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