| from typing import Optional |
|
|
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from diffusers.utils import deprecate |
| from diffusers.models.upsampling import Upsample2D |
|
|
| from distvae.models.layers.normalization import RMSNorm |
| from distvae.modules.adapters.layers.conv_adapters import Conv2dAdapter |
|
|
|
|
| class PatchUpsample2D(Upsample2D): |
| """A 2D upsampling layer with an optional convolution. |
| |
| Parameters: |
| channels (`int`): |
| number of channels in the inputs and outputs. |
| use_conv (`bool`, default `False`): |
| option to use a convolution. |
| use_conv_transpose (`bool`, default `False`): |
| option to use a convolution transpose. |
| out_channels (`int`, optional): |
| number of output channels. Defaults to `channels`. |
| name (`str`, default `conv`): |
| name of the upsampling 2D layer. |
| """ |
|
|
| def __init__( |
| self, |
| channels: int, |
| use_conv: bool = False, |
| use_conv_transpose: bool = False, |
| out_channels: Optional[int] = None, |
| name: str = "conv", |
| kernel_size: Optional[int] = None, |
| padding=1, |
| norm_type=None, |
| eps=None, |
| elementwise_affine=None, |
| bias=True, |
| interpolate=True, |
| conv_block_size = 0, |
| ): |
| assert norm_type is None, "norm_type has not been supported for PatchUpsample2D yat." |
| assert use_conv_transpose is False, "use_conv_transpose has not been supported for PatchUpsample2D yet." |
| super().__init__(channels, use_conv, use_conv_transpose, out_channels, name, |
| kernel_size, padding, norm_type, eps, elementwise_affine, |
| bias, interpolate) |
| if name == "conv": |
| self.conv = Conv2dAdapter(self.conv, block_size=conv_block_size) |
| else: |
| self.Conv2d_0 = Conv2dAdapter(self.Conv2d_0, block_size=conv_block_size) |
