Generate structured images from text. Describe scenes as labeled components—foreground, subject, background, lighting—for clear, organized results.
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A node based approach means structuring your prompt into labeled sections or components, like Foreground, Subject, Background, and Lighting. Each part describes one element of the scene. The AI interprets each section separately and combines them into a final image. This helps you control complex compositions and iterate on specific parts without rewriting the entire prompt.
Input
Foreground: a wooden table with a coffee cup. Background: a sunny window with curtains. Lighting: warm morning light from the left
Expected output
A structured scene showing a coffee cup on a wooden table in the foreground, with curtained window background and warm morning light from the left
Input
Character: a young man with short brown hair, wearing a blue hoodie. Action: walking on a city street. Setting: rainy evening with neon reflections
Expected output
A character-focused scene of a man in a blue hoodie walking on a rainy neon-lit city street, with noticeable reflection effects
Input
Layer 1: a grassy field. Layer 2: a small cottage with smoke from chimney. Layer 3: a mountain range at sunset. Sky: orange and purple gradient
Expected output
A layered landscape with grassy field foreground, cottage middle ground, mountain range background, and sunset gradient sky
Break down your scene into foreground, subject, background, and lighting elements. The generator interprets each component from your text.
Use labeled sections in your prompt to separate different visual elements. Each part contributes to the final composition.
Change one element at a time in your structured prompt to refine specific parts of the scene without losing the overall composition.
Describe the desired image.
Choose the available generation settings.
Generate, review, and download a suitable result.
Organizing your prompt into labeled sections helps communicate complex visual ideas clearly.
Copy a structure, then adjust the subject, lighting, camera, and output details for your own result.
Foreground: wildflowers in a meadow. Midground: a wooden barn with red roof. Background: rolling hills under a clear blue sky. Lighting: bright afternoon sun from above.
Separates the scene into foreground, midground, and background layers with distinct lighting, creating a clear spatial composition.
Character: a woman in a red coat holding an umbrella. Action: standing still. Setting: rainy city street at night. Lighting: neon signs reflecting on wet pavement.
Isolates the character, action, setting, and lighting into separate components for precise control over each visual element.
Product: a silver laptop open on a desk. Surface: dark wooden table. Background: blurred home office with bookshelf. Lighting: soft window light from the right.
Structures the product shot into product, surface, background, and lighting components, giving each part clear importance.
Subject: a glowing crystal floating in air. Environment: ancient stone temple interior. Atmosphere: mist and floating particles. Lighting: soft blue glow from the crystal.
Names the main subject, environment, atmosphere, and light source separately, allowing each to be refined independently.
Subject: a young man with glasses, smiling. Outfit: casual denim jacket. Background: blurred park with autumn trees. Lighting: warm golden hour light from the left.
Separates subject features, clothing, background, and lighting into distinct labeled sections for a clear portrait composition.
Match the workflow to the input you have and the result you need before opening the generator.
Input: Label each major element in your scene: Foreground, Subject, Background, Lighting. Describe each in one clear sentence.
Result: A layered composition where each labeled element is placed and rendered as described
Tool: /text-to-image
Input: Keep most labeled sections the same, but change one component's description to refine that element only
Result: A new version of the image with only the changed component updated, preserving the rest of the composition
Tool: /text-to-image
Input: Label Product, Surface, Background, and Lighting separately to define each visual layer precisely
Result: A professional-looking product image with each element clearly controlled and placed
Tool: /text-to-image
Input: Separate Character, Outfit, Action, Setting, and Lighting into labeled components
Result: A character portrait where each component is distinct and can be adjusted independently in future prompts
Tool: /text-to-image
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Open the AI image generator and write a structured prompt with labeled components. Generate, review, and refine one element at a time for better results.
