jaidaken
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Includes 30 custom nodes committed directly, 7 Civitai-exclusive loras stored via Git LFS, and a setup script that installs all dependencies and downloads HuggingFace-hosted models on vast.ai. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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import torch
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import torch.nn as nn
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from typing import Type
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class MLPBlock(nn.Module):
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def __init__(
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self,
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embedding_dim: int,
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mlp_dim: int,
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act: Type[nn.Module] = nn.GELU,
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) -> None:
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super().__init__()
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self.lin1 = nn.Linear(embedding_dim, mlp_dim)
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self.lin2 = nn.Linear(mlp_dim, embedding_dim)
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self.act = act()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.lin2(self.act(self.lin1(x)))
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# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
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# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa
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class LayerNorm2d(nn.Module):
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def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
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super().__init__()
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self.weight = nn.Parameter(torch.ones(num_channels))
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self.bias = nn.Parameter(torch.zeros(num_channels))
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self.eps = eps
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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u = x.mean(1, keepdim=True)
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s = (x - u).pow(2).mean(1, keepdim=True)
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x = (x - u) / torch.sqrt(s + self.eps)
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x = self.weight[:, None, None] * x + self.bias[:, None, None]
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return x
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