Imagem para prompt
Batch Convert, Characters Intact

An image to prompt tool reverse-engineers a picture into the text prompt that would recreate it. It reads the subject, composition, lighting, colour palette and art style, then writes those observations in the syntax your target model expects. It is the fastest way to reproduce a look you like without describing it yourself.

Prompts can be generated in Midjourney syntax (with --ar and --style parameters), Stable Diffusion syntax (with weighted terms and a matching negative prompt), and plain descriptive syntax for DALL-E 3, Flux, Ideogram and Leonardo. Video prompts for Sora and Kling include camera movement and shot duration.

Yes. Batch mode accepts multiple images in one upload and returns a prompt for each, exportable as CSV or JSON. This is the practical difference from most free converters, which handle one image per request and make storyboard or dataset work impractical.

Upload a reference image of the character once and save the description it produces as a reusable character block. That block is prepended to every subsequent prompt, so the same face, clothing and build carry across an entire sequence. This is what storyboard and short-film work needs and what a single-image converter cannot do.

You can convert images without registering. A free account raises the daily limit and stores your prompt history so you can search and re-export past results. Paid plans exist for higher volume and API access.

Images are used only to generate the prompt for that request and are deleted immediately afterwards. They are not retained, not used for training, and not shown to anyone else.

Regenerating an image from the prompt gives a close stylistic match rather than a pixel-identical copy, because image models are not deterministic. Expect the subject, palette, lighting and style to carry over reliably. Treat the output as a strong starting point and edit the wording to taste.

Yes. Prompts can be written in English or in your own language. This matters if you feed them into a model tuned for that language, where a native-language prompt often outperforms a translated one.