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import logging |
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import math |
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from abc import abstractmethod |
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from typing import Iterable, List, Optional, Tuple, Union |
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|
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import torch as th |
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import torch.nn as nn |
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import torch.nn.functional as F |
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from einops import rearrange |
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from torch.utils.checkpoint import checkpoint |
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|
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from ...modules.attention import SpatialTransformer |
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from ...modules.diffusionmodules.util import (avg_pool_nd, conv_nd, linear, |
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normalization, |
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timestep_embedding, zero_module) |
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from ...modules.video_attention import SpatialVideoTransformer |
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from ...util import exists |
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|
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logpy = logging.getLogger(__name__) |
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|
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class AttentionPool2d(nn.Module): |
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""" |
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Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py |
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""" |
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|
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def __init__( |
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self, |
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spacial_dim: int, |
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embed_dim: int, |
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num_heads_channels: int, |
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output_dim: Optional[int] = None, |
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): |
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super().__init__() |
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self.positional_embedding = nn.Parameter( |
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th.randn(embed_dim, spacial_dim**2 + 1) / embed_dim**0.5 |
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) |
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self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1) |
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self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1) |
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self.num_heads = embed_dim // num_heads_channels |
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self.attention = QKVAttention(self.num_heads) |
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|
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def forward(self, x: th.Tensor) -> th.Tensor: |
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b, c, _ = x.shape |
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x = x.reshape(b, c, -1) |
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x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) |
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x = x + self.positional_embedding[None, :, :].to(x.dtype) |
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x = self.qkv_proj(x) |
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x = self.attention(x) |
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x = self.c_proj(x) |
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return x[:, :, 0] |
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|
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class TimestepBlock(nn.Module): |
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""" |
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Any module where forward() takes timestep embeddings as a second argument. |
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""" |
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@abstractmethod |
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def forward(self, x: th.Tensor, emb: th.Tensor): |
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""" |
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Apply the module to `x` given `emb` timestep embeddings. |
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""" |
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class TimestepEmbedSequential(nn.Sequential, TimestepBlock): |
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""" |
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A sequential module that passes timestep embeddings to the children that |
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support it as an extra input. |
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""" |
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|
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def forward( |
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self, |
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x: th.Tensor, |
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emb: th.Tensor, |
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context: Optional[th.Tensor] = None, |
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image_only_indicator: Optional[th.Tensor] = None, |
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time_context: Optional[int] = None, |
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num_video_frames: Optional[int] = None, |
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): |
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from ...modules.diffusionmodules.video_model import VideoResBlock |
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|
