# Florence-2 Image Captioner

Canonical page: https://fuser.studio/models/florence-2-image-captioner

Florence-2 in Fuser writes captions and detailed descriptions of an image at three levels of detail. Use them as prompts or alt text in your workflow.

## Specs

| Spec | Value |
| --- | --- |
| Inputs | Image |
| Output | text |
| Cost | 4 credits per run |

Literal, spatially grounded image captions and dense training annotations at high speed

Creator: Microsoft

[Caption images now](https://app.fuser.studio)

## How Florence-2 Image Captioner works

From single-subject labels to granular scene descriptions, configure detail levels to match your indexing or training pipeline.

### Submit your image

Provide a photographic image URL for scene parsing and cataloging.

### Select detail level

Choose basic subject tags, detailed spatial context, or granular multi-sentence descriptions.

### Extract structured captions

Receive objective, literal visual descriptions formatted for LoRA training datasets or indexing.

## What Florence-2 Image Captioner is good at

Engineered for objective visual parsing, rapid inference, and dense spatial grounding across large image collections.

### Multi-tiered caption depth

Switch between single-sentence subject summaries, balanced environmental descriptions, and exhaustive multi-sentence scene analyses grounded in the model's specialized endpoints.

### LoRA dataset synthetic annotation

Synthesize dense, hallucination-resistant texture and object catalogs engineered specifically for training generative image architectures like FLUX and SDXL.

### High-throughput batch processing

Execute high-speed visual analysis across massive image batches with a uniform, lightweight compute footprint designed for automated asset pipelines.

### Spatial and texture grounding

Deconstruct photographic scenes into tangible objects, material surfaces, spatial orientations, and exact color palettes with literal precision.

## Made with Florence-2 Image Captioner

Explore how structured visual captioning translates real-world photographs into precise natural-language annotations.

### Atmospheric cinematic scene analysis

### Archival documentation and artifact inventory

### Automotive advertising cataloging

### High-fashion movement and fabric breakdown

## What people build with Florence-2 Image Captioner

Built for machine learning researchers, catalog engineers, and accessibility teams needing fast, literal image descriptions.

### Generative model dataset annotation

Produce thousands of consistent, hallucination-resistant synthetic captions for training custom image generators and subject LoRAs.

### Automated e-commerce tagging

Catalog thousands of inventory photographs into objective descriptions of materials, colors, cuts, and styles for search filters.

### Visual media search indexing

Convert vast image libraries into searchable natural-language indices, making complex historical or editorial imagery instantly discoverable.

### Accessible alt-text generation

Create literal, screen-reader-compliant alt-text descriptions that convey scene layout, subjects, and text without subjective interpretation.

### Creative asset auditing

Audit marketing collaterals and photographic boards to automatically extract object placements, color schemes, and visual motifs.

## Frequently Asked Questions

### What is the difference between the basic caption and detailed caption variants?

The basic caption variant generates a concise, single-sentence summary identifying only the primary subject, making it ideal for high-speed indexing and basic alt-text. The detailed caption variant expands on this by incorporating spatial context and immediate environmental relationships while maintaining a compact single-sentence structure.

### When should I use the more-detailed caption variant?

Select the more-detailed caption variant when you need dense, multi-sentence synthetic annotations for generative model training datasets like FLUX or SDXL LoRAs. It performs an exhaustive cataloging of background elements, surface textures, and fine spatial arrangements, whereas the standard detailed variant focuses primarily on foreground spatial relationships.

### What types of images and tasks is Florence-2 best suited for?

Florence-2 excels at photorealistic scene analysis, accessibility alt-text generation, large-scale search indexing, and automated LoRA dataset captioning. It is less suited for open-ended conversational visual reasoning, complex stylized 2D illustrations, anime aesthetic tagging, or transcribing stylized handwritten text.

### Who developed Florence-2 and what is its model architecture?

Florence-2 was developed by Microsoft and released open-source in June 2024. Despite its compact footprint of 0.77 billion parameters, it leverages a sequence-to-sequence transformer architecture combining a DaViT vision encoder with a BART text decoder trained on the FLD-5B dataset containing 5.4 billion visual annotations.

### What are the compute costs for batch processing with Florence-2?

All captioning variants—basic, detailed, and more-detailed—operate at a flat compute cost of 3.5 per execution, providing an economical solution for massive dataset processing and automated asset tagging.

## Try Florence-2 Image Captioner on Fuser

Literal, spatially grounded image captions and dense training annotations at high speed

[Caption images now](https://app.fuser.studio)

## Related models

- [Gemini Image](https://fuser.studio/models/gemini-image.md)
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- [Seedream](https://fuser.studio/models/seedream.md)
- [FLUX.3 Image](https://fuser.studio/models/flux-3-image.md)
- [FLUX.2](https://fuser.studio/models/flux-2.md)
- [Topaz Image Upscale](https://fuser.studio/models/topaz-image-upscale.md)

Node reference: [Florence-2 Image Captioner docs](https://docs.fuser.studio/docs/nodes/image/florence-2-image-captioner.md)

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- [AI Image Generation Glossary: 31 Terms Explained](https://fuser.studio/articles/ai-image-generation-glossary.md)