663 lines
27 KiB
Python
663 lines
27 KiB
Python
from abc import abstractmethod
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import math
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import numpy as np
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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 .util import (
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checkpoint,
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avg_pool_nd,
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zero_module,
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normalization,
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timestep_embedding,
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)
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from ..attention import SpatialTransformer
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from fcbh.ldm.util import exists
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import fcbh.ops
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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, emb):
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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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def forward(self, x, emb, context=None, transformer_options={}, output_shape=None):
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for layer in self:
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if isinstance(layer, TimestepBlock):
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x = layer(x, emb)
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elif isinstance(layer, SpatialTransformer):
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x = layer(x, context, transformer_options)
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elif isinstance(layer, Upsample):
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x = layer(x, output_shape=output_shape)
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else:
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x = layer(x)
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return x
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#This is needed because accelerate makes a copy of transformer_options which breaks "current_index"
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def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None):
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for layer in ts:
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if isinstance(layer, TimestepBlock):
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x = layer(x, emb)
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elif isinstance(layer, SpatialTransformer):
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x = layer(x, context, transformer_options)
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transformer_options["current_index"] += 1
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elif isinstance(layer, Upsample):
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x = layer(x, output_shape=output_shape)
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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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def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=fcbh.ops):
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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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if use_conv:
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self.conv = operations.conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype, device=device)
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def forward(self, x, output_shape=None):
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assert x.shape[1] == self.channels
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if self.dims == 3:
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shape = [x.shape[2], x.shape[3] * 2, x.shape[4] * 2]
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if output_shape is not None:
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shape[1] = output_shape[3]
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shape[2] = output_shape[4]
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else:
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shape = [x.shape[2] * 2, x.shape[3] * 2]
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if output_shape is not None:
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shape[0] = output_shape[2]
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shape[1] = output_shape[3]
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x = F.interpolate(x, size=shape, 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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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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def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=fcbh.ops):
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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)
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if use_conv:
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self.op = operations.conv_nd(
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dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype, device=device
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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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def forward(self, x):
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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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def __init__(
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self,
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channels,
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emb_channels,
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dropout,
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out_channels=None,
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use_conv=False,
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use_scale_shift_norm=False,
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dims=2,
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use_checkpoint=False,
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up=False,
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down=False,
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dtype=None,
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device=None,
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operations=fcbh.ops
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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.in_layers = nn.Sequential(
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nn.GroupNorm(32, channels, dtype=dtype, device=device),
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nn.SiLU(),
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operations.conv_nd(dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device),
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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, dtype=dtype, device=device)
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self.x_upd = Upsample(channels, False, dims, dtype=dtype, device=device)
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elif down:
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self.h_upd = Downsample(channels, False, dims, dtype=dtype, device=device)
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self.x_upd = Downsample(channels, False, dims, dtype=dtype, device=device)
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else:
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self.h_upd = self.x_upd = nn.Identity()
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self.emb_layers = nn.Sequential(
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nn.SiLU(),
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operations.Linear(
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emb_channels,
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2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device
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),
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)
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self.out_layers = nn.Sequential(
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nn.GroupNorm(32, self.out_channels, dtype=dtype, device=device),
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nn.SiLU(),
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nn.Dropout(p=dropout),
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zero_module(
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operations.conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1, dtype=dtype, device=device)
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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 = operations.conv_nd(
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dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device
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)
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else:
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self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device)
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def forward(self, x, emb):
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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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return checkpoint(
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self._forward, (x, emb), self.parameters(), self.use_checkpoint
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)
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def _forward(self, x, emb):
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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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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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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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class Timestep(nn.Module):
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def __init__(self, dim):
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super().__init__()
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self.dim = dim
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def forward(self, t):
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return timestep_embedding(t, self.dim)
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class UNetModel(nn.Module):
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"""
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The full UNet model with attention and timestep embedding.
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:param in_channels: channels in the input Tensor.
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:param model_channels: base channel count for the model.
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:param out_channels: channels in the output Tensor.
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:param num_res_blocks: number of residual blocks per downsample.
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:param attention_resolutions: a collection of downsample rates at which
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attention will take place. May be a set, list, or tuple.
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For example, if this contains 4, then at 4x downsampling, attention
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will be used.
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:param dropout: the dropout probability.
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:param channel_mult: channel multiplier for each level of the UNet.
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:param conv_resample: if True, use learned convolutions for upsampling and
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downsampling.
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:param dims: determines if the signal is 1D, 2D, or 3D.
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:param num_classes: if specified (as an int), then this model will be
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class-conditional with `num_classes` classes.
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:param use_checkpoint: use gradient checkpointing to reduce memory usage.
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:param num_heads: the number of attention heads in each attention layer.
