mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-01-25 15:55:18 +00:00
Support for Control Loras.
Control loras are controlnets where some of the weights are stored in "lora" format: an up and a down low rank matrice that when multiplied together and added to the unet weight give the controlnet weight. This allows a much smaller memory footprint depending on the rank of the matrices. These controlnets are used just like regular ones.
This commit is contained in:
comfyanonymous
parent
39ac856a33
commit
d6e4b342e6
@ -6,8 +6,6 @@ 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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conv_nd,
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linear,
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zero_module,
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timestep_embedding,
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)
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@ -15,7 +13,7 @@ from ..ldm.modules.diffusionmodules.util import (
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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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import comfy.ops
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class ControlledUnetModel(UNetModel):
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#implemented in the ldm unet
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@ -55,6 +53,8 @@ class ControlNet(nn.Module):
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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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@ -117,9 +117,9 @@ class ControlNet(nn.Module):
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time_embed_dim = model_channels * 4
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self.time_embed = nn.Sequential(
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linear(model_channels, time_embed_dim),
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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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linear(time_embed_dim, time_embed_dim),
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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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@ -132,9 +132,9 @@ class ControlNet(nn.Module):
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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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linear(adm_in_channels, time_embed_dim),
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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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linear(time_embed_dim, time_embed_dim),
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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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@ -143,28 +143,28 @@ class ControlNet(nn.Module):
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self.input_blocks = nn.ModuleList(
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[
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TimestepEmbedSequential(
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conv_nd(dims, in_channels, model_channels, 3, padding=1)
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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)])
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self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels, operations=operations)])
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self.input_hint_block = TimestepEmbedSequential(
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conv_nd(dims, hint_channels, 16, 3, padding=1),
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operations.conv_nd(dims, hint_channels, 16, 3, padding=1),
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nn.SiLU(),
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conv_nd(dims, 16, 16, 3, padding=1),
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operations.conv_nd(dims, 16, 16, 3, padding=1),
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nn.SiLU(),
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conv_nd(dims, 16, 32, 3, padding=1, stride=2),
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operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2),
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nn.SiLU(),
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conv_nd(dims, 32, 32, 3, padding=1),
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operations.conv_nd(dims, 32, 32, 3, padding=1),
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nn.SiLU(),
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conv_nd(dims, 32, 96, 3, padding=1, stride=2),
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operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2),
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nn.SiLU(),
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conv_nd(dims, 96, 96, 3, padding=1),
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operations.conv_nd(dims, 96, 96, 3, padding=1),
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nn.SiLU(),
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conv_nd(dims, 96, 256, 3, padding=1, stride=2),
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operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2),
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nn.SiLU(),
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zero_module(conv_nd(dims, 256, model_channels, 3, padding=1))
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zero_module(operations.conv_nd(dims, 256, model_channels, 3, padding=1))
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)
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self._feature_size = model_channels
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@ -182,6 +182,7 @@ class ControlNet(nn.Module):
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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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operations=operations
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)
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]
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ch = mult * model_channels
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@ -204,11 +205,11 @@ class ControlNet(nn.Module):
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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
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use_checkpoint=use_checkpoint, 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))
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self.zero_convs.append(self.make_zero_conv(ch, operations=operations))
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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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@ -224,16 +225,17 @@ class ControlNet(nn.Module):
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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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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
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ch, conv_resample, dims=dims, out_channels=out_ch, 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))
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self.zero_convs.append(self.make_zero_conv(ch, operations=operations))
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ds *= 2
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self._feature_size += ch
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@ -253,11 +255,12 @@ class ControlNet(nn.Module):
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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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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
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use_checkpoint=use_checkpoint, operations=operations
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),
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ResBlock(
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ch,
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@ -266,13 +269,14 @@ class ControlNet(nn.Module):
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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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operations=operations
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),
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)
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self.middle_block_out = self.make_zero_conv(ch)
