# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py import os import sys sys.path.append(os.path.split(sys.path[0])[0]) import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange class InflatedConv3d(nn.Conv2d): def forward(self, x): video_length = x.shape[2] x = rearrange(x, "b c f h w -> (b f) c h w") x = super().forward(x) x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length) return x class Upsample3D(nn.Module): def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.use_conv_transpose = use_conv_transpose self.name = name conv = None if use_conv_transpose: raise NotImplementedError elif use_conv: conv = InflatedConv3d(self.channels, self.out_channels, 3, padding=1) if name == "conv": self.conv = conv else: self.Conv2d_0 = conv def forward(self, hidden_states, output_size=None): assert hidden_states.shape[1] == self.channels if self.use_conv_transpose: raise NotImplementedError # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16 dtype = hidden_states.dtype if dtype == torch.bfloat16: hidden_states = hidden_states.to(torch.float32) # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 if hidden_states.shape[0] >= 64: hidden_states = hidden_states.contiguous() # if `output_size` is passed we force the interpolation output # size and do not make use of `scale_factor=2` if output_size is None: hidden_states = F.interpolate(hidden_states, scale_factor=[1.0, 2.0, 2.0], mode="nearest") else: hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest") # If the input is bfloat16, we cast back to bfloat16 if dtype == torch.bfloat16: hidden_states = hidden_states.to(dtype) if self.use_conv: if self.name == "conv": hidden_states = self.conv(hidden_states) else: hidden_states = self.Conv2d_0(hidden_states) return hidden_states class Downsample3D(nn.Module): def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.padding = padding stride = 2 self.name = name if use_conv: conv = InflatedConv3d(self.channels, self.out_channels, 3, stride=stride, padding=padding) else: raise NotImplementedError if name == "conv": self.Conv2d_0 = conv self.conv = conv elif name == "Conv2d_0": self.conv = conv else: self.conv = conv def forward(self, hidden_states): assert hidden_states.shape[1] == self.channels if self.use_conv and self.padding == 0: raise NotImplementedError assert hidden_states.shape[1] == self.channels hidden_states = self.conv(hidden_states) return hidden_states class ResnetBlock3D(nn.Module): def __init__( self, *, in_channels, out_channels=None, conv_shortcut=False, dropout=0.0, temb_channels=512, groups=32, groups_out=None, pre_norm=True, eps=1e-6, non_linearity="swish", time_embedding_norm="default", output_scale_factor=1.0, use_in_shortcut=None, ): super().__init__() self.pre_norm = pre_norm self.pre_norm = True self.in_channels = in_channels out_channels = in_channels if out_channels is None else out_channels self.out_channels = out_channels self.use_conv_shortcut = conv_shortcut self.time_embedding_norm = time_embedding_norm self.output_scale_factor = output_scale_factor if groups_out is None: groups_out = groups self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) self.conv1 = InflatedConv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) if temb_channels is not None: if self.time_embedding_norm == "default": time_emb_proj_out_channels = out_channels elif self.time_embedding_norm == "scale_shift": time_emb_proj_out_channels = out_channels * 2 else: raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ") self.time_emb_proj = torch.nn.Linear(temb_channels, time_emb_proj_out_channels) else: self.time_emb_proj = None self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True) self.dropout = torch.nn.Dropout(dropout) self.conv2 = InflatedConv3d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) if non_linearity == "swish": self.nonlinearity = lambda x: F.silu(x) elif non_linearity == "mish": self.nonlinearity = Mish() elif non_linearity == "silu": self.nonlinearity = nn.SiLU() self.use_in_shortcut = self.in_channels != self.out_channels if use_in_shortcut is None else use_in_shortcut self.conv_shortcut = None if self.use_in_shortcut: self.conv_shortcut = InflatedConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) def forward(self, input_tensor, temb): hidden_states = input_tensor hidden_states = self.norm1(hidden_states) hidden_states = self.nonlinearity(hidden_states) hidden_states = self.conv1(hidden_states) if temb is not None: temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None, None] if temb is not None and self.time_embedding_norm == "default": hidden_states = hidden_states + temb hidden_states = self.norm2(hidden_states) if temb is not None and self.time_embedding_norm == "scale_shift": scale, shift = torch.chunk(temb, 2, dim=1) hidden_states = hidden_states * (1 + scale) + shift hidden_states = self.nonlinearity(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.conv2(hidden_states) if self.conv_shortcut is not None: input_tensor = self.conv_shortcut(input_tensor) output_tensor = (input_tensor + hidden_states) / self.output_scale_factor return output_tensor class Mish(torch.nn.Module): def forward(self, hidden_states): return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states))