2024-08-04 15:45:43 -04:00
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import torch
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2025-04-14 18:00:33 -04:00
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import comfy.rmsnorm
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2024-08-04 15:45:43 -04:00
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def pad_to_patch_size(img, patch_size=(2, 2), padding_mode="circular"):
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2024-11-27 13:45:32 -05:00
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if padding_mode == "circular" and (torch.jit.is_tracing() or torch.jit.is_scripting()):
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2024-08-04 15:45:43 -04:00
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padding_mode = "reflect"
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2024-12-13 07:21:52 -05:00
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pad = ()
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for i in range(img.ndim - 2):
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pad = (0, (patch_size[i] - img.shape[i + 2] % patch_size[i]) % patch_size[i]) + pad
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return torch.nn.functional.pad(img, pad, mode=padding_mode)
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2024-08-28 16:18:39 -04:00
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2025-04-14 18:00:33 -04:00
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rms_norm = comfy.rmsnorm.rms_norm
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