AI Image to Prompt
Drop any image, get a descriptive caption or Stable Diffusion-style prompt. BLIP captioning runs entirely in your browser — no upload, no API key.
Why an AI Tool That Runs In Your Browser
Image-to-prompt tools are usually pitched at people running diffusion models locally and want a starting prompt for img2img or for fine-tuning. They are also useful for accessibility (writing alt text at scale), content auditing, and any workflow where you need a quick text description of a pile of images. Most hosted versions of this functionality require uploading your image to a third-party API. For unreleased designs, internal mockups, watermarked client work, or any sensitive image, that is a real problem. This tool uses BLIP — the Bootstrapped Language-Image Pretraining model from Salesforce Research — running in your browser via transformers.js. BLIP base was pretrained on roughly 14 million image-text pairs and fine-tuned for image captioning on the COCO dataset. It is released under the BSD-3-Clause license, which permits commercial use with attribution. The ONNX weights are mirrored by the Xenova organization on Hugging Face. The model produces short factual captions; the tool wraps them with optional style and quality tokens to produce something usable as a starting prompt for Stable Diffusion, Midjourney, or any other diffusion model.
How AI Image to Prompt Works
Click Load model on first visit. The browser downloads BLIP base — a vision encoder plus a text decoder, about 280 MB quantized total — from the Hugging Face CDN and caches both in IndexedDB. Drop or pick an image from your device. Three output modes are available: Caption produces a plain factual description ("a black cat sitting on a wooden chair"). SD prompt wraps the caption with neutral quality tokens ("a black cat sitting on a wooden chair, detailed, professional photography, sharp focus"). Conditional caption lets you provide a prefix like "a photograph of" or "a painting of" — the model continues the prompt from that prefix, which can produce more controlled output. BLIP captions are short by design — usually 10 to 20 words covering the dominant subjects and actions. For long, paragraph-length descriptions you would want a much larger model like BLIP-2 or LLaVA, which are not yet small enough for in-browser use. For the typical use case — getting a starting prompt for a diffusion model or generating accessibility alt text — BLIP base is well-sized.
Frequently Asked Questions
How much disk space does the BLIP model use?+
BLIP base is approximately 280 MB quantized total — a vision encoder (about 87 MB) and a text decoder (about 194 MB) downloaded together. The browser caches both in IndexedDB so later visits load in a few seconds. The first download takes about a minute on a typical home connection.
Are uploaded images sent to a server?+
No. After the model finishes downloading on first use, every caption runs entirely in your browser. The image stays on your device and is never sent to our servers, to the Hugging Face CDN, or to any third-party API.
Which captioning model powers this tool and under what license?+
The tool uses BLIP base from Salesforce Research, fine-tuned for image captioning on the COCO dataset. It is released under the BSD-3-Clause license, which permits commercial use with attribution. The ONNX weights are mirrored by the Xenova organization on Hugging Face.
How long are the generated captions?+
BLIP base produces short factual captions — typically 10 to 20 words covering the dominant subjects, actions, and scene context. It does not generate paragraph-length descriptions. For longer outputs, run the caption through one of the text AI tools (paraphraser or summarizer) or use a much larger captioning model on a server.
How do caption mode and SD prompt mode differ?+
Caption mode returns the raw BLIP output — a short factual description. SD prompt mode wraps that caption with neutral quality and style tokens (detailed, professional photography, sharp focus) so it can be pasted directly into Stable Diffusion or Midjourney as a starting prompt. Conditional caption mode lets you provide a prefix that the model continues from, giving you more control.
What image formats and sizes work?+
PNG, JPEG, WebP, and GIF (first frame). The vision encoder resizes inputs to 384 by 384 internally, so very large images do not produce more detailed captions — they just take longer to encode. For best results, crop to the part of the image you want described before captioning.
Why does the caption sometimes miss important details?+
BLIP base captures the dominant subjects and actions but misses fine details like exact colors, quantities, spatial relationships, or text within the image. It was trained on COCO, a dataset of everyday scenes, so it does best on natural photographs and weaker on charts, screenshots, anime, or technical drawings.
Can I use the output prompts commercially?+
BLIP itself is BSD-3-Clause licensed, which permits commercial use of the model. The output captions are descriptive text and not generally subject to copyright. Whether you can commercially use an image generated from a BLIP-derived prompt depends on the diffusion model and its license, not on this tool.
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