What does this do?

This Python script is used to analyze embeddings trained with textual inversion using the Automatic1111 Web UI. When ran, it will create graphs of the loss rate of the embedding and the magnitude of its vectors. It's safe to run while the embedding is being trained.

Screenshots

Example of the script being run

It will output two images:

Image #1: TestEmbed-[step]-loss.jpg, which plots the loss rate from the textual_inversion_loss.csv file. In your A1111 settings, set the "Save an csv containing the loss to log directory every N steps, 0 to disable" setting to 1 for best results. Ideally you want a loss rate average to be less than 0.30.

Image #2: TestEmbed-[step]-vector.jpg, which plots the magnitude of each vector inside the embedding. The higher the magnitudes, the less flexible the the embedding will be to other words in a prompt.

In this example, the embedding had a learning rate of: 0.05:10, 0.02:20, 0.01:60, 0.005:200, 0.002:500, 0.001:3000, 0.0005

Since each token in an embedding has 768 vectors, this can add up to a lot of lines being plotted and end up in a jumbled mess. In the .py file you set VECTOR_GRAPH_CREATE_LIMITED_GRAPH = True and change the VECTOR_GRAPH_LIMITED_GRAPH_NUM_VECTORS variable to limit how many are plotted.

If all you want to do is inspect an existing embedding file that you downloaded from the internet, you can use --file EmbeddingName with the EmbeddingName.pt file next to the script.

Strength is simply an average value of the vectors (negative values are turned positive so the average doesn't always equal 0). In my experiences, a value greater than 0.2 is when the embedding starts to become inflexible. Your results will vary.

Magnitude measures the mathematical magnitude, which is the sqrt of the sum of squares.

How to use

• Download the script by clicking the green "Code" button up top and then Download ZIP.
• Unzip it
• Place the inspect_embedding_training.py file at "Stable Diffusion\textual_inversion\YYYY-MM-DD\YourEmbeddingName" in your Automatic1111 installation.
• If you have Python set to run .py files, just double click the file to run it. Otherwise, open a command window in the directory and type python inspect_embedding_training.py and hit Enter.

If you get the error ModuleNotFoundError: No module named 'torch' when running the script, open a console and run the command pip install torch and try again.

Configuration

Open the inspect_embedding_training.py file with notepad to edit some variables near the top of the file:

SAVE_LOSS_GRAPH_IMG: bool = True
SAVE_VECTOR_GRAPH_IMG: bool = True

SHOW_PLOTS_AFTER_GENERATION: bool = False

GRAPH_IMAGE_SIZE: tuple[int, int] = (19, 9)
GRAPH_SHOW_TITLE: bool = True

VECTOR_GRAPH_CREATE_FULL_GRAPH: bool = True
VECTOR_GRAPH_CREATE_LIMITED_GRAPH: bool = False
VECTOR_GRAPH_LIMITED_GRAPH_NUM_VECTORS: int = 100
VECTOR_GRAPH_SHOW_LEARNING_RATE: bool = True

EXPORT_FOLDER_EMBEDDING_TABLE_TO: str = None

Optional launch arguments

• --help, -h Shows help text.
• --dir The "/path/to/embedding/folder" to use instead of the local path where this script is at. This directory should have the textual_inversion_loss.csv file in it.
• --out The "/path/to/an/output/folder" to use instead of the local path for outputting images.
• --file The "EmbeddingName.pt" to inspect. Prints the embedding's: internal name, model name/hash, number of vectors per token, training step count, and average vector strength/magnitude
• --folder The "/path/to/a/folder/with/embeddings" to to inspect the strength/weight of multiple embeddings at once, similar to --file but for a whole folder.

Changelog

2/19/2023

• Added support for .bin embedding file format

2/04/2023

• Will load prompt_tuning_loss.csv if it exists, which is created when using DreamArtist

2/03/2023

• Will ignore negative embeddings "-neg.pt" that are created with the DreamArtist extension. Using --folder will still inspect them.

1/27/2023

• Added EXPORT_FOLDER_EMBEDDING_TABLE_TO config setting to export the table generated by the --folder launch argument to xlsx, csv, json, or html.

1/19/2023

• Fixed an error when rendering the limited vector graph (a bug introduced in the 1/13/2023 update)

1/17/2023

• Added --folder launch arg to to inspect the strength/weight of multiple embeddings at once, similar to --file but for a whole folder

1/13/2023

• Fixed the vector magnitude reporting the incorrect value for embeddings with more than 1 vector per token

1/01/2023

• Added --file launch arg to inspect an individual embedding file to get its internal info: internal name, model name/hash, number of vectors per token, training step count, and average vector strength/magnitude
• Now displays the average vector's strength/magnitude in the console and right hand side of the graph

12/28/2022

• Initial release

Special thanks

Shondoit - for supplying the base code for loading and graphing the data.