| # 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 | |