sd-parsers 0.6

a library to read metadata from images created by Stable Diffusion

SD-Parsers

Read structured metadata from images created with stable diffusion.

Features

Prompts as well as some well-known generation parameters are provided as easily accessible properties (see Output).

Supports reading metadata from images generated with:

• Automatic1111's Stable Diffusion web UI
• ComfyUI *
• Fooocus
• InvokeAI
• NovelAI

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

Installation

pip install sd-parsers

Usage

From command line: python3 -m sd_parsers <filenames>.

Basic usage:

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

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

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:

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)

Parsing options:

Configure metadata extraction:

from sd_parsers import ParserManager, Eagerness

parser_manager = ParserManager(eagerness=Eagerness.EAGER)

Eagerness sets the metadata searching effort

The given eagerness level is the highest that will be considered.

i.e.: With Eagerness.DEFAULT set, the ParserManager will try FAST followed by DEFAULT.

For now, this only has an effect on PNG images:

• FAST: cut some corners to save some time

  This only looks at Image.info.

• DEFAULT: try to ensure all metadata is read

  This will also look at Image.text, reading the whole image data to do so.

• EAGER: include additional methods to try and retrieve metadata

  Includes the stenographic alpha extractor, which will look for hidden metadata. (computationally expensive!)

Only use specific (or custom) parser modules:

from sd_parsers import ParserManager
from sd_parsers.data import PromptInfo, Sampler
from sd_parsers.parsers import Parser, AUTOMATIC1111Parser

# basic implementation of a parser class
# see parsers/_dummy_parser.py for a more detailed explanation
class DummyParser(Parser):
    def parse(self, parameters):
        return PromptInfo(
            generator=self.generator,
            samplers=[Sampler(name="dummy_sampler", parameters={})],
            metadata={"some other": "metadata"},
            raw_parameters=parameters,
        )

# you can use multiple manager objects with different parsers
# caution: the order of parser entries matters!
# here, the DummyParser will ignore its input and always return a result,
# resulting in the AUTOMATIC1111 parser to never be used
parser_manager = ParserManager(managed_parsers=[DummyParser, AUTOMATIC1111Parser])

Change default parser modules:

from sd_parsers import ParserManager
from sd_parsers.parsers import MANAGED_PARSERS, AUTOMATIC1111Parser

# remove all preset parser modules
MANAGED_PARSERS.clear()

# add the AUTOMATIC1111 parser as only parser module
MANAGED_PARSERS.extend([AUTOMATIC1111Parser])

# the default will still be overriden with managed_parsers=...
parser_manager = ParserManager()

Add or change metadata extractors:

from PIL.Image import Image
from sd_parsers import ParserManager, Eagerness
from sd_parsers.data import Generators
from sd_parsers.extractors import METADATA_EXTRACTORS

# define a custom extractor
def custom_extractor(i: Image, g: Generators):
    return {"parameters": "custom extracted data\nSampler: UniPC, Steps: 15, CFG scale: 5"}

# remove all preset PNG extractors for the first (FAST) stage
METADATA_EXTRACTORS["PNG"][Eagerness.FAST].clear()

# add a custom extractor followed by the default FAST extractor
METADATA_EXTRACTORS["PNG"][Eagerness.FAST].append(custom_extractor)

parser_manager = ParserManager()

Output

The parse() method returns a PromptInfo (source) object when suitable metadata is found.

Use python3 -m sd_parsers <image.png> 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 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 found in the parsed metadata.

• negative_prompts: All negative prompts found in the parsed metadata.

• models: Models used in the image generation process.

• samplers: Samplers 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.

See 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