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for layer in self: |
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module = layer |
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|
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if isinstance(module, TimestepBlock) and not isinstance( |
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module, VideoResBlock |
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): |
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x = layer(x, emb) |
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elif isinstance(module, VideoResBlock): |
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x = layer(x, emb, num_video_frames, image_only_indicator) |
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elif isinstance(module, SpatialVideoTransformer): |
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x = layer( |
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x, |
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context, |
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time_context, |
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num_video_frames, |
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image_only_indicator, |
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) |
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elif isinstance(module, SpatialTransformer): |
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x = layer(x, context) |
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else: |
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x = layer(x) |
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return x |
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class Upsample(nn.Module): |
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""" |
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An upsampling layer with an optional convolution. |
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:param channels: channels in the inputs and outputs. |
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:param use_conv: a bool determining if a convolution is applied. |
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:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then |
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upsampling occurs in the inner-two dimensions. |
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""" |
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|
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def __init__( |
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self, |
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channels: int, |
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use_conv: bool, |
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dims: int = 2, |
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out_channels: Optional[int] = None, |
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padding: int = 1, |
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third_up: bool = False, |
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kernel_size: int = 3, |
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scale_factor: int = 2, |
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): |
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super().__init__() |
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self.channels = channels |
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self.out_channels = out_channels or channels |
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self.use_conv = use_conv |
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self.dims = dims |
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self.third_up = third_up |
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self.scale_factor = scale_factor |
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if use_conv: |
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self.conv = conv_nd( |
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dims, self.channels, self.out_channels, kernel_size, padding=padding |
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) |
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|
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def forward(self, x: th.Tensor) -> th.Tensor: |
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assert x.shape[1] == self.channels |
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|
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if self.dims == 3: |
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t_factor = 1 if not self.third_up else self.scale_factor |
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x = F.interpolate( |
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x, |
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( |
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t_factor * x.shape[2], |
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x.shape[3] * self.scale_factor, |
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x.shape[4] * self.scale_factor, |
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), |
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mode="nearest", |
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) |
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else: |
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x = F.interpolate(x, scale_factor=self.scale_factor, mode="nearest") |
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if self.use_conv: |
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x = self.conv(x) |
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return x |
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|
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class Downsample(nn.Module): |
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""" |
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A downsampling layer with an optional convolution. |
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:param channels: channels in the inputs and outputs. |
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:param use_conv: a bool determining if a convolution is applied. |
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:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then |
