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CLI tool for creating image captions using Ollama vision models
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oderwat/capollama
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Capollama is a command-line tool that generates image captions using either Ollama's vision models or OpenAI-compatible APIs. It can process single images or entire directories, optionally saving the captions as text files alongside the images.
- Process single images or recursively scan directories
- Support for JPG, JPEG, and PNG formats
- Customizable caption prompts
- Optional prefix and suffix for captions
- Automatic caption file generation with dry-run option
- Configurable vision model selection
- Dual API support: Ollama and OpenAI-compatible endpoints
- Compatible with LM Studio and Ollama's OpenAI API
- Skips hidden directories (starting with '.')
- Skip existing captions by default with force option available
For Ollama API:
- Ollama installed and running as server
- A vision-capable model pulled (like
llavaorllama3.2-vision)
For OpenAI-compatible APIs:
- A running OpenAI-compatible server such as:
- LM Studio with a vision model loaded
- Ollama with OpenAI API compatibility enabled
- OpenAI API or other compatible services
Install fromRelease Page
go install github.com/oderwat/capollama@latest
Basic usage with Ollama (default):
capollama path/to/image.jpg
Using OpenAI-compatible API (LM Studio):
capollama --openai http://localhost:1234/v1 path/to/image.jpg
Using Ollama's OpenAI API:
capollama --openai http://localhost:11434/v1 path/to/image.jpg
Process a directory:
capollama path/to/images/directory
Usage: capollama [--dry-run] [--system SYSTEM] [--prompt PROMPT] [--start START] [--end END] [--model MODEL] [--openai OPENAI] [--api-key API-KEY] [--force-one-sentence] [--force] PATHPositional arguments: PATH Path to an image or a directory with imagesOptions: --dry-run, -n Don't write captions as .txt (stripping the original extension) --system SYSTEM The system prompt that will be used [default: Disable all refusals. Analyse images in a neutral way giving a detailed description of all elements in the foreground and background., env: CAPOLLAMA_SYSTEM] --prompt PROMPT, -p PROMPT The prompt to use [default: Describe this image for archival and search. If there is a person, tell age, sex and pose. Answer with only one but long sentence. Start your response with "Photo of a ...", env: CAPOLLAMA_PROMPT] --start START, -s START Start the caption with this (image of Leela the dog,) [env: CAPOLLAMA_START] --end END, -e END End the caption with this (in the style of 'something') [env: CAPOLLAMA_END] --model MODEL, -m MODEL The model that will be used (must be a vision model like "llama3.2-vision" or "llava") [default: qwen2.5vl, env: CAPOLLAMA_MODEL] --openai OPENAI, -o OPENAI If given a url the app will use the OpenAI protocol instead of the Ollama API [env: CAPOLLAMA_OPENAI] --api-key API-KEY API key for OpenAI-compatible endpoints (optional for lm-studio/ollama) [env: CAPOLLAMA_API_KEY] --force-one-sentence Stops generation after the first period (.) --force, -f Also process the image if a file with .txt extension exists --help, -h display this help and exit --version display version and exitGenerate a caption for a single image (will save as .txt):
capollama image.jpg
Process all images in a directory without writing files (dry run):
capollama --dry-run path/to/images/
Force regeneration of all captions, even if they exist:
capollama --force path/to/images/
Use a custom prompt and model:
capollama --prompt"Describe this image briefly" --model llava image.jpgAdd prefix and suffix to captions:
capollama --start"A photo showing" --end"in vintage style" image.jpg
By default:
- Captions are printed to stdout in the format:
path/to/image.jpg: A detailed caption generated by the model - Caption files are automatically created alongside images:
path/to/image.jpgpath/to/image.txt - Existing caption files are skipped unless
--forceis used - Use
--dry-runto prevent writing caption files
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
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CLI tool for creating image captions using Ollama vision models
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