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:param num_heads_channels: if specified, ignore num_heads and instead use
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a fixed channel width per attention head.
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:param num_heads_upsample: works with num_heads to set a different number
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of heads for upsampling. Deprecated.
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:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
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:param resblock_updown: use residual blocks for up/downsampling.
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:param use_new_attention_order: use a different attention pattern for potentially
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increased efficiency.
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"""
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def __init__(
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self,
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image_size,
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in_channels,
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model_channels,
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out_channels,
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num_res_blocks,
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attention_resolutions,
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dropout=0,
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channel_mult=(1, 2, 4, 8),
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conv_resample=True,
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dims=2,
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num_classes=None,
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use_checkpoint=False,
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dtype=th.float32,
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num_heads=-1,
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num_head_channels=-1,
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num_heads_upsample=-1,
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use_scale_shift_norm=False,
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resblock_updown=False,
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use_new_attention_order=False,
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use_spatial_transformer=False, # custom transformer support
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transformer_depth=1, # custom transformer support
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context_dim=None, # custom transformer support
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n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
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legacy=True,
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disable_self_attentions=None,
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num_attention_blocks=None,
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disable_middle_self_attn=False,
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use_linear_in_transformer=False,
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adm_in_channels=None,
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transformer_depth_middle=None,
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device=None,
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operations=fcbh.ops,
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):
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super().__init__()
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assert use_spatial_transformer == True, "use_spatial_transformer has to be true"
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if use_spatial_transformer:
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assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
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if context_dim is not None:
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assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
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# from omegaconf.listconfig import ListConfig
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# if type(context_dim) == ListConfig:
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# context_dim = list(context_dim)
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if num_heads_upsample == -1:
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num_heads_upsample = num_heads
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if num_heads == -1:
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assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
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if num_head_channels == -1:
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assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
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self.image_size = image_size
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self.in_channels = in_channels
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self.model_channels = model_channels
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self.out_channels = out_channels
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if isinstance(transformer_depth, int):
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transformer_depth = len(channel_mult) * [transformer_depth]
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if transformer_depth_middle is None:
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transformer_depth_middle = transformer_depth[-1]
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if isinstance(num_res_blocks, int):
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self.num_res_blocks = len(channel_mult) * [num_res_blocks]
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else:
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if len(num_res_blocks) != len(channel_mult):
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raise ValueError("provide num_res_blocks either as an int (globally constant) or "
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"as a list/tuple (per-level) with the same length as channel_mult")
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self.num_res_blocks = num_res_blocks
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if disable_self_attentions is not None:
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# should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
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assert len(disable_self_attentions) == len(channel_mult)
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if num_attention_blocks is not None:
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assert len(num_attention_blocks) == len(self.num_res_blocks)
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assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
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print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
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f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
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f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
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f"attention will still not be set.")
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self.attention_resolutions = attention_resolutions
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self.dropout = dropout
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self.channel_mult = channel_mult
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self.conv_resample = conv_resample
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self.num_classes = num_classes
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self.use_checkpoint = use_checkpoint
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self.dtype = dtype
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self.num_heads = num_heads
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self.num_head_channels = num_head_channels
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self.num_heads_upsample = num_heads_upsample
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self.predict_codebook_ids = n_embed is not None
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time_embed_dim = model_channels * 4
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self.time_embed = nn.Sequential(
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operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),
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nn.SiLU(),
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operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
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)
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if self.num_classes is not None:
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if isinstance(self.num_classes, int):
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self.label_emb = nn.Embedding(num_classes, time_embed_dim)
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elif self.num_classes == "continuous":
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print("setting up linear c_adm embedding layer")
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self.label_emb = nn.Linear(1, time_embed_dim)