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downsampling occurs in the inner-two dimensions. |
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""" |
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|
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def __init__( |
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self, |
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channels: int, |
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use_conv: bool, |
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dims: int = 2, |
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out_channels: Optional[int] = None, |
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padding: int = 1, |
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third_down: bool = False, |
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): |
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super().__init__() |
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self.channels = channels |
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self.out_channels = out_channels or channels |
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self.use_conv = use_conv |
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self.dims = dims |
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stride = 2 if dims != 3 else ((1, 2, 2) if not third_down else (2, 2, 2)) |
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if use_conv: |
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logpy.info(f"Building a Downsample layer with {dims} dims.") |
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logpy.info( |
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f" --> settings are: \n in-chn: {self.channels}, out-chn: {self.out_channels}, " |
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f"kernel-size: 3, stride: {stride}, padding: {padding}" |
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) |
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if dims == 3: |
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logpy.info(f" --> Downsampling third axis (time): {third_down}") |
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self.op = conv_nd( |
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dims, |
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self.channels, |
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self.out_channels, |
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3, |
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stride=stride, |
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padding=padding, |
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) |
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else: |
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assert self.channels == self.out_channels |
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self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) |
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|
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def forward(self, x: th.Tensor) -> th.Tensor: |
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assert x.shape[1] == self.channels |
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return self.op(x) |
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class ResBlock(TimestepBlock): |
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""" |
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A residual block that can optionally change the number of channels. |
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:param channels: the number of input channels. |
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:param emb_channels: the number of timestep embedding channels. |
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:param dropout: the rate of dropout. |
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:param out_channels: if specified, the number of out channels. |
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:param use_conv: if True and out_channels is specified, use a spatial |
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convolution instead of a smaller 1x1 convolution to change the |
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channels in the skip connection. |
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:param dims: determines if the signal is 1D, 2D, or 3D. |
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:param use_checkpoint: if True, use gradient checkpointing on this module. |
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:param up: if True, use this block for upsampling. |
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:param down: if True, use this block for downsampling. |
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""" |
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|
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def __init__( |
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self, |
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channels: int, |
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emb_channels: int, |
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dropout: float, |
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out_channels: Optional[int] = None, |
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use_conv: bool = False, |
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use_scale_shift_norm: bool = False, |
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dims: int = 2, |
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use_checkpoint: bool = False, |
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up: bool = False, |
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down: bool = False, |
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kernel_size: int = 3, |
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exchange_temb_dims: bool = False, |
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skip_t_emb: bool = False, |
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): |
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super().__init__() |
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self.channels = channels |
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self.emb_channels = emb_channels |
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self.dropout = dropout |
