from abc import abstractmethod import math import numpy as np import torch as th import torch.nn as nn import torch.nn.functional as F from .util import ( checkpoint, avg_pool_nd, zero_module, normalization, timestep_embedding, ) from ..attention import SpatialTransformer from comfy.ldm.util import exists import comfy.ops class TimestepBlock(nn.Module): """ Any module where forward() takes timestep embeddings as a second argument. """ @abstractmethod def forward(self, x, emb): """ Apply the module to `x` given `emb` timestep embeddings. """ class TimestepEmbedSequential(nn.Sequential, TimestepBlock): """ A sequential module that passes timestep embeddings to the children that support it as an extra input. """ def forward(self, x, emb, context=None, transformer_options={}, output_shape=None): for layer in self: if isinstance(layer, TimestepBlock): x = layer(x, emb) elif isinstance(layer, SpatialTransformer): x = layer(x, context, transformer_options) elif isinstance(layer, Upsample): x = layer(x, output_shape=output_shape) else: x = layer(x) return x #This is needed because accelerate makes a copy of transformer_options which breaks "current_index" def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None): for layer in ts: if isinstance(layer, TimestepBlock): x = layer(x, emb) elif isinstance(layer, SpatialTransformer): x = layer(x, context, transformer_options) transformer_options["current_index"] += 1 elif isinstance(layer, Upsample): x = layer(x, output_shape=output_shape) else: x = layer(x) return x class Upsample(nn.Module): """ An upsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then upsampling occurs in the inner-two dimensions. """ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=comfy.ops): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.dims = dims if use_conv: self.conv = operations.conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype, device=device) def forward(self, x, output_shape=None): assert x.shape[1] == self.channels if self.dims == 3: shape = [x.shape[2], x.shape[3] * 2, x.shape[4] * 2] if output_shape is not None: shape[1] = output_shape[3] shape[2] = output_shape[4] else: shape = [x.shape[2] * 2, x.shape[3] * 2] if output_shape is not None: shape[0] = output_shape[2] shape[1] = output_shape[3] x = F.interpolate(x, size=shape, mode="nearest") if self.use_conv: x = self.conv(x) return x class Downsample(nn.Module): """ A downsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then downsampling occurs in the inner-two dimensions. """ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=comfy.ops): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.dims = dims stride = 2 if dims != 3 else (1, 2, 2) if use_conv: self.op = operations.conv_nd( dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype, device=device ) else: assert self.channels == self.out_channels self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) def forward(self, x): assert x.shape[1] == self.channels return self.op(x) class ResBlock(TimestepBlock): """ A residual block that can optionally change the number of channels. :param channels: the number of input channels. :param emb_channels: the number of timestep embedding channels. :param dropout: the rate of dropout. :param out_channels: if specified, the number of out channels. :param use_conv: if True and out_channels is specified, use a spatial convolution instead of a smaller 1x1 convolution to change the channels in the skip connection. :param dims: determines if the signal is 1D, 2D, or 3D. :param use_checkpoint: if True, use gradient checkpointing on this module. :param up: if True, use this block for upsampling. :param down: if True, use this block for downsampling. """ def __init__( self, channels, emb_channels, dropout, out_channels=None, use_conv=False, use_scale_shift_norm=False, dims=2, use_checkpoint=False, up=False, down=False, dtype=None, device=None, operations=comfy.ops ): super().__init__() self.channels = channels self.emb_channels = emb_channels self.dropout = dropout self.out_channels = out_channels or channels self.use_conv = use_conv self.use_checkpoint = use_checkpoint self.use_scale_shift_norm = use_scale_shift_norm self.in_layers = nn.Sequential( nn.GroupNorm(32, channels, dtype=dtype, device=device), nn.SiLU(), operations.conv_nd(dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device), ) self.updown = up or down if up: self.h_upd = Upsample(channels, False, dims, dtype=dtype, device=device) self.x_upd = Upsample(channels, False, dims, dtype=dtype, device=device) elif down: self.h_upd = Downsample(channels, False, dims, dtype=dtype, device=device) self.x_upd = Downsample(channels, False, dims, dtype=dtype, device=device) else: self.h_upd = self.x_upd = nn.Identity() self.emb_layers = nn.Sequential( nn.SiLU(), operations.Linear( emb_channels, 2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device ), ) self.out_layers = nn.Sequential( nn.GroupNorm(32, self.out_channels, dtype=dtype, device=device), nn.SiLU(), nn.Dropout(p=dropout), zero_module( operations.conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1, dtype=dtype, device=device) ), ) if self.out_channels == channels: self.skip_connection = nn.Identity() elif use_conv: self.skip_connection = operations.conv_nd( dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device ) else: self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device) def forward(self, x, emb): """ Apply the block to a Tensor, conditioned on a timestep embedding. :param x: an [N x C x ...] Tensor of features. :param emb: an [N x emb_channels] Tensor of timestep embeddings. :return: an [N x C x ...] Tensor of outputs. """ return checkpoint( self._forward, (x, emb), self.parameters(), self.use_checkpoint ) def _forward(self, x, emb): if self.updown: in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] h = in_rest(x) h = self.h_upd(h) x = self.x_upd(x) h = in_conv(h) else: h = self.in_layers(x) emb_out = self.emb_layers(emb).type(h.dtype) while len(emb_out.shape) < len(h.shape): emb_out = emb_out[..., None] if self.use_scale_shift_norm: out_norm, out_rest = self.out_layers[0], self.out_layers[1:] scale, shift = th.chunk(emb_out, 2, dim=1) h = out_norm(h) * (1 + scale) + shift h = out_rest(h) else: h = h + emb_out h = self.out_layers(h) return self.skip_connection(x) + h class Timestep(nn.Module): def __init__(self, dim): super().__init__() self.dim = dim def forward(self, t): return timestep_embedding(t, self.dim) class UNetModel(nn.Module): """ The full UNet model with attention and timestep embedding. :param in_channels: channels in the input Tensor. :param model_channels: base channel count for the model. :param out_channels: channels in the output Tensor. :param num_res_blocks: number of residual blocks per downsample. :param attention_resolutions: a collection of downsample rates at which attention will take place. May be a set, list, or tuple. For example, if this contains 4, then at 4x downsampling, attention will be used. :param dropout: the dropout probability. :param channel_mult: channel multiplier for each level of the UNet. :param conv_resample: if True, use learned convolutions for upsampling and downsampling. :param dims: determines if the signal is 1D, 2D, or 3D. :param num_classes: if specified (as an int), then this model will be class-conditional with `num_classes` classes. :param use_checkpoint: use gradient checkpointing to reduce memory usage. :param num_heads: the number of attention heads in each attention layer. :param num_heads_channels: if specified, ignore num_heads and instead use a fixed channel width per attention head. :param num_heads_upsample: works with num_heads to set a different number of heads for upsampling. Deprecated. :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. :param resblock_updown: use residual blocks for up/downsampling. :param use_new_attention_order: use a different attention pattern for potentially increased efficiency. """ def __init__( self, image_size, in_channels, model_channels, out_channels, num_res_blocks, attention_resolutions, dropout=0, channel_mult=(1, 2, 4, 8), conv_resample=True, dims=2, num_classes=None, use_checkpoint=False, use_fp16=False, use_bf16=False, num_heads=-1, num_head_channels=-1, 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, transformer_depth_middle=None, device=None, operations=comfy.ops, ): super().__init__() assert use_spatial_transformer == True, "use_spatial_transformer has to be true" 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(transformer_depth, int): transformer_depth = len(channel_mult) * [transformer_depth] if transformer_depth_middle is None: transformer_depth_middle = transformer_depth[-1] if isinstance(num_res_blocks, int): self.num_res_blocks = len(channel_mult) * [num_res_blocks] else: if len(num_res_blocks) != len(channel_mult): raise ValueError("provide num_res_blocks either as an int (globally constant) or " "as a list/tuple (per-level) with the same length as channel_mult") self.num_res_blocks = num_res_blocks if disable_self_attentions is not None: # 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( operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device), nn.SiLU(), operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), ) 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( operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device), nn.SiLU(), operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), ) ) else: raise ValueError() self.input_blocks = nn.ModuleList( [ TimestepEmbedSequential( operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device) ) ] ) 