File size: 1,938 Bytes
c0bba28 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | 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) |