Case Study: Iterating AI-Generated Aesthetic Consistency
Refining AI Creativity Through Prompt Engineering & Iteration
Overview
AI-generated content requires experimentation, refinement, and iteration to achieve the best results. This case study explores how I developed a cohesive visual theme across multiple AI-generated images—ensuring consistent lighting, texture, and color palette across different subjects.
Step 1: Setting the Creative Vision
Defining the Mood, Materials, and Color Palette
🔹 Color Palette: Soft pink monochrome
🔹 Lighting: Warm, golden sunlight
🔹 Materials: Plush, cozy textures (knit, wool, velvet)
🔹 Mood: Dreamy, intimate, and inviting
Initial Inspiration
💡 The challenge was to create a consistent aesthetic across multiple AI-generated images while keeping each unique subject visually unified.
Step 2: Writing the Base Prompt
To maintain cohesion, I used a single structured prompt, changing only the subject:
📌 Base Prompt:
"A [subject] made of soft pink knit fabric, bathed in warm natural sunlight, surrounded by plush textures like wool, velvet, and cotton, with a dreamy and inviting atmosphere."
🔄 Example Variations:
Cat: “Imagine a wool-knit soft pink cat bathed in warm natural light, surrounded by plush, cozy textures like wool, knit fabric, and velvet. The atmosphere is dreamy and inviting, with a gentle, soothing glow.”
Living Room: “Imagine a wool-knit soft pink living room bathed in warm natural light, surrounded by plush, cozy textures like wool, knit fabric, and velvet. The atmosphere is dreamy and inviting, with a gentle, soothing glow.”
Airplane: “Imagine a wool-knit soft pink airplane bathed in warm natural light, surrounded by plush, cozy textures like wool, knit fabric, and velvet. The atmosphere is dreamy and inviting, with a gentle, soothing glow.”
🖼 Image Set 1: Initial AI Outputs
Step 3: Refining for Consistency
Challenges & Adjustments
❌ Framing Differences – Some images were close-up, while others were too wide.
❌ Texture Inconsistencies – Some results lost the knit/wool details.
❌ Lighting Variations – The warmth and softness weren’t always consistent.
🔧 Iterations & Fixes:
✅ Specified “close-up shot” for uniform framing.
✅ Strengthened “knitted wool fabric” to keep textures accurate.
✅ Reinforced “warm golden sunlight” for consistent mood.
🖼 Image Set 2: After Refinements
Step 4: Final Results & Lessons Learned
Achieving a Unified AI-Generated Aesthetic
After multiple refinements, the final images maintained:
✅ Cohesive color palette (soft pinks throughout)
✅ Matching material textures (knit, wool, and velvet across all images)
✅ Consistent warm sunlight (same golden glow in every image)
🖼 Final Image Set
💡 Key Takeaways:
🔹 AI requires multiple iterations to achieve a polished look.
🔹 Strong prompt engineering is about guiding AI, not just accepting outputs.
🔹 Adjusting small details (like framing, lighting, and texture descriptors) makes a huge difference.
Step 5: AI Upscaling & Detail Enhancement
To enhance texture fidelity, I used Magnific AI to upscale the images, refining the fine-knit details and preserving realism while maintaining the original soft aesthetic.
Key Improvements:
✅ More defined wool & knit textures without looking artificial
✅ Sharpened lighting details for added depth
✅ Increased resolution for animation-ready assets
🖼 Before & After Comparisons
Step 6: Animating AI-Generated Content
💭 What’s Next?
I’m now taking these images into Runway AI to animate subtle movements, such as:
🌬 Soft wind gently moving the knitted curtains and blankets
✨ Subtle lighting shifts to enhance warmth
Final Thoughts: AI Creativity as an Iterative Process
This project demonstrates that AI creativity is not just about generating an image—it’s about refining, directing, and perfecting outputs over time. Through prompt engineering and iteration, AI can be a powerful tool for artists, designers, and brands looking to craft unique, cohesive visuals.