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self.out_channels = out_channels or channels |
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self.use_conv = use_conv |
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self.use_checkpoint = use_checkpoint |
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self.use_scale_shift_norm = use_scale_shift_norm |
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self.exchange_temb_dims = exchange_temb_dims |
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|
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if isinstance(kernel_size, Iterable): |
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padding = [k // 2 for k in kernel_size] |
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else: |
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padding = kernel_size // 2 |
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self.in_layers = nn.Sequential( |
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normalization(channels), |
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nn.SiLU(), |
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conv_nd(dims, channels, self.out_channels, kernel_size, padding=padding), |
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) |
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self.updown = up or down |
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if up: |
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self.h_upd = Upsample(channels, False, dims) |
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self.x_upd = Upsample(channels, False, dims) |
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elif down: |
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self.h_upd = Downsample(channels, False, dims) |
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self.x_upd = Downsample(channels, False, dims) |
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else: |
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self.h_upd = self.x_upd = nn.Identity() |
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|
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self.skip_t_emb = skip_t_emb |
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self.emb_out_channels = ( |
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2 * self.out_channels if use_scale_shift_norm else self.out_channels |
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) |
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if self.skip_t_emb: |
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logpy.info(f"Skipping timestep embedding in {self.__class__.__name__}") |
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assert not self.use_scale_shift_norm |
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self.emb_layers = None |
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self.exchange_temb_dims = False |
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else: |
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self.emb_layers = nn.Sequential( |
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nn.SiLU(), |
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linear( |
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emb_channels, |
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self.emb_out_channels, |
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), |
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) |
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|
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self.out_layers = nn.Sequential( |
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normalization(self.out_channels), |
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nn.SiLU(), |
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nn.Dropout(p=dropout), |
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zero_module( |
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conv_nd( |
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dims, |
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self.out_channels, |
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self.out_channels, |
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kernel_size, |
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padding=padding, |
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) |
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), |
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) |
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|
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if self.out_channels == channels: |
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self.skip_connection = nn.Identity() |
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elif use_conv: |
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self.skip_connection = conv_nd( |
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dims, channels, self.out_channels, kernel_size, padding=padding |
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) |
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else: |
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self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) |
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|
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def forward(self, x: th.Tensor, emb: th.Tensor) -> th.Tensor: |
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""" |
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Apply the block to a Tensor, conditioned on a timestep embedding. |
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:param x: an [N x C x ...] Tensor of features. |
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:param emb: an [N x emb_channels] Tensor of timestep embeddings. |
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:return: an [N x C x ...] Tensor of outputs. |
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""" |
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if self.use_checkpoint: |
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return checkpoint(self._forward, x, emb) |
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else: |
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return self._forward(x, emb) |
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|
