nodes/Extraltodeus-pre_cfg_comfy_nodes_for_ComfyUI
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Update nodes.py

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Extraltodeus
2024-08-12 21:41:44 +02:00
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parent 55200fcd76
commit 0b7b09fa2f
1 changed files with 0 additions and 20 deletions
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nodes.py
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@@ -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):