Florence-2-large-PromptGen-v2.0
Property | Value |
---|---|
Parameter Count | 823M |
Model Type | Image Captioning |
License | MIT |
Tensor Type | F32 |
What is Florence-2-large-PromptGen-v2.0?
Florence-2-large-PromptGen-v2.0 is an advanced image captioning model developed by MiaoshouAI that combines efficiency with versatility. This upgraded version builds upon its predecessor with enhanced caption quality and new analytical capabilities, while maintaining remarkably low VRAM requirements of just over 1GB.
Implementation Details
The model implements multiple instruction-based captioning modes, including tag generation, basic captioning, detailed captioning, and image analysis. It's specifically designed to work seamlessly with Flux models for both T5XXL CLIP and CLIP_L, enabling efficient single-pass caption generation.
- Memory-efficient architecture requiring only 1GB+ VRAM
- Support for multiple caption generation styles through specialized instructions
- Integration with MiaoshouAI Tagger ComfyUI for easy implementation
- F32 tensor type for precise computations
Core Capabilities
- Generate danbooru-style tags with <GENERATE_TAGS>
- Create structured position-aware captions with <DETAILED_CAPTION>
- Perform image composition analysis with <ANALYZE>
- Produce mixed caption styles combining detailed descriptions with tags
- Support for new <MIXED_CAPTION_PLUS> instruction combining analysis with mixed captioning
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its exceptional memory efficiency while delivering high-quality captions. Its ability to handle multiple caption styles and perform image analysis in a single lightweight package makes it particularly valuable for production environments.
Q: What are the recommended use cases?
The model is ideal for automated image captioning systems, content management platforms, and AI art workflows, particularly when working with Flux models. It's especially suitable for environments where resource efficiency is crucial but caption quality cannot be compromised.