850 lines
32 KiB
Python
850 lines
32 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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conv_nd,
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linear,
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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 comfy.ldm.util import exists
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# dummy replace
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def convert_module_to_f16(x):
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pass
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def convert_module_to_f32(x):
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pass
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## go
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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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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: int = None,
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):
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super().__init__()
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self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)
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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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def forward(self, x):
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b, c, *_spatial = x.shape
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x = x.reshape(b, c, -1) # NC(HW)
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x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1)
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x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1)
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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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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):
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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 = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
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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 TransposedUpsample(nn.Module):
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'Learned 2x upsampling without padding'
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def __init__(self, channels, out_channels=None, ks=5):
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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.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)
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def forward(self,x):
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return self.up(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):
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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 = conv_nd(
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dims, self.channels, self.out_channels, 3, stride=stride, 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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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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):
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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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normalization(channels),
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nn.SiLU(),
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conv_nd(dims, channels, self.out_channels, 3, padding=1),
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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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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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2 * self.out_channels if use_scale_shift_norm else self.out_channels,
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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(dims, self.out_channels, self.out_channels, 3, padding=1)
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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, 3, padding=1
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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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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 AttentionBlock(nn.Module):
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"""
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An attention block that allows spatial positions to attend to each other.
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Originally ported from here, but adapted to the N-d case.
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https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
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"""
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def __init__(
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self,
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channels,
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num_heads=1,
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num_head_channels=-1,
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use_checkpoint=False,
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use_new_attention_order=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:
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self.num_heads = num_heads
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else:
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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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# split qkv before split heads
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self.attention = QKVAttention(self.num_heads)
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else:
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# split heads before split qkv
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self.attention = QKVAttentionLegacy(self.num_heads)
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self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
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def forward(self, x):
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return checkpoint(self._forward, (x,), self.parameters(), True) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
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#return pt_checkpoint(self._forward, x) # pytorch
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def _forward(self, x):
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b, c, *spatial = x.shape
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x = x.reshape(b, c, -1)
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qkv = self.qkv(self.norm(x))
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h = self.attention(qkv)
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h = self.proj_out(h)
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return (x + h).reshape(b, c, *spatial)
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def count_flops_attn(model, _x, y):
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"""
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A counter for the `thop` package to count the operations in an
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attention operation.
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Meant to be used like:
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macs, params = thop.profile(
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model,
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inputs=(inputs, timestamps),
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custom_ops={QKVAttention: QKVAttention.count_flops},
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)
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"""
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b, c, *spatial = y[0].shape
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num_spatial = int(np.prod(spatial))
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# We perform two matmuls with the same number of ops.
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# The first computes the weight matrix, the second computes
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# the combination of the value vectors.
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matmul_ops = 2 * b * (num_spatial ** 2) * c
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model.total_ops += th.DoubleTensor([matmul_ops])
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class QKVAttentionLegacy(nn.Module):
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"""
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A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
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"""
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def __init__(self, n_heads):
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super().__init__()
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self.n_heads = n_heads
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def forward(self, qkv):
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"""
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Apply QKV attention.
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: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.
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"""
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bs, width, length = qkv.shape
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assert width % (3 * self.n_heads) == 0
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ch = width // (3 * self.n_heads)
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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))
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weight = th.einsum(
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"bct,bcs->bts", q * scale, k * scale
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) # More stable with f16 than dividing afterwards
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weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
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a = th.einsum("bts,bcs->bct", weight, v)
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return a.reshape(bs, -1, length)
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@staticmethod
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def count_flops(model, _x, y):
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return count_flops_attn(model, _x, y)
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class QKVAttention(nn.Module):
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"""
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A module which performs QKV attention and splits in a different order.
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"""
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def __init__(self, n_heads):
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super().__init__()
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self.n_heads = n_heads
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def forward(self, qkv):
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"""
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Apply QKV attention.
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:param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
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:return: an [N x (H * C) x T] tensor after attention.
