AI Image Prompt Generator
Build a detailed, well-structured image prompt in seconds. Describe your subject, then pick a style, lighting, camera angle, mood, color palette, and aspect ratio — and the tool assembles the right platform-specific syntax: a natural-language sentence for today's leaders — Nano Banana 2 (Google Gemini), GPT Image 2 (OpenAI) and FLUX — a weighted keyword prompt plus a negative prompt for Stable Diffusion, and --ar / --stylize flags for Midjourney. Live preview, sample prompts, and one-click copy. Free, no signup.
How to Use This Tool
- Pick your platform tab — Midjourney, GPT Image, FLUX, or Stable Diffusion. The output syntax changes to match each one.
- Describe your subject in plain words, or load a sample (Portrait, Landscape, Product, Abstract) to see a full example instantly.
- Choose the look — style, lighting, camera angle, mood, color palette, and aspect ratio, plus optional text to render inside the image (a standout strength of Nano Banana 2 and GPT Image 2). Each selection adds rich, model-friendly phrasing.
- Tune the platform extras. On Midjourney, set the
--stylizevalue (higher = more artistic liberty). On Stable Diffusion, edit the negative prompt to rule out artifacts. - Read the live preview — Midjourney flags are highlighted in green, SD weights in yellow — then hit Copy Prompt and paste it into your image tool.
- Remix to randomize the style/lighting/mood selections for fast variations on the same subject.
About Image Prompting & Platform Syntax
Text-to-image models are astonishingly capable, but they only render what you describe — and the difference between a muddy result and a striking one is almost always the prompt. A good image prompt does four jobs: it names the subject clearly, sets the style and medium, describes the lighting and composition, and establishes a mood and palette. Leave any of those to chance and the model fills the gap with its average guess. This generator turns those four jobs into a handful of choices and assembles them into a clean, coherent prompt — so instead of staring at a blank box, you're picking from proven building blocks.
The catch is that today's leading image tools don't all speak the same language. Midjourney and Stable Diffusion grew up on tag-heavy training data, so they respond best to comma-separated keywords and use special syntax for parameters: Midjourney appends flags like --ar 16:9 for aspect ratio and --stylize 250 for artistic strength, while Stable Diffusion supports weights like (subject:1.2) and a separate negative prompt. Nano Banana 2 (Google's Gemini image model, the most popular generator right now), GPT Image 2 (OpenAI's DALL-E successor and the top-ranked model), and FLUX are the opposite: they're tuned for natural language and ignore Midjourney-style flags, so you describe the scene in a plain sentence and set the aspect ratio with your tool's size control rather than in the prompt (FLUX goes further and ignores weights and negative prompts too). Paste a keyword-and-flag string into Nano Banana or GPT Image and you waste half of it; paste a flowery paragraph into Midjourney and it dilutes. This tool generates the correct form for whichever tab you're on, from the exact same selections, so you never have to remember which syntax goes where.
A few principles separate prompts that work from prompts that fight themselves. Specificity beats length: 'a misty pine forest at dawn, fog drifting between the trunks, soft golden light' outperforms 'a beautiful nature scene' and also a twenty-adjective pile-up. Coherence matters: one style, one lighting, one mood, and one palette that agree with each other produce a unified image, whereas contradictory tags (minimalist + highly detailed + busy) confuse the model. Negatives are quality control on Stable Diffusion — listing 'deformed hands, extra fingers, watermark, text' steers the model away from its most common failures. And photorealism is a vocabulary, not the word 'realistic': cameras, lenses, depth of field, and real-world texture cues are what tip a render into a photo. The tool's presets bake these lessons in, so a single choice like 'photorealistic' brings the right camera, lens, and sharp-focus cues along with it.
Once you have a prompt that's close, iterate deliberately. Lock a seed in your image tool and change one element at a time so you can see what each word actually does, rather than rolling a completely new image every run. Use the Remix button here to spin up coherent variations of the same subject when you want options, and adjust the stylize value to trade fidelity for flair. Save the prompts that land — a personal library of working prompts is the single biggest time-saver for anyone generating images regularly.
Great prompts are step one; turning AI image generation into a reliable creative pipeline — on-brand style guides, batch production, post-processing, and rights-clean usage — is the harder, higher-value work. Our AI-Powered Marketing team integrates Midjourney, GPT Image, FLUX, and Stable Diffusion into creative workflows that produce on-brand assets at 10x speed. Pair this generator with the AI Prompt Builder for text prompts, the Social Media Image Resizer to size your generated art for every platform, and the LLM Cost Calculator to budget the AI behind it all.
Frequently Asked Questions
What does the Midjourney --ar flag do?
The --ar flag sets the aspect ratio (width to height) of the image Midjourney generates. You write it at the end of the prompt, for example --ar 16:9 for widescreen, --ar 9:16 for a vertical phone wallpaper, --ar 1:1 for a square, or --ar 21:9 for ultrawide. Without it, Midjourney defaults to a square 1:1. Aspect ratio matters a lot because it shapes composition: a 16:9 frame invites a horizontal landscape while a 2:3 frame suits a portrait. Midjourney accepts most whole-number ratios but very extreme ones can distort or crop the subject. This generator appends the flag for you when you pick a ratio in the Midjourney tab, alongside the --stylize value, so the syntax is always correct.
How do Nano Banana 2, GPT Image and Midjourney prompts differ?
