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662 lines
27 KiB
662 lines
27 KiB
from abc import abstractmethod |
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import math |
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|
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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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|
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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 comfy.ldm.util import exists |
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import comfy.ops |
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|
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class TimestepBlock(nn.Module): |
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""" |
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Any module where forward() takes timestep embeddings as a second argument. |
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""" |
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|
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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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|
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class TimestepEmbedSequential(nn.Sequential, TimestepBlock): |
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""" |
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A sequential module that passes timestep embeddings to the children that |
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support it as an extra input. |
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""" |
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|
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def forward(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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|
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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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|
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class Upsample(nn.Module): |
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""" |
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An upsampling layer with an optional convolution. |
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:param channels: channels in the inputs and outputs. |
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:param use_conv: a bool determining if a convolution is applied. |
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:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then |
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upsampling occurs in the inner-two dimensions. |
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""" |
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|
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def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=comfy.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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|
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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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|
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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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|
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class Downsample(nn.Module): |
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""" |
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A downsampling layer with an optional convolution. |
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:param channels: channels in the inputs and outputs. |
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:param use_conv: a bool determining if a convolution is applied. |
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:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then |
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downsampling occurs in the inner-two dimensions. |
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""" |
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|
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def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=comfy.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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|
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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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|
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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=comfy.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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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=comfy.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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|
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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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|
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if num_heads_upsample == -1: |
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num_heads_upsample = num_heads |
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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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 |
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) |
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) |
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) |
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ch = out_ch |
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input_block_chans.append(ch) |
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ds *= 2 |
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self._feature_size += ch |
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|
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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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self.middle_block = 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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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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SpatialTransformer( # always uses a self-attn |
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ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, |
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disable_self_attn=disable_middle_self_attn, 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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ResBlock( |
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ch, |
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time_embed_dim, |
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dropout, |
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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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self._feature_size += ch |
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|
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self.output_blocks = nn.ModuleList([]) |
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for level, mult in list(enumerate(channel_mult))[::-1]: |
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for i in range(self.num_res_blocks[level] + 1): |
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ich = input_block_chans.pop() |
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layers = [ |
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ResBlock( |
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ch + ich, |
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time_embed_dim, |
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dropout, |
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out_channels=model_channels * mult, |
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dims=dims, |
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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)
|
|
|