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self.middle_block_out = self.make_zero_conv(ch, operations=operations)
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self._feature_size += ch
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def make_zero_conv(self, channels):
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return TimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0)))
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def make_zero_conv(self, channels, operations=None):
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return TimestepEmbedSequential(zero_module(operations.conv_nd(self.dims, channels, channels, 1, padding=0)))
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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)
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@ -10,7 +10,6 @@ from .diffusionmodules.util import checkpoint
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from .sub_quadratic_attention import efficient_dot_product_attention
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from comfy import model_management
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import comfy.ops
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if model_management.xformers_enabled():
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import xformers
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@ -52,9 +51,9 @@ def init_(tensor):
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# feedforward
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class GEGLU(nn.Module):
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def __init__(self, dim_in, dim_out, dtype=None, device=None):
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def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=None):
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super().__init__()
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self.proj = comfy.ops.Linear(dim_in, dim_out * 2, dtype=dtype, device=device)
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self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device)
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def forward(self, x):
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x, gate = self.proj(x).chunk(2, dim=-1)
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@ -62,19 +61,19 @@ class GEGLU(nn.Module):
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class FeedForward(nn.Module):
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def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None):
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def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=None):
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super().__init__()
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inner_dim = int(dim * mult)
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dim_out = default(dim_out, dim)
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project_in = nn.Sequential(
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comfy.ops.Linear(dim, inner_dim, dtype=dtype, device=device),
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operations.Linear(dim, inner_dim, dtype=dtype, device=device),
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nn.GELU()
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) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device)
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) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device, operations=operations)
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self.net = nn.Sequential(
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project_in,
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nn.Dropout(dropout),
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comfy.ops.Linear(inner_dim, dim_out, dtype=dtype, device=device)
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operations.Linear(inner_dim, dim_out, dtype=dtype, device=device)
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)
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def forward(self, x):
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@ -148,7 +147,7 @@ class SpatialSelfAttention(nn.Module):
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class CrossAttentionBirchSan(nn.Module):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=None):
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super().__init__()
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inner_dim = dim_head * heads
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context_dim = default(context_dim, query_dim)
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@ -156,12 +155,12 @@ class CrossAttentionBirchSan(nn.Module):
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self.scale = dim_head ** -0.5
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self.heads = heads
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self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_out = nn.Sequential(
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comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device),
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operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
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nn.Dropout(dropout)
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)
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@ -245,7 +244,7 @@ class CrossAttentionBirchSan(nn.Module):
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class CrossAttentionDoggettx(nn.Module):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=None):
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super().__init__()
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inner_dim = dim_head * heads
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context_dim = default(context_dim, query_dim)
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@ -253,12 +252,12 @@ class CrossAttentionDoggettx(nn.Module):
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self.scale = dim_head ** -0.5
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self.heads = heads
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self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_out = nn.Sequential(
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comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device),
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operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
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nn.Dropout(dropout)
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)
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@ -343,7 +342,7 @@ class CrossAttentionDoggettx(nn.Module):
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return self.to_out(r2)
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class CrossAttention(nn.Module):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=None):
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super().__init__()
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inner_dim = dim_head * heads
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context_dim = default(context_dim, query_dim)
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@ -351,12 +350,12 @@ class CrossAttention(nn.Module):
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self.scale = dim_head ** -0.5
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self.heads = heads
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self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_out = nn.Sequential(
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comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device),
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operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
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nn.Dropout(dropout)
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)
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@ -399,7 +398,7 @@ class CrossAttention(nn.Module):
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class MemoryEfficientCrossAttention(nn.Module):
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# https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, dtype=None, device=None):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, dtype=None, device=None, operations=None):
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super().__init__()
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print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
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f"{heads} heads.")