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def _forward(self, x: th.Tensor, emb: th.Tensor) -> th.Tensor: |
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if self.updown: |
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in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] |
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h = in_rest(x) |
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h = self.h_upd(h) |
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x = self.x_upd(x) |
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h = in_conv(h) |
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else: |
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h = self.in_layers(x) |
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|
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if self.skip_t_emb: |
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emb_out = th.zeros_like(h) |
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else: |
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emb_out = self.emb_layers(emb).type(h.dtype) |
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while len(emb_out.shape) < len(h.shape): |
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emb_out = emb_out[..., None] |
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if self.use_scale_shift_norm: |
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out_norm, out_rest = self.out_layers[0], self.out_layers[1:] |
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scale, shift = th.chunk(emb_out, 2, dim=1) |
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h = out_norm(h) * (1 + scale) + shift |
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h = out_rest(h) |
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else: |
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if self.exchange_temb_dims: |
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emb_out = rearrange(emb_out, "b t c ... -> b c t ...") |
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h = h + emb_out |
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h = self.out_layers(h) |
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return self.skip_connection(x) + h |
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|
|
|
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class AttentionBlock(nn.Module): |
|
""" |
|
An attention block that allows spatial positions to attend to each other. |
|
Originally ported from here, but adapted to the N-d case. |
|
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66. |
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""" |
|
|
|
def __init__( |
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self, |
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channels: int, |
|
num_heads: int = 1, |
|
num_head_channels: int = -1, |
|
use_checkpoint: bool = False, |
|
use_new_attention_order: bool = False, |
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): |
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super().__init__() |
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self.channels = channels |
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if num_head_channels == -1: |
|
self.num_heads = num_heads |
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else: |
|
assert ( |
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channels % num_head_channels == 0 |
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), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}" |
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self.num_heads = channels // num_head_channels |
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self.use_checkpoint = use_checkpoint |
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self.norm = normalization(channels) |
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self.qkv = conv_nd(1, channels, channels * 3, 1) |
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if use_new_attention_order: |
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|
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self.attention = QKVAttention(self.num_heads) |
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else: |
|
|
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self.attention = QKVAttentionLegacy(self.num_heads) |
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|
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self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) |
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|
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def forward(self, x: th.Tensor, **kwargs) -> th.Tensor: |
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return checkpoint(self._forward, x) |
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|
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def _forward(self, x: th.Tensor) -> th.Tensor: |
|
b, c, *spatial = x.shape |
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x = x.reshape(b, c, -1) |
|
qkv = self.qkv(self.norm(x)) |
|
h = self.attention(qkv) |
|
h = self.proj_out(h) |
|
return (x + h).reshape(b, c, *spatial) |
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|
|
|
|
class QKVAttentionLegacy(nn.Module): |
|
""" |
|
A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping |
|
""" |
|
|
|
def __init__(self, n_heads: int): |
|
super().__init__() |
|
self.n_heads = n_heads |
|
|
|
def forward(self, qkv: th.Tensor) -> th.Tensor: |
|
""" |
|
Apply QKV attention. |
|
:param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. |
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:return: an [N x (H * C) x T] tensor after attention. |
|
""" |
|
bs, width, length = qkv.shape |
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assert width % (3 * self.n_heads) == 0 |
|
ch = width // (3 * self.n_heads) |
|
q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1) |
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scale = 1 / math.sqrt(math.sqrt(ch)) |
|
weight = th.einsum( |
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"bct,bcs->bts", q * scale, k * scale |
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) |
|
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) |
|
a = th.einsum("bts,bcs->bct", weight, v) |
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return a.reshape(bs, -1, length) |
