OK]

Common Mistakes:

• Using vague or too short prompts
• Ignoring color details in the prompt
• Switching models without reason