InvokeAI/invokeai/backend/stable_diffusion/seamless.py

from contextlib import contextmanager
from typing import Callable, List, Optional, Tuple, Union

import torch
import torch.nn as nn
from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
from diffusers.models.lora import LoRACompatibleConv
from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel


@contextmanager
def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL, AutoencoderTiny], seamless_axes: List[str]):
    if not seamless_axes:
        yield
        return

    # override conv_forward
    # https://github.com/huggingface/diffusers/issues/556#issuecomment-1993287019
    def _conv_forward_asymmetric(self, input: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None):
        self.paddingX = (self._reversed_padding_repeated_twice[0], self._reversed_padding_repeated_twice[1], 0, 0)
        self.paddingY = (0, 0, self._reversed_padding_repeated_twice[2], self._reversed_padding_repeated_twice[3])
        working = torch.nn.functional.pad(input, self.paddingX, mode=x_mode)
        working = torch.nn.functional.pad(working, self.paddingY, mode=y_mode)
        return torch.nn.functional.conv2d(
            working, weight, bias, self.stride, torch.nn.modules.utils._pair(0), self.dilation, self.groups
        )

    original_layers: List[Tuple[nn.Conv2d, Callable]] = []

    try:
        x_mode = "circular" if "x" in seamless_axes else "constant"
        y_mode = "circular" if "y" in seamless_axes else "constant"

        conv_layers: List[torch.nn.Conv2d] = []

        for module in model.modules():
            if isinstance(module, torch.nn.Conv2d):
                conv_layers.append(module)

        for layer in conv_layers:
            if isinstance(layer, LoRACompatibleConv) and layer.lora_layer is None:
                layer.lora_layer = lambda *x: 0
            original_layers.append((layer, layer._conv_forward))
            layer._conv_forward = _conv_forward_asymmetric.__get__(layer, torch.nn.Conv2d)

        yield

    finally:
        for layer, orig_conv_forward in original_layers:
            layer._conv_forward = orig_conv_forward