Every model exposes a small set of dials beyond the prompt itself. The Midjourney parameter docs (docs.midjourney.com) list the canonical set: --ar for aspect ratio, --stylize for artistic license (0–1000), --seed for reproducibility, --no for exclusions, --cref for character reference, and --sref for style reference. SDXL and Flux expose CFG scale, steps, sampler, and seed via their respective UIs.
Three rules of thumb: lock seeds when iterating, vary seeds when exploring, and treat aspect ratio as a creative choice (3:2 for editorial photo, 4:5 for editorial illustration, 9:16 for vertical social, 1:1 for poster work).
What is the best way to write image prompts?
The best way to write image prompts is to use a seven-slot formula — Subject, Style, Composition, Lighting, Mood, Technical parameters, and Negative prompt — then test the same prompt across multiple models. Specificity beats poetry: 'a 67-year-old lighthouse keeper at golden hour, 85mm f/1.4, Magnum Photos aesthetic' will beat 'an old person by the sea' on every model.
How long should an image prompt be?
Most modern models (Flux, DALL-E 3, Ideogram 2.0) handle 75–250 token prompts well. SDXL works best with 60–120 tokens of comma-separated descriptors. Stuffing a prompt past ~250 tokens often causes the model to drop early tokens. Recent research on long-prompt benefits (arXiv 2403.06952) shows diminishing returns past a single dense paragraph.
Do negative prompts actually work?
Yes — for diffusion models like SDXL and Flux, the negative prompt is concatenated as the unconditional guidance signal. Standard negatives like 'lowres, bad anatomy, worst quality, jpeg artifacts, extra fingers, watermark' meaningfully reduce common failure modes. DALL-E 3 and Midjourney handle negatives differently — Midjourney uses --no, DALL-E 3 ignores them entirely and you should phrase exclusions in natural language.
What does CFG scale (or --stylize) actually do?
CFG (classifier-free guidance) controls how strictly the model follows your prompt. Low CFG (3–5) gives more creative, looser images; high CFG (10–15) hugs the prompt tightly but can over-saturate. Midjourney's --stylize parameter is the inverse — higher = more artistic license. Sweet spots: SDXL CFG 7, Flux guidance 3.5, Midjourney --stylize 100–250.
Why does the same prompt look different in DALL-E vs Midjourney vs Flux?
Each model was trained on different data with different conditioning. DALL-E 3 uses GPT-rewritten captions and rewards natural language. Midjourney is fine-tuned for aesthetic appeal. Flux 1.1 Pro is more literal and photoreal-leaning. SDXL is open-weights and depends entirely on the checkpoint. This is why testing the same prompt across 5+ models is the single highest-leverage habit.
What is a seed and why does it matter?
The seed is the random starting noise. Same prompt + same seed + same model + same parameters = identical image. Lock the seed when you want to iterate on a near-miss (change one word, keep everything else). Vary the seed when you want fresh variations. In Midjourney use --seed 42; in SDXL/Flux it's a parameter; DALL-E 3 doesn't expose seeds publicly.
Which AI gives the best image from this formula?
There is no single winner. Flux 1.1 Pro leads on photoreal portraits. Ideogram 2.0 wins for typography. DALL-E 3 follows long natural-language prompts most faithfully. SDXL with the right LoRA wins on stylized work. Recraft V3 is the editorial illustration leader. The fastest way to find out for your prompt is to run it across all of them in ZeroTwo Image Studio.
How do I get consistent characters across multiple images?
Three options: (1) lock the seed and change only one descriptor at a time; (2) train a LoRA on 10–20 reference images for SDXL/Flux; (3) use Midjourney's --cref (character reference) parameter pointing at a previous output URL. DALL-E 3 supports gen_id continuity in ChatGPT. For commercial work, LoRA training gives the most reliable identity lock.