Introduction

AI image generation has evolved from producing surreal, flawed images to creating photorealistic, commercially viable artwork in seconds. Tools like DALL-E 3, Midjourney, and Stable Diffusion enable anyone to generate high-quality images from text descriptions. This guide covers the major platforms, prompt engineering techniques, and production workflows.

AI Image Generation Guide

Platform Comparison

DALL-E 3

OpenAI's DALL-E 3 excels at understanding complex prompts and rendering text within images — a task that stumps most other models.

Strengths:

  • Best-in-class prompt adherence

  • Reliable text rendering in images

  • Integrated with ChatGPT for iterative refinement

  • Strong safety filters prevent problematic outputs

Limitations:

  • Less stylistic variety than Midjourney

  • Cannot generate images of public figures or copyrighted styles

  • Lower maximum resolution (1024x1792 or 1792x1024)

Best for: General use, marketing materials, images with text

from openai import OpenAI

client = OpenAI()

response = client.images.generate(

model="dall-e-3",

prompt="A photorealistic coffee cup on a wooden table, morning sunlight from a window, steam rising in curls, shallow depth of field",

size="1792x1024",

quality="hd",

n=1

)

Midjourney

Midjourney produces the most artistically striking images, with a distinctive aesthetic that many prefer for creative work.

Strengths:

  • Superior artistic quality and composition

  • Wide range of stylistic controls

  • Strong community with shared prompt libraries

  • Consistent character generation with "cref" parameter

Limitations:

  • Requires Discord to use (no dedicated API)

  • Less precise prompt following than DALL-E

  • Weaker at rendering text and complex scenes

  • Steeper learning curve for parameters

Best for: Artistic work, concept art, character design

Stable Diffusion

Stable Diffusion is the open-source option, offering maximum control and customization.

Strengths:

  • Completely free and open-source

  • Run locally with full privacy

  • Fine-tune custom models (LoRA, DreamBooth)

  • Vast ecosystem of community models and extensions

  • ControlNet for precise spatial control

Limitations:

  • Requires technical setup for best results

  • Vanilla model quality lags behind Midjourney

  • Requires GPU for reasonable speed

Best for: Custom workflows, fine-tuned models, offline generation

Prompt Engineering for Images

The Anatomy of an Effective Prompt

A well-structured image prompt has these components:

[Subject] + [Action] + [Environment] + [Lighting] + [Style] + [Composition] + [Technical Details]

Example:

"An elderly Japanese woman [subject] practicing calligraphy [action] in a sunlit tatami room with cherry blossoms visible through an open window [environment], soft natural lighting with warm tones [lighting], ukiyo-e inspired digital art [style], close-up on hands and brush with shallow depth of field [composition], highly detailed 8K [technical]"

Negative Prompts

In Stable Diffusion and Midjourney, negative prompts specify what to avoid:

Negative prompt: ugly, deformed, blurry, low quality, extra limbs, bad anatomy, watermark, text, signature

Midjourney uses the --no parameter: --no text, watermark, blurry

Style Modifiers

Different styles dramatically change output:

  • Photographic : "photorealistic, f/2.8 aperture, 85mm lens, natural lighting, RAW format"

  • Illustrative : "vector art, clean lines, flat design, vibrant colors, white background"

  • Oil painting : "oil on canvas, impasto texture, dramatic chiaroscuro, classical composition"

  • Anime : "anime style, cel-shaded, Studio Ghibli inspired, soft pastel colors"

Advanced Techniques

ControlNet (Stable Diffusion)

ControlNet provides spatial control over image generation:

  • Canny edge detection : Use an edge map to control composition

  • OpenPose : Specify exact human poses

  • Depth maps : Control 3D layout

  • Normal maps : Control surface details

Inpainting and Outpainting

  • Inpainting : Replace specific regions of an image while preserving the rest

  • Outpainting : Extend an image beyond its original boundaries

LoRA Fine-Tuning

Create a small adapter that generates specific characters, objects, or styles:

Using Diffusers

from diffusers import StableDiffusionXLPipeline

import torch

pipe = StableDiffusionXLPipeline.from_pretrained(

"stabilityai/stable-diffusion-xl-base-1.0",

torch_dtype=torch.float16

)

pipe.load_lora_weights("path/to/lora-weights")

pipe.to("cuda")

image = pipe("a character in a garden, anime style").images[0]

Production Workflow

A production image generation pipeline:

  • Brief analysis : Extract subject, style, and composition requirements

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Conclusion

Each AI image generation platform has distinct strengths. DALL-E 3 wins for reliability and text handling, Midjourney for artistic quality, and Stable Diffusion for customization and control. The best results come from understanding each tool's strengths and combining them in a workflow — generate concepts in Midjourney, refine specifics with DALL-E, and post-process with Stable Diffusion's tooling.