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, dtype=self.dtype, device=device, operations=operations, ) ] 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(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 ) ) 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, dtype=self.dtype, device=device, operations=operations ) if resblock_updown else Downsample( ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations ) ) ) ch = out_ch input_block_chans.append(ch) ds *= 2 self._feature_size += ch if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels if legacy: #num_heads = 1 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels self.middle_block = TimestepEmbedSequential( ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations ), SpatialTransformer( # always uses a self-attn ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer, use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations ), ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations ), ) self._feature_size += ch self.output_blocks = nn.ModuleList([]) for level, mult in list(enumerate(channel_mult))[::-1]: for i in range(self.num_res_blocks[level] + 1): ich = input_block_chans.pop() layers = [ ResBlock( ch + ich, time_embed_dim, dropout, out_channels=model_channels * mult, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations ) ] ch = model_channels * mult if ds in attention_resolutions: if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels if legacy: #num_heads = 1 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels if exists(disable_self_attentions): disabled_sa = disable_self_attentions[level] else: disabled_sa = False if not exists(num_attention_blocks) or i < num_attention_blocks[level]: layers.append( SpatialTransformer( ch, num_heads, dim_head, depth=transformer_depth[level], context_dim=context_dim, disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations ) ) if level and i == self.num_res_blocks[level]: out_ch = ch layers.append( ResBlock( ch, time_embed_dim, dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, up=True, dtype=self.dtype, device=device, operations=operations ) if resblock_updown else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations) ) ds //= 2 self.output_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch self.out = nn.Sequential( nn.GroupNorm(32, ch, dtype=self.dtype, device=device), nn.SiLU(), zero_module(operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device)), ) if self.predict_codebook_ids: self.id_predictor = nn.Sequential( nn.GroupNorm(32, ch, dtype=self.dtype, device=device), operations.conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device), #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits ) def forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs): """ Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps. :param context: conditioning plugged in via crossattn :param y: an [N] Tensor of labels, if class-conditional. :return: an [N x C x ...] Tensor of outputs. """ transformer_options["original_shape"] = list(x.shape) transformer_options["current_index"] = 0 transformer_patches = transformer_options.get("patches", {}) assert (y is not None) == ( self.num_classes is not None ), "must specify y if and only if the model is class-conditional" hs = [] t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(self.dtype) emb = self.time_embed(t_emb) if self.num_classes is not None: assert y.shape[0] == x.shape[0] emb = emb + self.label_emb(y) h = x.type(self.dtype) for id, module in enumerate(self.input_blocks): transformer_options["block"] = ("input", id) h = forward_timestep_embed(module, h, emb, context, transformer_options) if control is not None and 'input' in control and len(control['input']) > 0: ctrl = control['input'].pop() if ctrl is not None: h += ctrl hs.append(h) transformer_options["block"] = ("middle", 0) h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options) if control is not None and 'middle' in control and len(control['middle']) > 0: ctrl = control['middle'].pop() if ctrl is not None: h += ctrl for id, module in enumerate(self.output_blocks): transformer_options["block"] = ("output", id) hsp = hs.pop() if control is not None and 'output' in control and len(control['output']) > 0: ctrl = control['output'].pop() if ctrl is not None: hsp += ctrl if "output_block_patch" in transformer_patches: patch = transformer_patches["output_block_patch"] for p in patch: h, hsp = p(h, hsp, transformer_options) h = th.cat([h, hsp], dim=1) del hsp if len(hs) > 0: output_shape = hs[-1].shape else: output_shape = None h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape) h = h.type(x.dtype) if self.predict_codebook_ids: return self.id_predictor(h) else: return self.out(h)