HunyuanCaptioner
H
Hunyuancaptioner
Overview :
HunyuanCaptioner is a text-to-image technology model based on LLaVA, capable of generating highly accurate text descriptions for images, including object descriptions, object relationships, background information, and image style. It supports both Chinese and English single-image and multi-image reasoning, and can be locally demonstrated through Gradio.
Target Users :
This model is designed for enterprises and developers who need image description generation services, such as image recognition, content creation, and social media. It can help them quickly generate descriptions highly consistent with image content, improving work efficiency and user experience.
Total Visits: 29.7M
Top Region: US(17.94%)
Website Views : 49.7K
Use Cases
Automatic generation of image content descriptions for social media platforms
Providing detailed descriptions for product images for e-commerce platforms
Content creators adding descriptions to images in blogs or articles
Features
Supports Chinese and English image description generation
Can generate descriptions from multiple angles, such as objects, relationships, backgrounds, and styles
Based on LLaVA, ensuring technological advancement
Supports single-image and multi-image reasoning
Demonstrable locally through Gradio, convenient for user testing and experience
Provides detailed instructions for downloading the model and installing dependencies
How to Use
1. Install Dependencies: Follow the dependency installation guide provided on the page.
2. Download the Model: Download the HunyuanCaptioner model using the huggingface-cli tool.
3. Perform Single-Image Reasoning: Select Chinese or English mode, enter the image path and model path, and execute reasoning.
4. Perform Multi-Image Reasoning: Convert multiple images to a csv file, and then use the provided script for batch reasoning.
5. Start Gradio Demonstration: Follow the page instructions to start the local Gradio demonstration and experience the model's functionality.
6. Convert the output results to Arrow format as needed for further processing or analysis.
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