From 0b7b09fa2fee9e3a3e6936de2a13b7f7e8a34468 Mon Sep 17 00:00:00 2001 From: Extraltodeus Date: Mon, 12 Aug 2024 21:41:44 +0200 Subject: [PATCH] Update nodes.py --- nodes.py | 20 -------------------- 1 file changed, 20 deletions(-) diff --git a/nodes.py b/nodes.py index 69173e2..30f2a7b 100644 --- a/nodes.py +++ b/nodes.py @@ -1081,26 +1081,6 @@ def generate_gradient_mask(tensor, horizontal=False): merging_gradient = gradient.unsqueeze(1).repeat(tensor.size(0), tensor.size(1), 1, tensor.size(3)) return merging_gradient -@torch.no_grad() -def random_swap(tensors, proportion=1): - # torch.manual_seed(seed) - num_tensors = tensors.shape[0] - tensor_size = tensors[0].numel() - - true_count = int(tensor_size * proportion) - mask = torch.cat((torch.ones(true_count, dtype=torch.bool, device=tensors[0].device), - torch.zeros(tensor_size - true_count, dtype=torch.bool, device=tensors[0].device))) - mask = mask[torch.randperm(tensor_size)].reshape(tensors[0].shape) - if num_tensors == 2 and proportion < 1: - index_tensor = torch.ones_like(tensors[0], dtype=torch.int64, device=tensors[0].device) - else: - index_tensor = torch.randint(1 if proportion < 1 else 0, num_tensors, tensors[0].shape, device=tensors[0].device) - for i, t in enumerate(tensors): - if i == 0: continue - merge_mask = index_tensor == i & mask - tensors[0][merge_mask] = t[merge_mask] - return tensors[0],true_count - class gradient_scaling_pre_cfg_node: @classmethod def INPUT_TYPES(s):