Update documentation
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@ -25,14 +25,11 @@ import torch.nn.functional as F
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class EveryDreamBatch(Dataset):
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class EveryDreamBatch(Dataset):
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"""
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"""
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data_root: root path of all your training images, will be recursively searched for images
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data_loader: `DataLoaderMultiAspect` object
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repeats: how many times to repeat each image in the dataset
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flip_p: probability of flipping the image horizontally
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debug_level: 0=none, 1=print drops due to unfilled batches on aspect ratio buckets, 2=debug info per image, 3=save crops to disk for inspection
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debug_level: 0=none, 1=print drops due to unfilled batches on aspect ratio buckets, 2=debug info per image, 3=save crops to disk for inspection
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batch_size: how many images to return in a batch
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conditional_dropout: probability of dropping the caption for a given image
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conditional_dropout: probability of dropping the caption for a given image
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resolution: max resolution (relative to square)
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crop_jitter: number of pixels to jitter the crop by, only for non-square images
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jitter: number of pixels to jitter the crop by, only for non-square images
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seed: random seed
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"""
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"""
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def __init__(self,
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def __init__(self,
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data_loader: DataLoaderMultiAspect,
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data_loader: DataLoaderMultiAspect,
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