They reward opposite writing styles. Midjourney and Stable Diffusion respond best to comma-separated keywords and tags — 'a fox, watercolor, golden hour, dramatic lighting, --ar 16:9' — because they were trained on tag-heavy caption data and use flags for parameters. Nano Banana 2 (Google Gemini), GPT Image 2, FLUX and Imagen are tuned for natural language: they follow a clear descriptive sentence or short paragraph far better than a pile of tags, and they don't use Midjourney-style flags, so you describe the scene in words and set the aspect ratio with your tool's size or aspect-ratio control instead. That's why this tool generates a different output per platform from the same selections: a written sentence for Nano Banana 2, GPT Image 2 and FLUX, a weighted keyword prompt plus a negative prompt for Stable Diffusion, and a keyword string with flags for Midjourney. Pick the tab that matches your tool and copy the version it produces.
What is a Stable Diffusion negative prompt?
A negative prompt tells Stable Diffusion what to avoid — it lists things you do NOT want in the image, and the model steers away from them. It's one of the most effective quality levers in SD because the base model often introduces common artifacts. A typical negative prompt includes terms like 'blurry, lowres, deformed hands, extra fingers, extra limbs, jpeg artifacts, watermark, text, bad anatomy'. For a clean product shot you might add 'clutter, reflections, dust'; for a stylized illustration you might add 'photo, realistic' to push it away from photorealism. Midjourney has a lighter equivalent (the --no parameter), while Nano Banana 2, GPT Image and FLUX don't use negative prompts at all (with FLUX you steer away from artifacts using positive wording instead). This generator gives you an editable negative-prompt field on the Stable Diffusion tab (and a --no field on the Midjourney tab) and seeds it with a sensible default you can tailor.
How do I prompt for photorealistic images?
Photorealism comes from photography vocabulary, not the word 'realistic' alone. Name a camera and lens ('shot on a Canon EOS R5, 85mm f/1.4'), describe the lighting precisely (golden hour, softbox studio light, rim light), specify depth of field ('shallow depth of field, bokeh'), and favor concrete photographic detail over generic boosters like 'ultra-detailed' or '8k', which add little on current models. Real-world cues — skin texture, fabric weave, reflections, imperfections — push the model toward a photo rather than an illustration. Keep the subject description concrete and avoid fantastical elements that signal 'art'. On Stable Diffusion, a strong negative prompt ('cartoon, painting, illustration, cgi') reinforces the photographic look. This tool's 'photorealistic' preset adds the right cues for you: a full-frame camera and 85mm lens for the natural-language models, and DSLR and sharp-focus tags for Stable Diffusion and Midjourney; pair it with studio or golden-hour lighting and a close-up or eye-level camera for the most convincing results.
How do I reference an art style in a prompt?
You can steer style three ways. First, with descriptive style words — 'watercolor', 'oil painting', 'cel-shaded anime', 'low-poly 3D render' — which this tool's Style selector turns into rich phrasing. Second, with medium and technique cues ('impasto brushstrokes', 'cross-hatching', 'octane render'). Third, on Midjourney specifically, with an image style reference: the --sref flag followed by an image URL transfers the look of that reference to your generation, and --cref does the same for a character. Naming living artists is increasingly discouraged or restricted for ethical and policy reasons, so prefer describing the visual qualities you want (color, texture, era, mood) over copying a specific artist's name. Combine a style with matching lighting and palette for a coherent result rather than fighting selections that clash.
What are seed numbers and how do they help reproducibility?
A seed is the number that initializes the random noise an image model starts from. The same prompt with the same seed (and same settings) produces the same — or nearly the same — image every time, which makes results reproducible. That's useful for iteration: lock the seed, change one word, and you can see the effect of just that change instead of a totally different picture. In Midjourney you retrieve a job's seed and reuse it with --seed N; in Stable Diffusion you set the seed directly in your interface; Nano Banana, GPT Image and FLUX expose less seed control. Varying the seed while keeping the prompt fixed is also how you generate alternatives of the same concept. This generator focuses on the prompt text itself — set the seed in your image tool — but writing a stable, well-structured prompt is what makes seed-locked iteration meaningful.
What is prompt weight syntax like (word:1.3)?
Weighting lets you tell the model how much to emphasize part of a prompt. In Stable Diffusion the syntax is (term:number) — for example (red dress:1.3) increases the influence of 'red dress' by 30%, while (background:0.6) reduces it. Values above 1 strengthen a term and values below 1 weaken it; stacking parentheses like ((word)) is older shorthand for emphasis. Use it to rescue a detail the model keeps ignoring or to tone down one that's taking over. Midjourney uses a different mechanism — the ::number syntax (cat::2 dog::1) and prompt weighting — while Nano Banana 2, GPT Image and FLUX have no weighting syntax and rely on clear wording. This tool wraps your subject in a light (subject:1.2) weight on the Stable Diffusion tab so it stays the focal point; you can adjust or add weights after copying.
What are the most common AI image prompt mistakes?
The biggest one is being too vague — 'a nice landscape' gives the model nothing to work with, while 'a misty pine forest at dawn, fog between the trees, soft golden light' gives it direction. The second is contradiction: asking for 'minimalist, highly detailed, busy composition' in one prompt confuses the model. Third is mixing the wrong syntax for the platform — keyword tags and --flags do nothing in Nano Banana, GPT Image or FLUX, and natural-language paragraphs are diluted in keyword-driven models. Fourth is ignoring negatives on Stable Diffusion, then wondering why hands and text look wrong. Fifth is overloading: twenty competing style words produce mush, whereas a clear subject plus a few coherent descriptors (one style, one lighting, one mood, one palette) produces a strong image. This generator is built around that last principle — one well-chosen value per dimension, assembled into clean platform-correct syntax.