Image Prompts

image-prompts

Core Philosophy

Authoring AI image generation prompts (Midjourney, Flux.1, DALL-E 3, Stable Diffusion) is not typing random adjectives like "hyperrealistic, 8k, award-winning trending on artstation" into a text box. Generative image models respond to precise photographic parameters, lighting physics, historical art references, spatial composition rules, and deliberate debiasing techniques. High-craft image prompts construct an exact visual scene while actively counteracting the models' algorithmic drift toward plastic, oversaturated stereotypes.

4-Step Generative Image Prompt Architecture

Step 1: The 5-Part Architectural Prompt Anatomy

1. The Structural Prompt Formula:
   • [Subject & Action] + [Environment & Context] + [Composition & Framing] + [Lighting & Atmosphere] + [Camera, Lens & Film Stock / Style Engine]
2. Component Breakdown:
   • Subject: Specific, concrete details (clothing texture, age, posture, expression, action).
   • Environment: Exact time of day, architectural materials (brutalist raw concrete, wet asphalt, mahogany paneling).
   • Composition: Rule of thirds, low-angle hero shot, Dutch tilt, wide-angle cinematic perspective.
   • Lighting: Volumetric god rays, directional Rembrandt lighting, diffuse cloudy softbox, neon back-rim lighting.
   • Camera / Medium: 35mm photograph, Leica M11, f/1.8 aperture, shallow depth of field, Kodak Portra 400 film grain.

Step 2: Eliminating Generative Clichés & Plastic Skin

1. The AI Slop Kill-List for Prompts:
   • Ban empty fluff words: photorealistic, hyperrealistic, ultra-detailed, 4K, 8K, masterpiece, trending on artstation. These words pollute the latent space with low-quality amateur renders.
2. Enforcing Natural Texture & Imperfection:
   • Specify real-world micro-textures to kill plastic rendering:
     • Skin: "Subtle skin pores, natural skin texture, unretouched, faint freckles, uneven lighting."
     • Environment: "Dust motes in light beams, worn edges on leather notebook, subtle motion blur."

Step 3: Countering Stereotypical Demographic Drift

1. Active Debiasing:
   • Image models default to severe cultural and gender stereotypes (e.g. prompt "software engineer" produces exclusively young white or Asian men; "nurse" produces young women).
   • Explicitly specify diverse, realistic demographic descriptors, ages (e.g. "50-year-old female systems architect"), clothing (realistic workwear, not futuristic spandex), and natural working environments.

Step 4: Technical Parameters & Engine Flags

1. Platform-Specific Flag Tuning:
   • Midjourney v6:
     • Aspect Ratios: --ar 16:9 (landscape/hero), --ar 1:1 (avatar), --ar 4:5 (social portrait).
     • Stylize: --s 50 to --s 150 (restrained realism; default 250 is often overly cartoonish).
     • Weird & Chaos: Keep --c 0 to --c 10 for reproducible commercial production.
   • Flux.1 (Dev / Schnell):
     • Responds best to natural language paragraphs over comma-separated tag soup.

Deliverable Format: AI Image Prompt Specification (IMAGE-PROMPTS.md)

# AI Image Generation Prompt Book: [Campaign / Feature]

## 1. Asset: Hero Visual for Developer Documentation
- **Target Model**: Flux.1 Dev / Midjourney v6
- **Aspect Ratio**: 16:9 (`--ar 16:9`)
- **Visual Genre**: Documentary editorial photograph

### The Master Prompt
> An authentic 35mm documentary photograph of a 42-year-old female lead infrastructure engineer debugging code late at night in a server room. She wears a navy wool sweater and glasses with thin titanium frames. Subtle facial expression of intense concentration. The background features server racks with out-of-focus amber and soft white blinking LED indicator lights. Soft rim lighting from terminal screens, moody atmospheric shadows, shallow depth of field, f/2.0 aperture. Shot on Leica M6 with Kodak Portra 400 film grain, natural skin pores and subtle shadows, zero plastic smoothing, zero neon sci-fi holograms. --ar 16:9 --s 100 --v 6.0

## 2. Negative Constraints & Exclusions
- No glowing holographic HUDs floating in air.
- No purple or magenta cyberpunk neon lighting.
- No airbrushed smooth plastic mannequin skin.

Worked Example: Overhauling a Generic Tech Hero Visual

• Original Prompt: "Software engineer working on futuristic AI, hyperrealistic, 8k, glowing screens, masterpiece." -> Produced a cartoonish neon cyborg.
• Refined Craft Prompt: Specified natural documentary photography, natural daylight from a warehouse window, real mechanical keyboard, and Kodak film grain.
• Result: Photorealistic, credible editorial image used as the hero graphic for an enterprise Series B announcement.

Verification Checklist

• Prompt follows the 5-part anatomical structure (Subject, Context, Framing, Lighting, Camera).
• Banned fluff words ("hyperrealistic", "8k", "trending on artstation") are 100% eliminated.
• Natural skin texture, grain, and physical imperfections are explicitly instructed.
• Stereotypical algorithmic drift is countered with specific demographic descriptors.
• Technical flags (--ar, --s) match target generation engine.

Anti-Patterns

• Tag Soup Prompting: Stacking 40 disconnected keywords with commas hoping the model figures it out.
• Cyberpunk Defaulting: Letting models inject purple neon glows and floating binary code into serious enterprise visuals.
• Neglecting Aspect Ratios: Generating square images for a 16:9 website hero banner and cropping off critical subjects.