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Merge pull request #115 from qslug/config-fix 

Update docs around configuring captions and other image config
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# Data organization
# Selecting and preparing images
Since this trainer relies on having captions for your training images you will need to decide how you want deal with this.
## Number of images
There are two currently supported methods to retrieve captions:
You should probably start with less than 100 images, until you get a feel for training. When you are ready, ED2 supports up to tens of thousands of images.
1. Name the files with the caption. Underscore marks the end of the caption (ex. "john smith on a boat_999.jpg")
2. Put your captions for each image in a .txt file with the same name as the image. All UTF-8 text is supported with no reserved or special case characters. (ex. 00001.jpg, 00001.txt)
## Image size and quality
ED2 supports `.jpg`, `.jpeg`, `.png`, `.bmp`, `.webp`, and `.jfif` image formats.
You will need to place all your images and captions into a folder. Inside that folder, you can use subfolders to organize data as you please. The trainer will recursively search for images and captions. It may be useful, for instance, to split each character into a subfolder, and have other subfolders for cityscapes, etc.
Current recommendation is _at least_ 1 megapixel (ex 1024x1024, 1100x900, 1300x800, etc). That being said, technology continues to advance rapidly. ED2 has no problem handling 4K images, so it's up to you to pick the appropriate trade-off with disk and network costs.
When you train, you will use "--data_root" to point to the root folder of your data. All images in that folder and its subfolders will be used for training.
If you wish to boost training on a particular folder of images, put a "multiply.txt" in that folder with a whole number in it, such as 2. This will multiply the number of times images in that specific folder are used for training per epoch. This is useful if you have two characters you want to train, separated into separate folders, but one character has fewer images.
Scaling images up is not a great idea though, though it may be tolerable as a very small percentage of your data set. If you only have 512x512 images, don't try training at 768.
# Data preparation
Use high quality, in-focus, low-noise, images, capturing the concept(s) under training with high fidelity wherever possible.
## Image size
## Cropping and Aspect Ratios
The trainer will automatically fit your images to the best possible size. It is best to leave your images larger tham you may think for typical Stable Diffusion training. Even 4K images will be handled fine so just don't sweat it if you have large images. The only downside is they take a bit more disk space. There is almost no performance impact for having higher resolution images.
You can crop your images in an image editor __if it highlights the concept under training__, e.g. to get good close ups of things like faces, or to split images up that contain multiple characters.
Current recommendation is 1 megapixel (ex 1024x1024, 1100x900, 1300x800, etc) or larger, but thinking ahead to future technology advancements you may wish to keep them at even larger resolutions. Again, don't worry about the trainer squeezing or cropping, it will handle it!
**You do not need to crop to a square image**
Aspect ratios up to 4:1 or 1:4 are supported.
Aspect ratios between 4:1 and 1:4 are supported; the trainer will handle bucketing and resizing your images as needed.
## Cropping
It is ok to use a full shot of two characters in one image and also a cropped version of each character separately, but make sure every image is captioned appropriately for what is actually present in each image.
You can crop your images in an image editor *if you need, in order to get good close ups of things like faces, or to split images up that contain multiple characters.* As above, make sure **after** cropping your images are still fairly large. It is ok to use a full shot of two characters in one image and also a cropped version of each character separately, but make sure every image is captioned appropriately for what is actually present in each image.
## Captions
## Caption Design
A caption consists of a main prompt, followed by one or more comma-separated tags.
For most use cases, use a sane English sentence to describe the image. Try to put your character or main object name close to the start.
**If you are training on images of humans, there is little benefit in using "unique" names most of the time**. Don't worry so much about using a "rare" toking, or making up gibberish words. Just try generating a few images using your concept names, and make sure there are no serious conflicts.
Those training anime models can use booru tags as well using other utilities to generate the captions.
### Styles
@ -45,6 +47,68 @@ Include the surroundings and context in your captions. Ex. "cloud strife standi
Also consider some basic mention of pose. ex. "clouds strife sitting on a blue wooden bench in front of a concrete wall" or "barrett wallace holding his fist in front of his face with an angry look on his face, looking at the camera." Captions can capture value not only for the character's look, but also for the pose, the background scene, and the camera angle. You can be creative here, there is a lot of potential!
### Further reading
The [Data Balancing](BALANCING.md) guide has some more information on how to balance your data and what to consider for model preservation and mixing in ground truth data.
# Constructing a dataset
A dataset consists of image files coupled to captions and other configuration.
You are welcome to use any folder structure that makes sense for your project, but you should know that there are configuration tricks that rely on data being partitioned into folders and subfolders.
## Assigning captions
### by Filename
The simplest and least powerful way to caption images is by filename. The name of the file, without extension, and excluding any characters after an _ (underscore).
```
a photo of ted bennet, sitting on a green armchair_1.jpg
a photo of ted bennet, laying down_1.jpg
a photo of ted bennet, laying down_2.jpg
```
### by Caption file
If you are running in a Windows environment, you may not be able to fit your whole caption in the file name.
Instead you can create a text file with the same name as your image file, but with a `.txt` or `.caption` extension, and the content of the text file will be used as the caption, **ignoring the name of the file**.
### by Caption yaml
You can capture a more complex caption structure by using a `.yaml` sidecar instead of a text file. Specifically you can assign weights to tags for [shuffling](SHUFFLING_TAGS.md).
The format for `.yaml` captions:
```
main_prompt: a photo of ted bennet
tags:
- "sitting on a green armchair" # The tag can be a simple string
- tag: "wearing a tuxedo" # or it can be a tag string
weight: 1.5 # optionally paired with a shuffle weight
```
### Assigning captions to entire folders
As mentioned above, a caption is a main prompt accompanied by zero or more tags.
Currently it is not possible for a caption to have more than one main tag, although this limitation may be removed in the future.
But, in some cases it may make sense to add the same tag to all images in a folder. You can place any configuration that should apply to all images in a local folder (ignoring anything in any subfolders) by adding a file called `local.yaml` to the folder. In this file you can, for example, add:
```
tags:
- tag: "in the style of xyz"
```
And this tag will be appended to any tags specified at the image level.
If you want this tag, or any other configuration, to to apply to images in subfolders as well you can create a file called `global.yaml` and it will apply to all images in the local folder **and** to any images in any subfolders, recursively.
## Other image configuration
In addition to captions, you can also specify the frequency with which each image should show up in training (`multiply`), or the frequency in which the trainer should be given a flipped version of the image (`flip_p`), or the frequency in which the caption should be dropped completely focusing the training on the image alone, ignoring the caption (`cond_dropout`).
For simple cases you can create a file called `multiply.txt`, `flip_p.txt`, and/or `cond_dropout.txt`, containing the single numeric value for that configuration parameter that should be applied to all images in the local folder.
Alternatively you can add these properties to any of the supported `.yaml` configuration files, image-level, `local.yaml`, and/or `global.yaml`
```
main_prompt: a photo of ted bennet
tags:
- sitting on a green armchair
multiply: 2
flip_p: 0.5
cond_droput: 0.05
```
See [Advanced Tweaking](ATWEAKING.md) for more information on image flipping and conditional dropout.
The [Data Balancing](BALANCING.md) guide has some more information on how to balance your data using multipliers, and what to consider for model preservation and mixing in ground truth data.