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: about 1 year ago
JSON representation
A Python library to read metadata from images created by Stable Diffusion.
- Host: GitHub
- URL: https://github.com/d3x-at/sd-parsers
- Owner: d3x-at
- License: mit
- Created: 2023-04-02T20:21:02.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2025-04-03T18:22:13.000Z (about 1 year ago)
- Last Synced: 2025-04-13T11:09:33.663Z (about 1 year ago)
- Topics: automatic1111, comfyui, fooocus, invokeai, metadata-extraction, novelai, parse, parser, stable-diffusion, stable-diffusion-library
- Language: Python
- Homepage:
- Size: 749 KB
- Stars: 33
- Watchers: 1
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# 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](#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 ```
### 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)
```
### Parsing options:
#### Configure metadata extraction:
```python
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](src/sd_parsers/extractors/_png_stenographic_alpha.py) extractor, which will look for hidden metadata. (computationally expensive!)
#### Only use specific (or custom) parser modules:
```python
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 instances using 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:
```python
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:
```python
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 custom extractor
METADATA_EXTRACTORS["PNG"][Eagerness.FAST].append(custom_extractor)
parser_manager = ParserManager()
```
### 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