|
|
|
|
|
class QKVAttention(nn.Module): |
|
""" |
|
A module which performs QKV attention and splits in a different order. |
|
""" |
|
|
|
def __init__(self, n_heads: int): |
|
super().__init__() |
|
self.n_heads = n_heads |
|
|
|
def forward(self, qkv: th.Tensor) -> th.Tensor: |
|
""" |
|
Apply QKV attention. |
|
:param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. |
|
:return: an [N x (H * C) x T] tensor after attention. |
|
""" |
|
bs, width, length = qkv.shape |
|
assert width % (3 * self.n_heads) == 0 |
|
ch = width // (3 * self.n_heads) |
|
q, k, v = qkv.chunk(3, dim=1) |
|
scale = 1 / math.sqrt(math.sqrt(ch)) |
|
weight = th.einsum( |
|
"bct,bcs->bts", |
|
(q * scale).view(bs * self.n_heads, ch, length), |
|
(k * scale).view(bs * self.n_heads, ch, length), |
|
) |
|
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) |
|
a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length)) |
|
return a.reshape(bs, -1, length) |
|
|
|
|
|
class Timestep(nn.Module): |
|
def __init__(self, dim: int): |
|
super().__init__() |
|
self.dim = dim |
|
|
|
def forward(self, t: th.Tensor) -> th.Tensor: |
|
return timestep_embedding(t, self.dim) |
|
|
|
|
|
class UNetModel(nn.Module): |
|
""" |
|
The full UNet model with attention and timestep embedding. |
|
:param in_channels: channels in the input Tensor. |
|
:param model_channels: base channel count for the model. |
|
:param out_channels: channels in the output Tensor. |
|
:param num_res_blocks: number of residual blocks per downsample. |
|
:param attention_resolutions: a collection of downsample rates at which |
|
attention will take place. May be a set, list, or tuple. |
|
For example, if this contains 4, then at 4x downsampling, attention |
|
will be used. |
|
:param dropout: the dropout probability. |
|
:param channel_mult: channel multiplier for each level of the UNet. |
|
:param conv_resample: if True, use learned convolutions for upsampling and |
|
downsampling. |
|
:param dims: determines if the signal is 1D, 2D, or 3D. |
|
:param num_classes: if specified (as an int), then this model will be |
|
class-conditional with `num_classes` classes. |
|
:param use_checkpoint: use gradient checkpointing to reduce memory usage. |
|
:param num_heads: the number of attention heads in each attention layer. |
|
:param num_heads_channels: if specified, ignore num_heads and instead use |
|
a fixed channel width per attention head. |
|
:param num_heads_upsample: works with num_heads to set a different number |
|
of heads for upsampling. Deprecated. |
|
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism. |
|
:param resblock_updown: use residual blocks for up/downsampling. |
|
:param use_new_attention_order: use a different attention pattern for potentially |
|
increased efficiency. |
|
""" |
|
|
|
def __init__( |
|
self, |
|
in_channels: int, |
|
model_channels: int, |
|
out_channels: int, |
|
num_res_blocks: int, |
|
attention_resolutions: int, |
|
dropout: float = 0.0, |
|
channel_mult: Union[List, Tuple] = (1, 2, 4, 8), |
|
conv_resample: bool = True, |
|
dims: int = 2, |
|
num_classes: Optional[Union[int, str]] = None, |
|
use_checkpoint: bool = False, |
|
num_heads: int = -1, |
|
num_head_channels: int = -1, |
|
num_heads_upsample: int = -1, |
|
use_scale_shift_norm: bool = False, |
|
resblock_updown: bool = False, |
|
transformer_depth: int = 1, |
|
context_dim: Optional[int] = None, |
|
disable_self_attentions: Optional[List[bool]] = None, |
|
num_attention_blocks: Optional[List[int]] = None, |
|
disable_middle_self_attn: bool = False, |
|
disable_middle_transformer: bool = False, |
|
use_linear_in_transformer: bool = False, |
|
spatial_transformer_attn_type: str = "softmax", |
|
adm_in_channels: Optional[int] = None, |
|
): |
|
super().__init__() |
|
|
|
if num_heads_upsample == -1: |
|
num_heads_upsample = num_heads |
|
|
|
if num_heads == -1: |
|
assert ( |
|
num_head_channels != -1 |
|
), "Either num_heads or num_head_channels has to be set" |
|
|
|
if num_head_channels == -1: |
|
assert ( |
|
num_heads != -1 |
|
), "Either num_heads or num_head_channels has to be set" |
|
|
|
self.in_channels = in_channels |
|
self.model_channels = model_channels |
|
self.out_channels = out_channels |
|
if isinstance(transformer_depth, int): |
|
transformer_depth = len(channel_mult) * [transformer_depth] |
|
transformer_depth_middle = transformer_depth[-1] |
|
|
|
if isinstance(num_res_blocks, int): |
|
self.num_res_blocks = len(channel_mult) * [num_res_blocks] |
|
else: |
|
if len(num_res_blocks) != len(channel_mult): |
|
raise ValueError( |
|
"provide num_res_blocks either as an int (globally constant) or " |
|
"as a list/tuple (per-level) with the same length as channel_mult" |
|
) |
|
self.num_res_blocks = num_res_blocks |
|
|
|
if disable_self_attentions is not None: |
|
assert len(disable_self_attentions) == len(channel_mult) |
|
if num_attention_blocks is not None: |
|
assert len(num_attention_blocks) == len(self.num_res_blocks) |
|
assert all( |
|
map( |
|
lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], |
|
range(len(num_attention_blocks)), |
|
) |
|
) |
|
logpy.info( |
|
f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. " |
|
f"This option has LESS priority than attention_resolutions {attention_resolutions}, " |
|
f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, " |
|
f"attention will still not be set." |
|
) |
|
|
|
self.attention_resolutions = attention_resolutions |
|
self.dropout = dropout |
|
self.channel_mult = channel_mult |
|
self.conv_resample = conv_resample |
|
self.num_classes = num_classes |
|
self.use_checkpoint = use_checkpoint |
|
self.num_heads = num_heads |
|
self.num_head_channels = num_head_channels |
|
self.num_heads_upsample = num_heads_upsample |
|
|
|
time_embed_dim = model_channels * 4 |
|
self.time_embed = nn.Sequential( |
|
linear(model_channels, time_embed_dim), |
|
nn.SiLU(), |
|
linear(time_embed_dim, time_embed_dim), |
|
) |
|
|
|
if self.num_classes is not None: |
|
if isinstance(self.num_classes, int): |
|
self.label_emb = nn.Embedding(num_classes, time_embed_dim) |
|
elif self.num_classes == "continuous": |
|
logpy.info("setting up linear c_adm embedding layer") |
|
self.label_emb = nn.Linear(1, time_embed_dim) |
|
elif self.num_classes == "timestep": |
|
self.label_emb = nn.Sequential( |
|
