Kenerate AI Image to Prompt
Drop in any image to reverse-engineer its visual DNA — extract structured attributes and generate formatted prompts for every top AI model.

Or try a sample
A cinematic waist-up portrait of a young woman with a gentle expression, lit by soft natural window light, muted earth tones, shallow depth of field, shot on an 85mm f/1.8 portrait lens with authentic skin texture.
Subject
Young woman with gentle gaze and natural styling, subtle smile
Composition
Centered waist-up portrait, shallow depth of field, blurred background
Lighting
Soft directional natural window light, gentle fill, golden rim highlight
Style
Cinematic realism, muted organic tones, clean analog aesthetic
Camera
85mm portrait lens, f/1.8 aperture, creamy bokeh
Negative
Blur, low resolution, bad anatomy, overexposed highlights, plastic skin
Mood & Color
Dreamy, calm, intimate, soft and elegant
Upload or select reference image
Upload any picture or pick a sample photo to start reverse engineering.
AI deep visual decomposition
Extracts 7 structural visual attributes: subject, composition, lighting, camera, style, mood, and negatives.
Multi-model tailored prompts
Switch tabs to get tailored prompt syntax for Midjourney, Flux, SD, DALL-E, Gemini, and ChatGPT.
Inspiration
Discover AI video and image creations made with Kenerate AI
Cyberpunk Neon Samurai
“Futuristic samurai walking through a rain-soaked cyberpunk Tokyo street, volumetric neon lighting”
Majestic Crystal Phoenix
“A mythical bird made of glowing azure crystal feathers, perched on ancient ruins”
Frequently Asked Questions
The analysis organizes visible details such as the subject, composition, lighting, camera, style, mood, and elements to avoid into a structured prompt.
No. It analyzes a reference image and returns prompt text. You can copy that text and use it in an image-generation workflow.
The workbench provides prompt formats for several model families, including Midjourney, Flux, Stable Diffusion, DALL·E, Gemini, and ChatGPT. Review and edit the output for your chosen model.