Image to Prompt Generator
Upload any image and instantly get a high-quality, ready-to-use AI prompt that works across all major image models.
An image to prompt generator converts a photo into a structured AI prompt with subject, style, lighting, and composition.
Advanced: choose a model for optimized output
Real Examples You Can Copy
Real prompts from real images
Six real images and the exact prompts this tool returned for them.
How to Use This Tool
Image to prompt in 3 steps
Upload a JPG, PNG, WebP, or GIF (max 10 MB).
General mode works in Midjourney and most generators with minor tweaks. Click a chip for SD, Flux, Gemini, ImageFX, or Nano Banana to get model-tuned syntax.
The prompt appears on the right. Click Copy, paste into your image generator, edit freely.
Why it works
Why this works better than writing prompts manually
Most prompts fail because they miss key visual signals like lighting, composition, subject detail, and style.
This tool extracts those automatically and converts them into a prompt format that produces more consistent results.
Why use an image to prompt generator?
Manually writing prompts requires guessing details like lighting, style, and composition, which often leads to inconsistent results. An image to prompt generator removes that guesswork by analyzing a reference image and converting it into a structured prompt that works across different AI models.
This is especially useful when working with multiple tools like Midjourney, Stable Diffusion, Flux, or Gemini, where each model interprets prompts differently. Instead of rewriting prompts for each one, you can start from a consistent base and adapt faster.
For people who work from reference images and want a prompt they can paste directly into a model, without having to translate the image into keywords manually. The default output is general-purpose; the cluster pages below produce variants tuned for specific models.
FAQ
Frequently asked questions
Related tools
Need prompts for a specific model? Use a model-specific generator
Choose a dedicated generator below if you want prompts tuned for a specific AI model.
What an image-to-prompt generator does and does not do
An image-to-prompt generator reads a photo and writes out the prompt that would make a similar image in an AI generator. Below is a short editorial primer on what the category is for, what it handles well, and where the human user still has to make decisions the tool cannot make for them.
What the tool actually returns
The output of any image-to-prompt generator is a structured text description of the image, formatted as a prompt for a target AI image model. The structure varies by model — Midjourney wants comma-separated tokens with parameters, Stable Diffusion wants weighted tokens plus a paired negative, Flux wants natural-language sentences — but the core job is the same: read the visible content of the image, identify subject, lighting, composition, mood, and style, and emit text the model can act on.
The vision part is solved. Modern vision-language models reliably identify what is in a photograph — subject, setting, time of day, apparent mood, dominant colors, camera distance and angle. The interesting work is the formatting: writing a prompt that captures the same information in the syntax the target generator expects. This is why this site has six model-specific generators rather than one universal one.
Common use cases
Four cases where image-to-prompt is genuinely useful, not just convenient.
Recreating a visual style. If you have a reference image whose style you want to apply to different subjects, image-to-prompt is the fastest way to extract the stylistic anchors. Take the reference, generate a prompt, then swap the subject portion of the prompt with what you actually want to depict. The lighting, composition, and style language stays; the subject changes.
Recovering lost prompts. If you have a directory of generated images you liked but did not save the prompts for, running them through image-to-prompt gives you a reasonable approximation of each. Not exact, but close enough to get back to the same visual territory.
Translating between models. If you have a working prompt for one model (say, Midjourney) and want similar output from a different model (Stable Diffusion or Flux), the workflow is: generate the image with the original prompt, run that image through the target-model’s image-to-prompt generator, and use the new prompt as a starting point. Faster than rewriting from scratch and tends to land closer to the original intent than a structural rewrite would.
Understanding what the model sees. Sometimes a prompt produces inconsistent output and you cannot tell why. Generating a few images, picking the closest match, and running it through image-to-prompt shows you which elements the vision model is registering and which it is not. The diff between your original prompt and the one image-to-prompt produces is often where the inconsistency lives.
Choosing between the six models
The six generators in this cluster are not interchangeable. Each one outputs prompt text shaped for the specific model named:
Midjourney — Compact, comma-separated, with native parameters (--ar, --s, --v). Best for stylised work where the parameter syntax adds information that comma-separated text cannot.
Stable Diffusion — Weighted tokens with parentheses ((red:1.3)), paired with a negative prompt that suppresses common artefacts. Best for users running SDXL locally or via the web UI.
Flux — Long descriptive sentences in natural language. Flux.1 Dev was trained on long captions and rewards descriptive prose over keyword lists. Best for photorealistic and cinematic work.
Gemini — Conversational structured prompts that can name subject, style, and composition as explicit components. Handles both structured and natural-language prompts equally well; risk is over-prompting rather than under-prompting.
ImageFX — Atmospheric and cinematographic vocabulary. Lens specifications, film stocks, and lighting language shift output more on ImageFX than on other models. Best for photo-style work with specific technical character.
Nano Banana — Short focused prompts. Lightweight model that loses the thread on long prompts; five to fifteen tokens with one stylistic anchor outperforms a 50-token elaborate description.
If you do not know which model fits your image, the image-to-prompt generator hub covers the decision in more detail with examples per model.
Three things image-to-prompt does not solve
Three honest limits worth naming.
Exact recreation. Image-to-prompt produces a prompt that captures the visible elements; running that prompt back through the model produces an image with the same elements but not the same image. This is a feature, not a bug — if the goal is exact recreation, the right tool is image-to-image generation (img2img in SD, image editing in Gemini), not prompt extraction.
Copyrighted images. If the source image is a movie still, an artist’s portfolio piece, or a brand-owned asset, generating a prompt that approximates it sits in a legal grey zone. The output is technically a derivative work, and the closer the generated image lands to the source, the more exposure carried. The honest move for those cases is to use the source as inspiration but write the prompt from scratch.
Knowing what you actually want. The tool tells you what is in the reference image. It cannot tell you whether that is what you want — your reference might be 80% of the way to your intent, and the prompt you actually need is the reference’s vocabulary applied to a different subject. That editorial decision happens before the prompt is generated, not after.
Workflow integration
The most useful place for image-to-prompt is not at the start of an image project, but in the middle of one — when an existing prompt is producing inconsistent output and you want to see what a different vision-language model thinks the source image looks like. Generate a few images with your current prompt, pick the closest match to your intent, run that match through image-to-prompt, and edit your original to incorporate whatever the vision model saw that you missed. The vision model does not know your intent; it only knows what is visible. Its description is closer to what other generators will produce from a similar prompt than your original intent-driven prompt is.
After getting the prompt back, two natural next steps. If the prompt feels too short, run it through the prompt expander to add lighting and composition language. If the prompt scores well but produces inconsistent output, run it through the prompt analyser to see which dimension the model is weakest on. Both are short hops from this cluster.