Florence-2-base-PromptGen
Maintainer: MiaoshouAI
45
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
Florence-2-base-PromptGen is an advanced image captioning model developed by MiaoshouAI. It is based on the Microsoft Florence-2 Model and fine-tuned for the specific task of generating high-quality image prompts and captions. The model was trained on a dataset of images and cleaned tags from Civitai, with the goal of improving the accuracy and formatting of prompts used to generate these images.
Model inputs and outputs
Florence-2-base-PromptGen is a text-to-text model, taking in a prompt as input and generating a detailed caption or prompt as output. The model supports several types of prompts, including <GENERATE_PROMPT>, <DETAILED_CAPTION>, and <MORE_DETAILED_CAPTION>.
Inputs
- Prompt: A text prompt that instructs the model to generate a detailed caption or prompt for an image.
Outputs
- Detailed caption: A comprehensive description of an image, formatted in a style similar to Danbooru tags.
Capabilities
Florence-2-base-PromptGen excels at generating detailed and accurate image prompts and captions. It is particularly well-suited for tasks like image captioning, prompt engineering, and data augmentation for training other computer vision models.
What can I use it for?
Florence-2-base-PromptGen can be used in a variety of applications, such as:
- Generating detailed captions for images to be used in datasets or training machine learning models.
- Automating the process of creating prompts for generative AI models like DALL-E or Stable Diffusion.
- Improving the tagging and captioning experience in tools like MiaoshouAI Tagger for ComfyUI.
Things to try
Experiment with different types of prompts to see how Florence-2-base-PromptGen responds. Try prompts that are more open-ended or specific, and observe how the model's output varies. You can also explore the model's performance on different types of images, such as real-world scenes, digital art, or abstract compositions.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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Florence-2-base-PromptGen is an advanced image captioning tool based on the Microsoft Florence-2 Model Base and fine-tuned by MiaoshouAI. It is trained on images and cleaned tags from Civitai to improve the tagging experience and accuracy of prompts used to generate these images. The model is a significant upgrade from previous versions, adding new caption instructions like and while improving accuracy. Model inputs and outputs Inputs Image**: An image to be captioned Outputs Detailed captions**: Descriptions of the image in varying levels of detail, including subject positions and text from the image Image tags**: Structured tags and prompts that can be used to recreate the image Capabilities Florence-2-base-PromptGen excels at generating high-quality, detailed image captions and tags. It can provide very granular descriptions of an image's contents, down to the positions of subjects and text within the frame. The model is also lightweight and memory-efficient, allowing for fast generation on modest hardware. What can I use it for? Florence-2-base-PromptGen is an ideal tool for improving the tagging and prompting workflow when training image generation models like those in the Flux ecosystem. It can eliminate the need to run separate tagging tools, boosting speed and efficiency. The model's detailed captions and tags can also be useful for other applications like visual search, image organization, and data annotation. Things to try Try experimenting with the different caption instructions like and to see how the level of detail in the output changes. You can also test the model's ability to read and incorporate text from the image into the captions. Finally, see how the generated tags and prompts perform when used to recreate the original image with a Flux-based generation model.
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Florence-2-base-PromptGen is an advanced image captioning model developed by MiaoshouAI. It is based on the Microsoft Florence-2 Model and fine-tuned for the specific task of generating high-quality image prompts and captions. The model was trained on a dataset of images and cleaned tags from Civitai, with the goal of improving the accuracy and formatting of prompts used to generate these images. Model inputs and outputs Florence-2-base-PromptGen is a text-to-text model, taking in a prompt as input and generating a detailed caption or prompt as output. The model supports several types of prompts, including `, , and `. Inputs Prompt**: A text prompt that instructs the model to generate a detailed caption or prompt for an image. Outputs Detailed caption**: A comprehensive description of an image, formatted in a style similar to Danbooru tags. Capabilities Florence-2-base-PromptGen excels at generating detailed and accurate image prompts and captions. It is particularly well-suited for tasks like image captioning, prompt engineering, and data augmentation for training other computer vision models. What can I use it for? Florence-2-base-PromptGen can be used in a variety of applications, such as: Generating detailed captions for images to be used in datasets or training machine learning models. Automating the process of creating prompts for generative AI models like DALL-E or Stable Diffusion. Improving the tagging and captioning experience in tools like MiaoshouAI Tagger for ComfyUI. Things to try Experiment with different types of prompts to see how Florence-2-base-PromptGen responds. Try prompts that are more open-ended or specific, and observe how the model's output varies. You can also explore the model's performance on different types of images, such as real-world scenes, digital art, or abstract compositions.
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