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"""
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This file is part of ComfyUI.
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Copyright (C) 2024 Stability AI
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <https://www.gnu.org/licenses/>.
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"""
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import torch
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import comfy.model_management
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def cast_bias_weight(s, input):
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bias = None
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non_blocking = comfy.model_management.device_should_use_non_blocking(input.device)
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if s.bias is not None:
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bias = s.bias.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)
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if s.bias_function is not None:
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bias = s.bias_function(bias)
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weight = s.weight.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)
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if s.weight_function is not None:
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weight = s.weight_function(weight)
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return weight, bias
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class CastWeightBiasOp:
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comfy_cast_weights = False
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weight_function = None
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bias_function = None
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class disable_weight_init:
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class Linear(torch.nn.Linear, CastWeightBiasOp):
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def reset_parameters(self):
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return None
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def forward_comfy_cast_weights(self, input):
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weight, bias = cast_bias_weight(self, input)
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return torch.nn.functional.linear(input, weight, bias)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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class Conv1d(torch.nn.Conv1d, CastWeightBiasOp):
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def reset_parameters(self):
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return None
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def forward_comfy_cast_weights(self, input):
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weight, bias = cast_bias_weight(self, input)
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return self._conv_forward(input, weight, bias)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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class Conv2d(torch.nn.Conv2d, CastWeightBiasOp):
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def reset_parameters(self):
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return None
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def forward_comfy_cast_weights(self, input):
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weight, bias = cast_bias_weight(self, input)
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return self._conv_forward(input, weight, bias)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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class Conv3d(torch.nn.Conv3d, CastWeightBiasOp):
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def reset_parameters(self):
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return None
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def forward_comfy_cast_weights(self, input):
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weight, bias = cast_bias_weight(self, input)
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return self._conv_forward(input, weight, bias)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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class GroupNorm(torch.nn.GroupNorm, CastWeightBiasOp):
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def reset_parameters(self):
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return None
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def forward_comfy_cast_weights(self, input):
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weight, bias = cast_bias_weight(self, input)
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return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp):
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def reset_parameters(self):
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return None
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def forward_comfy_cast_weights(self, input):
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if self.weight is not None:
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weight, bias = cast_bias_weight(self, input)
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else:
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weight = None
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bias = None
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return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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class ConvTranspose2d(torch.nn.ConvTranspose2d, CastWeightBiasOp):
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def reset_parameters(self):
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return None
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def forward_comfy_cast_weights(self, input, output_size=None):
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num_spatial_dims = 2
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output_padding = self._output_padding(
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input, output_size, self.stride, self.padding, self.kernel_size,
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num_spatial_dims, self.dilation)
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weight, bias = cast_bias_weight(self, input)
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return torch.nn.functional.conv_transpose2d(
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input, weight, bias, self.stride, self.padding,
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output_padding, self.groups, self.dilation)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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class ConvTranspose1d(torch.nn.ConvTranspose1d, CastWeightBiasOp):
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def reset_parameters(self):
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return None
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def forward_comfy_cast_weights(self, input, output_size=None):
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num_spatial_dims = 1
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output_padding = self._output_padding(
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input, output_size, self.stride, self.padding, self.kernel_size,
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num_spatial_dims, self.dilation)
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weight, bias = cast_bias_weight(self, input)
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return torch.nn.functional.conv_transpose1d(
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input, weight, bias, self.stride, self.padding,
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output_padding, self.groups, self.dilation)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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@classmethod
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def conv_nd(s, dims, *args, **kwargs):
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if dims == 2:
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return s.Conv2d(*args, **kwargs)
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elif dims == 3:
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return s.Conv3d(*args, **kwargs)
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else:
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raise ValueError(f"unsupported dimensions: {dims}")
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class manual_cast(disable_weight_init):
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class Linear(disable_weight_init.Linear):
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comfy_cast_weights = True
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class Conv1d(disable_weight_init.Conv1d):
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comfy_cast_weights = True
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class Conv2d(disable_weight_init.Conv2d):
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comfy_cast_weights = True
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class Conv3d(disable_weight_init.Conv3d):
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comfy_cast_weights = True
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class GroupNorm(disable_weight_init.GroupNorm):
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comfy_cast_weights = True
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class LayerNorm(disable_weight_init.LayerNorm):
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comfy_cast_weights = True
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class ConvTranspose2d(disable_weight_init.ConvTranspose2d):
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comfy_cast_weights = True
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class ConvTranspose1d(disable_weight_init.ConvTranspose1d):
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comfy_cast_weights = True
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