#taken from: https://github.com/lllyasviel/ControlNet #and modified import torch import torch as th import torch.nn as nn from ..ldm.modules.diffusionmodules.util import ( zero_module, timestep_embedding, ) from ..ldm.modules.attention import SpatialTransformer from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample from ..ldm.util import exists from .control_types import UNION_CONTROLNET_TYPES from collections import OrderedDict import comfy.ops from comfy.ldm.modules.attention import optimized_attention class OptimizedAttention(nn.Module): def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None): super().__init__() self.heads = nhead self.c = c self.in_proj = operations.Linear(c, c * 3, bias=True, dtype=dtype, device=device) self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device) def forward(self, x): x = self.in_proj(x) q, k, v = x.split(self.c, dim=2) out = optimized_attention(q, k, v, self.heads) return self.out_proj(out) class QuickGELU(nn.Module): def forward(self, x: torch.Tensor): return x * torch.sigmoid(1.702 * x) class ResBlockUnionControlnet(nn.Module): def __init__(self, dim, nhead, dtype=None, device=None, operations=None): super().__init__() self.attn = OptimizedAttention(dim, nhead, dtype=dtype, device=device, operations=operations) self.ln_1 = operations.LayerNorm(dim, dtype=dtype, device=device) self.mlp = nn.Sequential( OrderedDict([("c_fc", operations.Linear(dim, dim * 4, dtype=dtype, device=device)), ("gelu", QuickGELU()), ("c_proj", operations.Linear(dim * 4, dim, dtype=dtype, device=device))])) self.ln_2 = operations.LayerNorm(dim, dtype=dtype, device=device) def attention(self, x: torch.Tensor): return self.attn(x) def forward(self, x: torch.Tensor): x = x + self.attention(self.ln_1(x)) x = x + self.mlp(self.ln_2(x)) return x class ControlledUnetModel(UNetModel): #implemented in the ldm unet pass class ControlNet(nn.Module): def __init__( self, image_size, in_channels, model_channels, hint_channels, num_res_blocks, dropout=0, channel_mult=(1, 2, 4, 8), conv_resample=True, dims=2, num_classes=None, use_checkpoint=False, dtype=torch.float32, 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, transformer_depth_output=None, attn_precision=None, union_controlnet_num_control_type=None, device=None, operations=comfy.ops.disable_weight_init, **kwargs, ): 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.dims = dims self.image_size = image_size self.in_channels = in_channels self.model_channels = model_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)))) transformer_depth = transformer_depth[:] 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 = 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.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels, operations=operations, dtype=self.dtype, device=device)]) self.input_hint_block = TimestepEmbedSequential( operations.conv_nd(dims, hint_channels, 16, 3, padding=1, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 16, 16, 3, padding=1, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 32, 32, 3, padding=1, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 96, 96, 3, padding=1, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 256, 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 num_transformers = transformer_depth.pop(0) if num_transformers > 0: 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=num_transformers, context_dim=context_dim, disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations ) ) self.input_blocks.append(TimestepEmbedSequential(*layers)) self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)) 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) self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)) 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 mid_block = [ 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 )] if transformer_depth_middle >= 0: mid_block += [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, attn_precision=attn_precision, 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.middle_block = TimestepEmbedSequential(*mid_block) self.middle_block_out = self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device) self._feature_size += ch if union_controlnet_num_control_type is not None: self.num_control_type = union_controlnet_num_control_type