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elif self.num_classes == "sequential":
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assert adm_in_channels is not None
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self.label_emb = nn.Sequential(
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nn.Sequential(
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operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
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nn.SiLU(),
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operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
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)
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)
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else:
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raise ValueError()
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self.input_blocks = nn.ModuleList(
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[
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TimestepEmbedSequential(
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operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)
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)
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]
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)
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self._feature_size = model_channels
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input_block_chans = [model_channels]
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ch = model_channels
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ds = 1
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for level, mult in enumerate(channel_mult):
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for nr in range(self.num_res_blocks[level]):
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layers = [
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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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out_channels=mult * model_channels,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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dtype=self.dtype,
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device=device,
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operations=operations,
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)
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]
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ch = mult * model_channels
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if ds in attention_resolutions:
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if num_head_channels == -1:
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dim_head = ch // num_heads
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else:
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num_heads = ch // num_head_channels
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dim_head = num_head_channels
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if legacy:
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#num_heads = 1
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dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
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if exists(disable_self_attentions):
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disabled_sa = disable_self_attentions[level]
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else:
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disabled_sa = False
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if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
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layers.append(SpatialTransformer(
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ch, num_heads, dim_head, depth=transformer_depth[level], context_dim=context_dim,
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disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
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use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
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)
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)
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self.input_blocks.append(TimestepEmbedSequential(*layers))
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self._feature_size += ch
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input_block_chans.append(ch)
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if level != len(channel_mult) - 1:
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out_ch = ch
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self.input_blocks.append(
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TimestepEmbedSequential(
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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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out_channels=out_ch,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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down=True,
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dtype=self.dtype,
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device=device,
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operations=operations
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)
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if resblock_updown
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else Downsample(
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ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations
|
|
)
|
|
)
|
|
)
|
|
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
|
|
if legacy:
|
|
#num_heads = 1
|
|
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
|
|
self.middle_block = TimestepEmbedSequential(
|
|
ResBlock(
|
|
ch,
|
|
time_embed_dim,
|
|
dropout,
|
|
dims=dims,
|
|
use_checkpoint=use_checkpoint,
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
|
dtype=self.dtype,
|
|
device=device,
|
|
operations=operations
|
|
),
|
|
SpatialTransformer( # always uses a self-attn
|
|
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,
|
|
use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
|
|
),
|
|
ResBlock(
|
|
ch,
|
|
time_embed_dim,
|
|
dropout,
|
|
dims=dims,
|
|
use_checkpoint=use_checkpoint,
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
|
dtype=self.dtype,
|
|
device=device,
|
|
operations=operations
|
|
),
|
|
)
|
|
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,
|
|
dtype=self.dtype,
|
|
device=device,
|
|
operations=operations
|
|
)
|
|
]
|
|
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 legacy:
|
|
#num_heads = 1
|
|
dim_head = ch // num_heads if use_spatial_transformer else 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,
|
|
use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
|
|
)
|
|
)
|
|
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,
|
|
dtype=self.dtype,
|
|
device=device,
|
|
operations=operations
|
|
)
|
|
if resblock_updown
|
|
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations)
|
|
)
|
|
ds //= 2
|
|
self.output_blocks.append(TimestepEmbedSequential(*layers))
|
|
self._feature_size += ch
|
|
|
|
self.out = nn.Sequential(
|
|
nn.GroupNorm(32, ch, dtype=self.dtype, device=device),
|
|
nn.SiLU(),
|
|
zero_module(operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device)),
|
|
)
|
|
if self.predict_codebook_ids:
|
|
self.id_predictor = nn.Sequential(
|
|
nn.GroupNorm(32, ch, dtype=self.dtype, device=device),
|
|
operations.conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device),
|
|
#nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits
|
|
)
|
|
|
|
def forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
|
|
"""
|
|
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.
|
|
"""
|
|
transformer_options["original_shape"] = list(x.shape)
|
|
transformer_options["current_index"] = 0
|
|
transformer_patches = transformer_options.get("patches", {})
|
|
|
|
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).to(self.dtype)
|
|
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.type(self.dtype)
|
|
for id, module in enumerate(self.input_blocks):
|
|
transformer_options["block"] = ("input", id)
|
|
h = forward_timestep_embed(module, h, emb, context, transformer_options)
|
|
if control is not None and 'input' in control and len(control['input']) > 0:
|
|
ctrl = control['input'].pop()
|
|
if ctrl is not None:
|
|
h += ctrl
|
|
hs.append(h)
|
|
transformer_options["block"] = ("middle", 0)
|
|
h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options)
|
|
if control is not None and 'middle' in control and len(control['middle']) > 0:
|
|
ctrl = control['middle'].pop()
|
|
if ctrl is not None:
|
|
h += ctrl
|
|
|
|
for id, module in enumerate(self.output_blocks):
|
|
transformer_options["block"] = ("output", id)
|
|
hsp = hs.pop()
|
|
if control is not None and 'output' in control and len(control['output']) > 0:
|
|
ctrl = control['output'].pop()
|
|
if ctrl is not None:
|
|
hsp += ctrl
|
|
|
|
if "output_block_patch" in transformer_patches:
|
|
patch = transformer_patches["output_block_patch"]
|
|
for p in patch:
|
|
h, hsp = p(h, hsp, transformer_options)
|
|
|
|
h = th.cat([h, hsp], dim=1)
|
|
del hsp
|
|
if len(hs) > 0:
|
|
output_shape = hs[-1].shape
|
|
else:
|
|
output_shape = None
|
|
h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape)
|
|
h = h.type(x.dtype)
|
|
if self.predict_codebook_ids:
|
|
return self.id_predictor(h)
|
|
else:
|
|
return self.out(h)
|