AI Art Prompt
Generator.
Free builder for image prompts across Midjourney, Stable Diffusion, DALLE, Flux, and Leonardo. Style, mood, lighting, and composition controls.
Subject plus style plus lighting plus composition. Stack layers, stronger images.
Describe what you want
3 prompt variations
Click Copy to use[describe the subject], cinematic style, dramatic / high contrast mood, golden hour lighting, rule of thirds composition
[describe the subject]. Style: Cinematic. Mood: Dramatic / high contrast. Lighting: Golden hour. Composition: Rule of thirds. Target: Any image model.
SUBJECT: [describe the subject] STYLE: Cinematic MOOD: Dramatic / high contrast LIGHTING: Golden hour COMPOSITION: Rule of thirds QUALITY: high detail, sharp focus, award winning composition, cohesive lighting
Under the hood
Why layered prompts beat descriptive paragraphs.
A clear one-sentence subject grounds the image. Everything else is a modifier on top. Vague subjects produce vague images no matter how many style tags you stack.
Style, mood, lighting, and composition each transform the image independently. Changing one layer at a time is the fastest way to learn what each axis does on each model.
Midjourney reads tag strings. SDXL reads structured prompts with negatives. DALLE reads instructions. Matching the format to the model is a free quality boost.
Related free tools
Specialized generators for specific tasks.
FAQ
Questions about AI art prompting.
Which image model should I pick?+
Midjourney v6 and v7 produce the most cohesive, cinematic output with the least prompt tuning. Stable Diffusion XL and SD3 give you control over samplers, ControlNet, and negative prompts but need more effort. DALLE 3 is strongest at following precise instructions including text. Flux is the current top scorer on prompt adherence and typography. Pick Midjourney for style, SD for control, DALLE for instruction following, Flux for precision.
What are the three variants for?+
Short tag strings work best for Midjourney, DALLE, and Flux where the model does heavy interpretation from sparse cues. Descriptive paragraphs work for any model and give richer control over mood and narrative. Structured with negatives is the SDXL and ComfyUI format: explicit subject, quality tags, and a negative prompt field to exclude artefacts.
Does style matter more than subject?+
Often yes. Changing 'cinematic' to 'watercolour' transforms the image more than changing 'astronaut' to 'firefighter'. Prompt quality comes from the style, mood, and lighting layers on top of a clear subject. Start with a one-sentence subject, then stack the modifiers.
What is a negative prompt and when do I need one?+
A negative prompt tells Stable Diffusion and similar models what to avoid: extra fingers, text watermarks, blurry, low quality, bad anatomy. Midjourney does not have a dedicated negative field, you use the --no flag instead. DALLE and Flux do not really need one. Negative prompts matter most on SDXL and SD3.
How specific should lighting and camera descriptions be?+
More specific usually beats less specific, up to a point. 'Golden hour' produces better output than 'nice light'. 'Shot on 35mm ARRI Alexa' produces better output than 'cinematic'. But stacking 15 camera and lens tags dilutes the prompt. Pick one or two strong cues per layer.
Why does the same prompt produce different results across models?+
Each model was trained on different datasets with different captioning conventions. Midjourney learned from curated aesthetic images, SDXL from a broader web scrape, DALLE from OpenAI's proprietary mix. The same text lands differently. A prompt optimised for Midjourney often needs rework for SDXL and vice versa.
Can I use this for commercial work?+
The prompts are free to use. Rights to the generated images depend on the model's terms of service. Midjourney, DALLE, and most paid services grant commercial rights on paid plans. Stable Diffusion outputs are typically unrestricted if you run the model yourself. Always check the current terms before commercial use.
How do I get consistent characters across multiple images?+
Character consistency is the hardest problem in image generation. Midjourney has a --cref (character reference) flag. Stable Diffusion uses IP-Adapter, LoRA training, or ControlNet. For small runs, generate one canonical image and use it as reference for subsequent prompts. For production, train a LoRA on your character and invoke it by token.