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@ -409,11 +408,11 @@ class MemoryEfficientCrossAttention(nn.Module):
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self.heads = heads
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self.dim_head = dim_head
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self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
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self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
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self.attention_op: Optional[Any] = None
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def forward(self, x, context=None, value=None, mask=None):
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@ -450,7 +449,7 @@ class MemoryEfficientCrossAttention(nn.Module):
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return self.to_out(out)
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class CrossAttentionPytorch(nn.Module):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=None):
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super().__init__()
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inner_dim = dim_head * heads
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context_dim = default(context_dim, query_dim)
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@ -458,11 +457,11 @@ class CrossAttentionPytorch(nn.Module):
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self.heads = heads
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self.dim_head = dim_head
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self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
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self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
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self.attention_op: Optional[Any] = None
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def forward(self, x, context=None, value=None, mask=None):
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@ -508,14 +507,14 @@ else:
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class BasicTransformerBlock(nn.Module):
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def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
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disable_self_attn=False, dtype=None, device=None):
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disable_self_attn=False, dtype=None, device=None, operations=None):
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super().__init__()
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self.disable_self_attn = disable_self_attn
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self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
|
||||
context_dim=context_dim if self.disable_self_attn else None, dtype=dtype, device=device) # is a self-attention if not self.disable_self_attn
|
||||
self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device)
|
||||
context_dim=context_dim if self.disable_self_attn else None, dtype=dtype, device=device, operations=operations) # is a self-attention if not self.disable_self_attn
|
||||
self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations)
|
||||
self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
|
||||
heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype, device=device) # is self-attn if context is none
|
||||
heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype, device=device, operations=operations) # is self-attn if context is none
|
||||
self.norm1 = nn.LayerNorm(dim, dtype=dtype, device=device)
|
||||
self.norm2 = nn.LayerNorm(dim, dtype=dtype, device=device)
|
||||
self.norm3 = nn.LayerNorm(dim, dtype=dtype, device=device)
|
||||
@ -648,7 +647,7 @@ class SpatialTransformer(nn.Module):
|
||||
def __init__(self, in_channels, n_heads, d_head,
|
||||
depth=1, dropout=0., context_dim=None,
|
||||
disable_self_attn=False, use_linear=False,
|
||||
use_checkpoint=True, dtype=None, device=None):
|
||||
use_checkpoint=True, dtype=None, device=None, operations=None):
|
||||
super().__init__()
|
||||
if exists(context_dim) and not isinstance(context_dim, list):
|
||||
context_dim = [context_dim] * depth
|
||||
@ -656,26 +655,26 @@ class SpatialTransformer(nn.Module):
|
||||
inner_dim = n_heads * d_head
|
||||
self.norm = Normalize(in_channels, dtype=dtype, device=device)
|
||||
if not use_linear:
|
||||
self.proj_in = nn.Conv2d(in_channels,
|
||||
self.proj_in = operations.Conv2d(in_channels,
|
||||
inner_dim,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0, dtype=dtype, device=device)
|
||||
else:
|
||||
self.proj_in = comfy.ops.Linear(in_channels, inner_dim, dtype=dtype, device=device)
|
||||
self.proj_in = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device)
|
||||
|
||||
self.transformer_blocks = nn.ModuleList(
|
||||
[BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
|
||||
disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, dtype=dtype, device=device)
|
||||
disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, dtype=dtype, device=device, operations=operations)
|
||||
for d in range(depth)]
|
||||
)
|
||||
if not use_linear:
|
||||
self.proj_out = nn.Conv2d(inner_dim,in_channels,
|
||||
self.proj_out = operations.Conv2d(inner_dim,in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0, dtype=dtype, device=device)
|
||||
else:
|
||||
self.proj_out = comfy.ops.Linear(in_channels, inner_dim, dtype=dtype, device=device)
|
||||
self.proj_out = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device)
|
||||
self.use_linear = use_linear
|
||||
|
||||
def forward(self, x, context=None, transformer_options={}):
|
||||
|
||||
@ -8,8 +8,6 @@ import torch.nn.functional as F
|
||||
|
||||
from .util import (
|
||||
checkpoint,
|
||||
conv_nd,
|
||||
linear,
|
||||
avg_pool_nd,
|
||||
zero_module,
|
||||
normalization,
|
||||
@ -17,7 +15,7 @@ from .util import (
|
||||
)
|
||||
from ..attention import SpatialTransformer
|
||||
from comfy.ldm.util import exists
|
||||
|
||||
import comfy.ops
|
||||
|
||||
class TimestepBlock(nn.Module):
|
||||
"""
|
||||
@ -72,14 +70,14 @@ class Upsample(nn.Module):
|
||||
upsampling occurs in the inner-two dimensions.
