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 <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)

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

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)

Output

The output returned from ParserManager is a PromptInfo object (as can be seen when executing python3 -m sd_parsers <image.png>) or None if no metadata was found.

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.

  Samplers act as the central data autorithy (see PromptInfo).

  A Sampler contains the following properties specific to itself:

  • sampler_id: A unique id of the sampler (if present in the metadata)
  • name: The name of the sampler
  • parameters: Generation parameters, including cfg_scale, seed, steps and others.
  • 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