Image to Prompt
Turn any image into a reusable prompt. Optionally re-theme it to a new subject while keeping the original style.
Image to Prompt Generator
Drop or select your image here Browse
PNG, JPG, JPEG, WebP · Up to 10MB
Keep the image's style, swap the subject/IP/text to a new theme.
Generated Prompt
No prompt generated yet
Upload an image and click Generate Prompt
Examples
Upload any image, extract a reusable prompt template, then edit the swap phrases (or use Re-theme) to generate a new subject in the exact same style.
GPT-Image-2Why each target model gets a different prompt
Most 'image-to-prompt' tools output one generic caption no matter which model you paste it into. We don't, because the same caption produces visibly different results across image models — each one was trained on different prompt phrasing conventions, and a prompt tuned for Midjourney usually under-performs on GPT-Image-2 and vice versa.
GPT-Image-2
Trained for natural-language descriptions with explicit composition cues. Long sentences work; comma-separated tags work badly. We bias toward 'A poster showing X in the style of Y, with Z arranged in the foreground' and explicit typography instructions like 'the headline reads: …'.
Midjourney v7
Tag-style prompts, comma-separated, with --ar / --style / --stylize parameters. We append --ar based on the source image's aspect ratio and avoid full English sentences (Midjourney downweights articles like 'a' and 'the').
FLUX (1.1 / Pro / Dev)
Prefers compact natural language (sweet spot ~30–80 tokens). Long prompts past ~150 tokens get truncated silently. Strong on photorealism cues ('shot on …', '85mm f/1.4', 'available light'); weaker on stylized illustration unless you explicitly say 'illustration' or 'flat vector'.
Nano Banana / Imagen / Seedream / Sora
Each has its own quirks — Imagen wants explicit subject placement, Seedream renders Asian-language typography better than its competitors, Sora needs camera-motion verbs ('the camera dollies in') even for still frames. We tune each variant accordingly.
How to actually use the prompt
1. Pick the target model that matches where you'll paste
If you'll paste into Midjourney, pick Midjourney — not 'generic'. The output prompt is dramatically different.
2. Use the swap hints, don't just generate verbatim
Each generated prompt highlights swappable phrases (subject, IP, headline text). Keep the structure, swap those — that's how you reuse one good image as a template for ten new variants.
3. Validate by pasting back
Paste the unmodified prompt back into the target model and compare against the source image. If the regenerated image is structurally close (same composition, same color story, same typography placement), the prompt captures the right signals. If not, the source image is probably an edge case — see Limitations below.
Where this tool falls short
- Heavy text-on-image (long paragraphs, complex tabular layouts): we transcribe what we see, but image models still struggle to render long body copy. The prompt is fine; the model is the bottleneck.
- Photographic vs. illustrated style detection: edge cases (3D rendered illustrations that look photographic, or photos heavily stylized to look 3D) sometimes get the wrong style label. Override manually if needed.
- Faces of specific real people: we deliberately describe by features ('woman with short dark hair, mid-30s') instead of names — both for likeness rights and because most image models block named-person prompts anyway.
- Trademarked IP: we describe characters generically ('young samurai with a red headband and three whisker marks per cheek') so the prompt remains usable. If you need exact IP names, edit them in manually after generation.