num_trans_channel = 320 num_trans_head = 8 num_trans_layer = 1 num_proj_channel = 320 # task_scale_factor = num_trans_channel ** 0.5 self.task_embedding = nn.Parameter(torch.empty(self.num_control_type, num_trans_channel, dtype=self.dtype, device=device)) self.transformer_layes = nn.Sequential(*[ResBlockUnionControlnet(num_trans_channel, num_trans_head, dtype=self.dtype, device=device, operations=operations) for _ in range(num_trans_layer)]) self.spatial_ch_projs = operations.Linear(num_trans_channel, num_proj_channel, dtype=self.dtype, device=device) #----------------------------------------------------------------------------------------------------- control_add_embed_dim = 256 class ControlAddEmbedding(nn.Module): def __init__(self, in_dim, out_dim, num_control_type, dtype=None, device=None, operations=None): super().__init__() self.num_control_type = num_control_type self.in_dim = in_dim self.linear_1 = operations.Linear(in_dim * num_control_type, out_dim, dtype=dtype, device=device) self.linear_2 = operations.Linear(out_dim, out_dim, dtype=dtype, device=device) def forward(self, control_type, dtype, device): c_type = torch.zeros((self.num_control_type,), device=device) c_type[control_type] = 1.0 c_type = timestep_embedding(c_type.flatten(), self.in_dim, repeat_only=False).to(dtype).reshape((-1, self.num_control_type * self.in_dim)) return self.linear_2(torch.nn.functional.silu(self.linear_1(c_type))) self.control_add_embedding = ControlAddEmbedding(control_add_embed_dim, time_embed_dim, self.num_control_type, dtype=self.dtype, device=device, operations=operations) else: self.task_embedding = None self.control_add_embedding = None def union_controlnet_merge(self, hint, control_type, emb, context): # Equivalent to: https://github.com/xinsir6/ControlNetPlus/tree/main inputs = [] condition_list = [] for idx in range(min(1, len(control_type))): controlnet_cond = self.input_hint_block(hint[idx], emb, context) feat_seq = torch.mean(controlnet_cond, dim=(2, 3)) if idx < len(control_type): feat_seq += self.task_embedding[control_type[idx]].to(dtype=feat_seq.dtype, device=feat_seq.device) inputs.append(feat_seq.unsqueeze(1)) condition_list.append(controlnet_cond) x = torch.cat(inputs, dim=1) x = self.transformer_layes(x) controlnet_cond_fuser = None for idx in range(len(control_type)): alpha = self.spatial_ch_projs(x[:, idx]) alpha = alpha.unsqueeze(-1).unsqueeze(-1) o = condition_list[idx] + alpha if controlnet_cond_fuser is None: controlnet_cond_fuser = o else: controlnet_cond_fuser += o return controlnet_cond_fuser def make_zero_conv(self, channels, operations=None, dtype=None, device=None): return TimestepEmbedSequential(operations.conv_nd(self.dims, channels, channels, 1, padding=0, dtype=dtype, device=device)) def forward(self, x, hint, timesteps, context, y=None, **kwargs): t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) emb = self.time_embed(t_emb) guided_hint = None if self.control_add_embedding is not None: #Union Controlnet control_type = kwargs.get("control_type", []) if any([c >= self.num_control_type for c in control_type]): max_type = max(control_type) max_type_name = { v: k for k, v in UNION_CONTROLNET_TYPES.items() }[max_type] raise ValueError( f"Control type {max_type_name}({max_type}) is out of range for the number of control types" + f"({self.num_control_type}) supported.\n" + "Please consider using the ProMax ControlNet Union model.\n" + "https://huggingface.co/xinsir/controlnet-union-sdxl-1.0/tree/main" ) emb += self.control_add_embedding(control_type, emb.dtype, emb.device) if len(control_type) > 0: if len(hint.shape) < 5: hint = hint.unsqueeze(dim=0) guided_hint = self.union_controlnet_merge(hint, control_type, emb, context) if guided_hint is None: guided_hint = self.input_hint_block(hint, emb, context) out_output = [] out_middle = [] hs = [] if self.num_classes is not None: assert y.shape[0] == x.shape[0] emb = emb + self.label_emb(y) h = x for module, zero_conv in zip(self.input_blocks, self.zero_convs): if guided_hint is not None: h = module(h, emb, context) h += guided_hint guided_hint = None else: h = module(h, emb, context) out_output.append(zero_conv(h, emb, context)) h = self.middle_block(h, emb, context) out_middle.append(self.middle_block_out(h, emb, context)) return {"middle": out_middle, "output": out_output}