Files
InvokeAI/invokeai/backend/flux/controlnet/controlnet_flux_output.py

59 lines
2.1 KiB
Python

from dataclasses import dataclass
import torch
@dataclass
class ControlNetFluxOutput:
single_block_residuals: list[torch.Tensor] | None
double_block_residuals: list[torch.Tensor] | None
def apply_weight(self, weight: float):
if self.single_block_residuals is not None:
for i in range(len(self.single_block_residuals)):
self.single_block_residuals[i] = self.single_block_residuals[i] * weight
if self.double_block_residuals is not None:
for i in range(len(self.double_block_residuals)):
self.double_block_residuals[i] = self.double_block_residuals[i] * weight
def add_tensor_lists_elementwise(
list1: list[torch.Tensor] | None, list2: list[torch.Tensor] | None
) -> list[torch.Tensor] | None:
"""Add two tensor lists elementwise that could be None."""
if list1 is None and list2 is None:
return None
if list1 is None:
return list2
if list2 is None:
return list1
new_list: list[torch.Tensor] = []
for list1_tensor, list2_tensor in zip(list1, list2, strict=True):
new_list.append(list1_tensor + list2_tensor)
return new_list
def add_controlnet_flux_outputs(
controlnet_output_1: ControlNetFluxOutput, controlnet_output_2: ControlNetFluxOutput
) -> ControlNetFluxOutput:
return ControlNetFluxOutput(
single_block_residuals=add_tensor_lists_elementwise(
controlnet_output_1.single_block_residuals, controlnet_output_2.single_block_residuals
),
double_block_residuals=add_tensor_lists_elementwise(
controlnet_output_1.double_block_residuals, controlnet_output_2.double_block_residuals
),
)
def sum_controlnet_flux_outputs(
controlnet_outputs: list[ControlNetFluxOutput],
) -> ControlNetFluxOutput:
controlnet_output_sum = ControlNetFluxOutput(single_block_residuals=None, double_block_residuals=None)
for controlnet_output in controlnet_outputs:
controlnet_output_sum = add_controlnet_flux_outputs(controlnet_output_sum, controlnet_output)
return controlnet_output_sum