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"""
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bs, width, length = qkv.shape
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assert width % (3 * self.n_heads) == 0
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ch = width // (3 * self.n_heads)
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q, k, v = qkv.chunk(3, dim=1)
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scale = 1 / math.sqrt(math.sqrt(ch))
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weight = th.einsum(
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"bct,bcs->bts",
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(q * scale).view(bs * self.n_heads, ch, length),
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(k * scale).view(bs * self.n_heads, ch, length),
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) # More stable with f16 than dividing afterwards
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weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
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a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
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return a.reshape(bs, -1, length)
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@staticmethod
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def count_flops(model, _x, y):
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return count_flops_attn(model, _x, y)
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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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use_fp16=False,
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use_bf16=False,
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num_heads=-1,
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num_head_channels=-1,
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|
num_heads_upsample=-1,
|
|
use_scale_shift_norm=False,
|
|
resblock_updown=False,
|
|
use_new_attention_order=False,
|
|
use_spatial_transformer=False, # custom transformer support
|
|
transformer_depth=1, # custom transformer support
|
|
context_dim=None, # custom transformer support
|
|
n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
|
|
legacy=True,
|
|
disable_self_attentions=None,
|
|
num_attention_blocks=None,
|
|
disable_middle_self_attn=False,
|
|
use_linear_in_transformer=False,
|
|
adm_in_channels=None,
|
|
):
|
|
super().__init__()
|
|
if use_spatial_transformer:
|
|
assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
|
|
|
|
if context_dim is not None:
|
|
assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
|
|
# from omegaconf.listconfig import ListConfig
|
|
# if type(context_dim) == ListConfig:
|
|
# context_dim = list(context_dim)
|
|
|
|
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.image_size = image_size
|
|
self.in_channels = in_channels
|
|
self.model_channels = model_channels
|
|
self.out_channels = out_channels
|
|
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:
|
|
# should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
|
|
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))))
|
|
print(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.dtype = th.float16 if use_fp16 else th.float32
|
|
self.dtype = th.bfloat16 if use_bf16 else self.dtype
|
|
self.num_heads = num_heads
|
|
self.num_head_channels = num_head_channels
|
|
self.num_heads_upsample = num_heads_upsample
|
|
self.predict_codebook_ids = n_embed is not None
|
|
|
|
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":
|
|
print("setting up linear c_adm embedding layer")
|
|
self.label_emb = nn.Linear(1, 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 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 nr < num_attention_blocks[level]:
|
|
layers.append(
|
|
AttentionBlock(
|
|
ch,
|
|
use_checkpoint=use_checkpoint,
|
|
num_heads=num_heads,
|
|
num_head_channels=dim_head,
|
|
use_new_attention_order=use_new_attention_order,
|
|
) if not use_spatial_transformer else SpatialTransformer(
|
|
ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
|
|
disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
|
|
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
|
|
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,
|
|
),
|
|
AttentionBlock(
|
|
ch,
|
|
use_checkpoint=use_checkpoint,
|
|
num_heads=num_heads,
|
|
num_head_channels=dim_head,
|
|
use_new_attention_order=use_new_attention_order,
|
|
) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn
|
|
ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
|
|
disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
|
|
use_checkpoint=use_checkpoint
|
|
),
|
|
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 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(
|
|
AttentionBlock(
|
|
ch,
|
|
use_checkpoint=use_checkpoint,
|
|
num_heads=num_heads_upsample,
|
|
num_head_channels=dim_head,
|
|
use_new_attention_order=use_new_attention_order,
|
|
) if not use_spatial_transformer else SpatialTransformer(
|
|
ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
|
|
disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
|
|
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)),
|
|
)
|
|
if self.predict_codebook_ids:
|
|
self.id_predictor = nn.Sequential(
|
|
normalization(ch),
|
|
conv_nd(dims, model_channels, n_embed, 1),
|
|
#nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits
|
|
)
|
|
|
|
def convert_to_fp16(self):
|
|
"""
|
|
Convert the torso of the model to float16.
|
|
"""
|
|
self.input_blocks.apply(convert_module_to_f16)
|
|
self.middle_block.apply(convert_module_to_f16)
|
|
self.output_blocks.apply(convert_module_to_f16)
|
|
|
|
def convert_to_fp32(self):
|
|
"""
|
|
Convert the torso of the model to float32.
|
|
"""
|
|
self.input_blocks.apply(convert_module_to_f32)
|
|
self.middle_block.apply(convert_module_to_f32)
|
|
self.output_blocks.apply(convert_module_to_f32)
|
|
|
|
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
|
|
|
|
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.type(self.dtype)
|
|
for id, module in enumerate(self.input_blocks):
|
|
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)
|
|
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:
|
|
h += control['middle'].pop()
|
|
|
|
for module in self.output_blocks:
|
|
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
|
|
|
|
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)
|