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https://github.com/d3x-at/sd-parsers

A Python library to read metadata from images created by Stable Diffusion.
https://github.com/d3x-at/sd-parsers

automatic1111 comfyui fooocus invokeai metadata-extraction novelai parse parser stable-diffusion stable-diffusion-library

Last synced: 5 days ago
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A Python library to read metadata from images created by Stable Diffusion.

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README 

        
## Features



Supports reading metadata from images generated with:

* Automatic1111's Stable Diffusion web UI

* ComfyUI *

* Fooocus

* InvokeAI

* NovelAI



Provides a list of prompts used in the generation of the image, as well as generator-specific metadata.



\* Custom ComfyUI nodes might parse incorrectly / with incomplete data.



## Installation

```

pip install sd-parsers

```



## Usage



From command line: ```python3 -m sd_parsers ```.



### Basic usage:



For a simple query, import ```ParserManager``` from ```sd_parsers``` and use its ```parse()``` method to parse an image. (see [examples](examples))



#### Read prompt information from a given filename with `parse()`:

```python

from sd_parsers import ParserManager



parser_manager = ParserManager()



def main():

    prompt_info = parser_manager.parse("image.png")



    if prompt_info:

        for prompt in prompt_info.prompts:

            print(f"Prompt: {prompt.value}")

```



#### Read prompt information from an already opened image:

```python

from PIL import Image

from sd_parsers import ParserManager



parser_manager = ParserManager()



def main():

    with Image.open('image.png') as image:

        prompt_info = parser_manager.parse(image)

```



#### Each parser module can also be used directly, omitting the use of ```ParserManager```:



```python

from PIL import Image

from sd_parsers.data import PromptInfo

from sd_parsers.exceptions import ParserError

from sd_parsers.parsers import AUTOMATIC1111Parser



parser = AUTOMATIC1111Parser()



def main():

    try:

        with Image.open("image.png") as image:

            # read_parameters() returns relevant image metadata parameters

            # and optional context information needed for parsing

            parameters, parsing_context = parser.read_parameters(image)



        # parse() builds a standardized data structure from the raw parameters

        generator, samplers, metadata = parser.parse(parameters, parsing_context)



    except ParserError:

        ...



    # creating a PromptInfo object from the obtained data allows for the use

    # of convenience poperties like ".prompts" or ".models"

    prompt_info = PromptInfo(generator, samplers, metadata, parameters)

```



### Output

The `parse()` method returns a `PromptInfo` ([source](src/sd_parsers/data/prompt_info.py)) object when suitable metadata is found.



> Use ```python3 -m sd_parsers ``` to get an idea of the data parsed from an image file.



> To get a result in JSON form, an approach as demonstrated in https://github.com/d3x-at/sd-parsers-web can be used.



`PromptInfo` contains the following properties :

* `generator`: Specifies the image [generator](src/sd_parsers/data/generators.py) that may have been used for creating the image.



* `full_prompt`: A full prompt, if present in the image metadata.



  Otherwise, a simple concatenation of all prompts found.



* `full_negative_prompt`: A full negative prompt if present in the image metadata. 

  

  Otherwise, a simple concatenation of all negative prompts found.



* `prompts`: All [prompts](src/sd_parsers/data/prompt.py) found in the parsed metadata.



* `negative_prompts`: All negative [prompts](src/sd_parsers/data/prompt.py) found in the parsed metadata.



* `models`: [Models](src/sd_parsers/data/model.py) used in the image generation process.



* `samplers`: [Samplers](src/sd_parsers/data/sampler.py) used in the image generation process.



  A Sampler contains the following properties specific to itself:

    * `name`: The name of the sampler



    * `parameters`: Generation parameters, including _cfg_scale_, _seed_, _steps_ and others.



    * `sampler_id`: A unique id of the sampler (if present in the metadata)



    * `model`: The model used by this sampler.



    * `prompts`: A list of positive prompts used by this sampler.

    

    * `negative_prompts`: A list of negative prompts used by this sampler.



* `metadata`: Additional metadata which could not be attributed to one of the former described.



  Highly dependent on the provided data structure of the respective image generator.



* `raw_parameters`: The unprocessed metadata entries as found in the parsed image (if present).



## Contributing

As i don't have the time and resources to keep up with all the available AI-based image generators out there, the scale and features of this library is depending greatly on your help.



If you find the sd-parsers library unable to read metadata from an image, feel free to open an [issue](https://github.com/d3x-at/sd-parsers/issues).



See [CONTRIBUTING.md](https://github.com/d3x-at/sd-parsers/blob/master/.github/CONTRIBUTING.md), if you are willing to help with improving the library itself and/or to create/maintain an additional parser module.



## Credits

Idea and motivation using AUTOMATIC1111's stable diffusion webui

- https://github.com/AUTOMATIC1111/stable-diffusion-webui



Example workflows for testing the ComfyUI parser

- https://github.com/comfyanonymous/ComfyUI_examples