This content originally appeared on DEV Community and was authored by Denardis Asquith
Tired of AI tools that can't get hands right? I created Imagen4 to solve modern AI image generation - one model that nails details other AIs miss.
It was 3 AM when I finally admitted defeat. My client needed a simple product mockup - just a phone with a custom case design. After 47 generations across 5 tools, every AI still gave me:
• Six-fingered hands 🤦
• Floating logos
• "Abstract" textures that looked like GPU artifacts
I had promised "AI magic" and delivered nightmare fuel instead.
Why Most AI Image Tools Fail
Through 200+ failed attempts, I discovered three fatal flaws in current gen AI:
The Frankenstein Effect
Models stitch together random trained elements instead of understanding spatial relationships. That's why you get:
background = random_cityscape()
foreground = random_person()
return background + foreground # 🤮Training Data Blind Spots
Most models fail on:
• Hands (obviously)
• Text (always garbled)
• Branded items (illegal training data)No Creative Constraints
Without proper guidance, AIs default to "psychedelic clipart" aesthetics.
The Solution: Constrained AI Generation
The breakthrough came when I stopped treating AI as a black box and started giving it:
- Physical constraints (depth maps, normal vectors)
- Style anchors (reference color palettes)
- Iterative refinement
That's why I built https://www.imagen4.org/
Try It Yourself (Free Tier Available)
This content originally appeared on DEV Community and was authored by Denardis Asquith
Denardis Asquith | Sciencx (2025-07-22T12:20:02+00:00) Why I Built Imagen4 After 200 Failed AI Image Generations. Retrieved from https://www.scien.cx/2025/07/22/why-i-built-imagen4-after-200-failed-ai-image-generations/
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