models/flowsep/diffusers/pipelines/controlnet/multicontrolnet.py

from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin


class MultiControlNetModel(ModelMixin):
    r"""
    Multiple `ControlNetModel` wrapper class for Multi-ControlNet

    This module is a wrapper for multiple instances of the `ControlNetModel`. The `forward()` API is designed to be
    compatible with `ControlNetModel`.

    Args:
        controlnets (`List[ControlNetModel]`):
            Provides additional conditioning to the unet during the denoising process. You must set multiple
            `ControlNetModel` as a list.
    """

    def __init__(self, controlnets: Union[List[ControlNetModel], Tuple[ControlNetModel]]):
        super().__init__()
        self.nets = nn.ModuleList(controlnets)

    def forward(
        self,
        sample: torch.FloatTensor,
        timestep: Union[torch.Tensor, float, int],
        encoder_hidden_states: torch.Tensor,
        controlnet_cond: List[torch.tensor],
        conditioning_scale: List[float],
        class_labels: Optional[torch.Tensor] = None,
        timestep_cond: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        guess_mode: bool = False,
        return_dict: bool = True,
    ) -> Union[ControlNetOutput, Tuple]:
        for i, (image, scale, controlnet) in enumerate(zip(controlnet_cond, conditioning_scale, self.nets)):
            down_samples, mid_sample = controlnet(
                sample,
                timestep,
                encoder_hidden_states,
                image,
                scale,
                class_labels,
                timestep_cond,
                attention_mask,
                cross_attention_kwargs,
                guess_mode,
                return_dict,
            )

            # merge samples
            if i == 0:
                down_block_res_samples, mid_block_res_sample = down_samples, mid_sample
            else:
                down_block_res_samples = [
                    samples_prev + samples_curr
                    for samples_prev, samples_curr in zip(down_block_res_samples, down_samples)
                ]
                mid_block_res_sample += mid_sample

        return down_block_res_samples, mid_block_res_sample