AI Image Generation Guide
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.
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
2\. **Prompt construction**: Build structured prompt with all components
3\. **Multi-seed generation**: Generate 4-8 variations with different seeds
4\. **Selection and refinement**: Upscale the best result, make targeted edits
5\. **Post-processing**: Adjust colors, add overlays, resize for destination
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.