Timestep(model_channels), |
|
nn.Sequential( |
|
linear(model_channels, time_embed_dim), |
|
nn.SiLU(), |
|
linear(time_embed_dim, time_embed_dim), |
|
), |
|
) |
|
elif self.num_classes == "sequential": |
|
assert adm_in_channels is not None |
|
self.label_emb = nn.Sequential( |
|
nn.Sequential( |
|
linear(adm_in_channels, time_embed_dim), |
|
nn.SiLU(), |
|
linear(time_embed_dim, time_embed_dim), |
|
) |
|
) |
|
else: |
|
raise ValueError |
|
|
|
self.input_blocks = nn.ModuleList( |
|
[ |
|
TimestepEmbedSequential( |
|
conv_nd(dims, in_channels, model_channels, 3, padding=1) |
|
) |
|
] |
|
) |
|
self._feature_size = model_channels |
|
input_block_chans = [model_channels] |
|
ch = model_channels |
|
ds = 1 |
|
for level, mult in enumerate(channel_mult): |
|
for nr in range(self.num_res_blocks[level]): |
|
layers = [ |
|
ResBlock( |
|
ch, |
|
time_embed_dim, |
|
dropout, |
|
out_channels=mult * model_channels, |
|
dims=dims, |
|
use_checkpoint=use_checkpoint, |
|
use_scale_shift_norm=use_scale_shift_norm, |
|
) |
|
] |
|
ch = mult * model_channels |
|
if ds in attention_resolutions: |
|
if num_head_channels == -1: |
|
dim_head = ch // num_heads |
|
else: |
|
num_heads = ch // num_head_channels |
|
dim_head = num_head_channels |
|
|
|
if context_dim is not None and exists(disable_self_attentions): |
|
disabled_sa = disable_self_attentions[level] |
|
else: |
|
disabled_sa = False |
|
|
|
if ( |
|
not exists(num_attention_blocks) |
|
or nr < num_attention_blocks[level] |
|
): |
|
layers.append( |
|
SpatialTransformer( |
|
ch, |
|
num_heads, |
|
dim_head, |
|
depth=transformer_depth[level], |
|
context_dim=context_dim, |
|
disable_self_attn=disabled_sa, |
|
use_linear=use_linear_in_transformer, |
|
attn_type=spatial_transformer_attn_type, |
|
use_checkpoint=use_checkpoint, |
|
) |
|
) |
|
self.input_blocks.append(TimestepEmbedSequential(*layers)) |
|
self._feature_size += ch |
|
input_block_chans.append(ch) |
|
if level != len(channel_mult) - 1: |
|
out_ch = ch |
|
self.input_blocks.append( |
|
TimestepEmbedSequential( |
|
ResBlock( |
|
ch, |
|
time_embed_dim, |
|
dropout, |
|
out_channels=out_ch, |
|
dims=dims, |
|
use_checkpoint=use_checkpoint, |
|
use_scale_shift_norm=use_scale_shift_norm, |
|
down=True, |
|
) |
|
if resblock_updown |
|
else Downsample( |
|
ch, conv_resample, dims=dims, out_channels=out_ch |
|
) |
|
) |
|
) |
|
ch = out_ch |
|
input_block_chans.append(ch) |
|
ds *= 2 |
|
self._feature_size += ch |
|
|
|
if num_head_channels == -1: |
|
dim_head = ch // num_heads |
|
else: |
|
num_heads = ch // num_head_channels |
|
dim_head = num_head_channels |
|
|
|
self.middle_block = TimestepEmbedSequential( |
|
ResBlock( |
|
ch, |
|
time_embed_dim, |
|
dropout, |
|
out_channels=ch, |
|
dims=dims, |
|
use_checkpoint=use_checkpoint, |
|
use_scale_shift_norm=use_scale_shift_norm, |
|
), |
|
SpatialTransformer( |
|
ch, |
|
num_heads, |
|
dim_head, |
|
depth=transformer_depth_middle, |
|
context_dim=context_dim, |
|
disable_self_attn=disable_middle_self_attn, |
|
use_linear=use_linear_in_transformer, |
|
attn_type=spatial_transformer_attn_type, |
|
use_checkpoint=use_checkpoint, |
|
) |
|
if not disable_middle_transformer |
|
else th.nn.Identity(), |
|
ResBlock( |
|
ch, |
|
time_embed_dim, |
|
dropout, |
|
dims=dims, |
|
use_checkpoint=use_checkpoint, |
|
use_scale_shift_norm=use_scale_shift_norm, |
|
), |
|
) |
|
self._feature_size += ch |
|
|
|
self.output_blocks = nn.ModuleList([]) |
|
for level, mult in list(enumerate(channel_mult))[::-1]: |
|
for i in range(self.num_res_blocks[level] + 1): |
|
ich = input_block_chans.pop() |
|
layers = [ |
|
ResBlock( |
|
ch + ich, |
|
time_embed_dim, |
|
dropout, |
|
out_channels=model_channels * mult, |
|
dims=dims, |
|
use_checkpoint=use_checkpoint, |
|
use_scale_shift_norm=use_scale_shift_norm, |
|
) |
|
] |
|
ch = model_channels * mult |
|
if ds in attention_resolutions: |
|
if num_head_channels == -1: |
|
dim_head = ch // num_heads |
|
else: |
|
num_heads = ch // num_head_channels |
|
dim_head = num_head_channels |
|
|
|
if exists(disable_self_attentions): |
|
disabled_sa = disable_self_attentions[level] |
|
else: |
|
disabled_sa = False |
|
|
|
if ( |
|
not exists(num_attention_blocks) |
|
or i < num_attention_blocks[level] |
|
): |
|
layers.append( |
|
SpatialTransformer( |
|
ch, |
|
num_heads, |
|
dim_head, |
|
depth=transformer_depth[level], |
|
context_dim=context_dim, |
|
disable_self_attn=disabled_sa, |
|
use_linear=use_linear_in_transformer, |
|
attn_type=spatial_transformer_attn_type, |
|
use_checkpoint=use_checkpoint, |
|
) |
|
) |
|
if level and i == self.num_res_blocks[level]: |
|
out_ch = ch |
|
layers.append( |
|
ResBlock( |
|
ch, |
|
time_embed_dim, |
|
dropout, |
|
out_channels=out_ch, |
|
dims=dims, |
|
use_checkpoint=use_checkpoint, |
|
use_scale_shift_norm=use_scale_shift_norm, |
|
up=True, |
|
) |
|
if resblock_updown |
|
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) |
|
) |
|
ds //= 2 |
|
self.output_blocks.append(TimestepEmbedSequential(*layers)) |
|
self._feature_size += ch |
|
|
|
self.out = nn.Sequential( |
|
normalization(ch), |
|
nn.SiLU(), |
|
zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), |
|
) |
|
|
|
def forward( |
|
self, |
|
x: th.Tensor, |
|
timesteps: Optional[th.Tensor] = None, |
|
context: Optional[th.Tensor] = None, |
|
y: Optional[th.Tensor] = None, |
|
**kwargs, |
|
) -> th.Tensor: |
|
""" |
|
Apply the model to an input batch. |
|
:param x: an [N x C x ...] Tensor of inputs. |
|
:param timesteps: a 1-D batch of timesteps. |
|
:param context: conditioning plugged in via crossattn |
|
:param y: an [N] Tensor of labels, if class-conditional. |
|
:return: an [N x C x ...] Tensor of outputs. |
|
""" |
|
assert (y is not None) == ( |
|
self.num_classes is not None |
|
), "must specify y if and only if the model is class-conditional" |
|
hs = [] |
|
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) |
|
emb = self.time_embed(t_emb) |
|
|
|
if self.num_classes is not None: |
|
assert y.shape[0] == x.shape[0] |
|
emb = emb + self.label_emb(y) |
|
|
|
h = x |
|
for module in self.input_blocks: |
|
h = module(h, emb, context) |
|
hs.append(h) |
|
h = self.middle_block(h, emb, context) |
|
for module in self.output_blocks: |
|
h = th.cat([h, hs.pop()], dim=1) |
|
h = module(h, emb, context) |
|
h = h.type(x.dtype) |
|
|
|
return self.out(h) |
|
|
