Concepts

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The Concepts tab is where you tell OneTrainer where your inputs are. Concepts can be your train data, regularization data or any other data you want to train on.

Concepts Tab UI Overview

The Concepts tab is made up of the following elements:

Dropdown menu (default: concepts)

You can setup multiple configs for your concepts, and the dropdown is how you select them. OneTrainer only trains from the current selected config.

Add config

Pressing this button will bring up an UI element to ask for a name to create a new concepts config

Add concept

Pressing this button will create a new default blank concept in the current config

Delete concept (red X)

Pressing this button on a concept will delete it from the config

Duplicate concept (green plus)

Pressing this button will duplicate this concept, including all settings

Enable concept (toggle, default: on)

This sliding toggle will tell OneTrainer to train using this concept or not. The toggle is blue when enabled.

Edit concept

Clicking on any part of the concept image, outside of the buttons and toggles, will open the concept settings window. This is a separate window where all the rest of the concept settings can be modified, and is the next part of this wiki section.

Concepts Settings - General

The general tab of the Concept focuses on the basic info, balancing and caching settings.

Name (Default: Blank)

This field allows you to enter a name for your concept. If you do not choose a name, it will default to the folder name you enter when you close the window.

Enabled (Default: True)

A toggle that will mirror the toggle on the tab itself. This toggle controls if OneTrainer will train on this data or not.

Path (Default: Blank)

This field will allow you to type or paste the path the location to your concept images. You can also the button next to the field (...) to browse for the folder instead.

Prompt Source (Default: from text file per sample)

This dropdown has three options.

• From text file per sample - 0001.jpg will use 0001.txt file as the prompt
• From single text file - the text file in the field to the right of the dropdown will be used for all images
• From image file name - tag1 tag2 tag3.jpg will use tag1 tag2 tag3 as the prompt

Include Subdirectories (Default: False)

This toggle will allow you to use subdirectories for ease of your use, but have OneTrainer treat them as one concept for internal management

Image Variations (Default: 1)

This field controls how many images will be cached using the variables from the image augmentation tab.

Text Variations (Default: 1)

This field controls how many prompts will be cached for each image. Note: If you are training the text encoder, prompts are not cached and this setting does not need to be changed.

Balancing (Default: 1 Repeats)

These two fields control the balancing for the concept. Balancing allows you to, as the name suggests, balance out one concept amongst others. One use case for this is regularization images. If you have 100 source images, but have 10,000 reg images, you can use balancing to train only a fraction of the images every epoch. There are two ways to use this setting.

• Repeats - Your source images times the value will be used every epoch. For example, if you have 10,000 images and use .01, 100 images will be used every epoch.
• Sample - Explicitly tells OneTrainer how many images to use each epoch. For example. Using 100 samples will always use 100 images per epoch. This is true if you have 10,000 images or 20.

Loss Weight (Default: 1)

Another technique to balance your inputs. One use case for this is using a value less than 1 for reg images if you find they are affecting the training run more than you would like.

Image Augmentation Tab

This tab focuses on image augmentation to help diversify your image set. This tends to become more important as your input size becomes smaller.

Original Concepts Wiki Data

That's the place to put your data set.

For a single run you can simply add concepts and edit them with a link to your dataset (with or without captions) then select the prompt source (single text file if you don't use any caption, from text file per sample if you set text files for the caption or from image name it you put the caption in the image name). When working on multiple runs and subject to train, you can create a configuration (config) and add concept(s) to it. You'll be able to call it again later.

Note for embedding and additional embeddings, you need to include the embedding placeholder (defaulted to <embedding>) in the caption, it can be <embedding> or any other word(s) you want. Either in the image caption or in the single text file. Yes, it can be several words separated by spaces, so with a caption like "Blurry photograph of a man wearing a military suit", you could use 'Blurry photograph", "man" and "military suit" as placeholders for 3 embeddings.

Concept options.

You can use the default settings or enable image variations to enhance the training quality.

Each concept has four options:

• Image variations: Specifies the number of differently cached image versions of a concept.
• Text variations: The same for the text. This only applies if the text encoder is not trained.
• Balancing: With this option you can adjust the number of images of a concept that are included in each epoch. Either use Repeats as a multiplier of the concept (it can be less than 1) or Samples to specify the exact number of images used per epoch (it can be higher than the number of images in the concept).
• Loss Weight: This is a multiplier for the loss for this concept.

Here is an example:

• Concept A: 10 images, 10 image variations, 5.0 repeats
• Concept B: 100 images, 2 image variations, 1.0 repeats
• Concept C: 5000 images, 1 image variations, 0.1 repeats

Concept A will be cached 10 times, concept B twice, and concept C only once. This is similar to the latent caching epochs, but adjustable for each concept. The latent caching epochs have been removed. Each epoch is trained on 50 images from concept A, 100 images from concept B, and 500 images from concept C.

Some additional remarks:

• Resolution override for each concept. You can set the resolution individually for each concept.
• Fixed image augmentations: instead of using random numbers, you can specify fixed values for image augmentations.
• Enabling or disabling of concepts. Each concept can be disabled individually.
• You can now add single concepts to the training run without caching the entire dataset again. Only the new concept will be cached.
• Loss weight for each concept. This can be useful for things like regularization images that should not be trained as regular training images.
• Text Variations can be either tag shuffle (see option) or multi-line caption files. As noted, this is required when not training the text encoder.