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)
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.
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.
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.
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 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
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.
For style, consider adding a suffix on the caption that describes the style. Examples would be "by claude monet" or "in the style of gta box art" at the end of the caption. This will help the model learn recall style at inference time so you can style other subjects you did not train with the style. You may also consider "drawing of" or "painting of" at the start of the caption when appropriate.
Consider also including a style tag as above if you are training anything besides photos. For instance, if you are training a few characters from a video game you can consider "cloud strife holding a buster sword, screenshot from final fantasy for ps5" if you wish to capture the "style" of the game render along with the characters.
Include the surroundings and context in your captions. Ex. "cloud strife standing on a dirt path in midgar city slums district" Again, this will allow you to recall the "dirt path in midgar city slums district" style at inference time, and will even pick up on pieces of that like "midgar city" (if enough samples are present with similar words) as a style or scenery you can apply later. This can extract additional value from your training besides just the character.
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!
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.
