# Resolutions to train on, given as the side length of a square image. You can have multiple sizes here.
# !!!WARNING!!!: this might work differently to how you think it does. Images are first grouped to aspect ratio
# buckets, then each image is resized to ALL of the areas specified by the resolutions list. This is a way to do
# multi-resolution training, i.e. training on multiple total pixel areas at once. Your dataset is effectively duplicated
# as many times as the length of this list.
# If you just want to use predetermined (width, height, frames) size buckets, see the example cosmos_dataset.toml
# file for how you can do that.
resolutions = [512]

# You can give resolutions as (width, height) pairs also. This doesn't do anything different, it's just
# another way of specifying the area(s) (i.e. total number of pixels) you want to train on.
# resolutions = [[1280, 720]]

# Enable aspect ratio bucketing. For the different AR buckets, the final size will be such that
# the areas match the resolutions you configured above.
enable_ar_bucket = true

# The aspect ratio and frame bucket settings may be specified for each [[directory]] entry as well.
# Directory-level settings will override top-level settings.

# Min and max aspect ratios, given as width/height ratio.
min_ar = 0.5
max_ar = 2.0
# Total number of aspect ratio buckets, evenly spaced (in log space) between min_ar and max_ar.
num_ar_buckets = 7

# Can manually specify ar_buckets instead of using the range-style config above.
# Each entry can be width/height ratio, or (width, height) pair. But you can't mix them, because of TOML.
# ar_buckets = [[512, 512], [448, 576]]
# ar_buckets = [1.0, 1.5]

# For video training, you need to configure frame buckets (similar to aspect ratio buckets). There will always
# be a frame bucket of 1 for images. Videos will be assigned to the first frame bucket that the video is greater than or equal to in length.
# But videos are never assigned to the image frame bucket (1); if the video is very short it would just be dropped.
frame_buckets = [1, 33]
# If you have >24GB VRAM, or multiple GPUs and use pipeline parallelism, or lower the spatial resolution, you could maybe train with longer frame buckets
# frame_buckets = [1, 33, 65, 97]


[[directory]]
# Path to directory of images/videos, and corresponding caption files. The caption files should match the media file name, but with a .txt extension.
# A missing caption file will log a warning, but then just train using an empty caption.
path = 'input'
# How many repeats for 1 epoch. The dataset will act like it is duplicated this many times.
# The semantics of this are the same as sd-scripts: num_repeats=1 means one epoch is a single pass over all examples (no duplication).
num_repeats = 10
# Example of overriding some settings, and using ar_buckets to directly specify ARs.
# ar_buckets = [[448, 576]]
# resolutions = [[448, 576]]
# frame_buckets = [1]


# You can list multiple directories.

# [[directory]]
# path = '/home/anon/data/images/something_else'
# num_repeats = 5