ImagePromptHelper-danube3-500M
| Property | Value |
|---|---|
| Parameter Count | 514M |
| Base Model | h2o-danube3-500m-base |
| License | Apache-2.0 |
| Format | BF16 |
What is ImagePromptHelper-danube3-500M?
ImagePromptHelper-danube3-500M is a specialized language model designed to enhance and generate detailed image prompts. Built on the h2o-danube3-500m-base architecture, this model has been fine-tuned on four carefully curated datasets focused on image descriptions and stable diffusion prompts. It offers multiple prompt styles including Detailed, Very detailed, Danbooru, and Mixed formats.
Implementation Details
The model leverages transformer architecture and is trained using the Hugging Face transformers library. It's available in both standard and quantized GGUF formats, making it accessible through both Hugging Face and Ollama platforms. The model builds upon the work of MiaoshouAI's Florence-2-large-PromptGen-v1.5 for its SFT dataset creation.
- Multiple prompt styling options with distinct outputs
- 514M parameter efficient architecture
- BF16 tensor format for optimal performance
- Available in multiple deployment formats (standard and GGUF)
Core Capabilities
- Generation of detailed artistic descriptions
- Support for multiple prompt styles (Detailed, Very detailed, Danbooru, Mixed)
- Natural language understanding and enhancement
- Context-aware image prompt generation
- Integration with popular image generation workflows
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to generate different styles of prompts using simple prefixes (Detailed:, Very detailed:, Danbooru:, Mixed:) makes it versatile for various image generation needs. Its relatively small size of 514M parameters makes it efficient while maintaining high-quality outputs.
Q: What are the recommended use cases?
The model is ideal for artists and creators working with image generation AI systems, helping them create more detailed and specific prompts. It's particularly useful for generating Danbooru-style tags, detailed scene descriptions, and mixed-format prompts for various artistic applications.