|
||||
"""
|
||||
|
||||
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None):
|
||||
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=None):
|
||||
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 = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype, device=device)
|
||||
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
|
||||
@ -108,7 +106,7 @@ class Downsample(nn.Module):
|
||||
downsampling occurs in the inner-two dimensions.
|
||||
"""
|
||||
|
||||
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None):
|
||||
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=None):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.out_channels = out_channels or channels
|
||||
@ -116,7 +114,7 @@ class Downsample(nn.Module):
|
||||
self.dims = dims
|
||||
stride = 2 if dims != 3 else (1, 2, 2)
|
||||
if use_conv:
|
||||
self.op = conv_nd(
|
||||
self.op = operations.conv_nd(
|
||||
dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype, device=device
|
||||
)
|
||||
else:
|
||||
@ -158,6 +156,7 @@ class ResBlock(TimestepBlock):
|
||||
down=False,
|
||||
dtype=None,
|
||||
device=None,
|
||||
operations=None
|
||||
):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
@ -171,7 +170,7 @@ class ResBlock(TimestepBlock):
|
||||
self.in_layers = nn.Sequential(
|
||||
nn.GroupNorm(32, channels, dtype=dtype, device=device),
|
||||
nn.SiLU(),
|
||||
conv_nd(dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device),
|
||||
operations.conv_nd(dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device),
|
||||
)
|
||||
|
||||
self.updown = up or down
|
||||
@ -187,7 +186,7 @@ class ResBlock(TimestepBlock):
|
||||
|
||||
self.emb_layers = nn.Sequential(
|
||||
nn.SiLU(),
|
||||
linear(
|
||||
operations.Linear(
|
||||
emb_channels,
|
||||
2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device
|
||||
),
|
||||
@ -197,18 +196,18 @@ class ResBlock(TimestepBlock):
|
||||
nn.SiLU(),
|
||||
nn.Dropout(p=dropout),
|
||||
zero_module(
|
||||
conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1, dtype=dtype, device=device)
|
||||
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 = conv_nd(
|
||||
self.skip_connection = operations.conv_nd(
|
||||
dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device
|
||||
)
|
||||
else:
|
||||
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device)
|
||||
self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device)
|
||||
|
||||
def forward(self, x, emb):
|
||||
"""
|
||||
@ -317,6 +316,7 @@ class UNetModel(nn.Module):
|
||||
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"
|
||||
@ -379,9 +379,9 @@ class UNetModel(nn.Module):
|
||||
|
||||
time_embed_dim = model_channels * 4
|
||||
self.time_embed = nn.Sequential(
|
||||
linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),
|
||||
operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),
|
||||
nn.SiLU(),
|
||||
linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
|
||||
operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
|
||||
)
|
||||
|
||||
if self.num_classes is not None:
|
||||
@ -394,9 +394,9 @@ class UNetModel(nn.Module):
|
||||
assert adm_in_channels is not None
|
||||
self.label_emb = nn.Sequential(
|
||||
nn.Sequential(
|
||||
linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
|
||||
operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
|
||||
nn.SiLU(),
|
||||
linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
|
||||
operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
|
||||
)
|
||||
)
|
||||
else:
|
||||
@ -405,7 +405,7 @@ class UNetModel(nn.Module):
|
||||
self.input_blocks = nn.ModuleList(
|
||||
[
|
||||
TimestepEmbedSequential(
|
||||
conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)
|
||||
operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)
|
||||
)
|
||||
]
|
||||
)
|
||||
@ -426,6 +426,7 @@ class UNetModel(nn.Module):
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
dtype=self.dtype,
|
||||
device=device,
|
||||
operations=operations,
|
||||
)
|
||||
]
|
||||
ch = mult * model_channels
|
||||
@ -447,7 +448,7 @@ class UNetModel(nn.Module):
|
||||
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
|
||||
use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
|
||||
)
|
||||
)
|
||||
self.input_blocks.append(TimestepEmbedSequential(*layers))
|
||||
@ -468,10 +469,11 @@ class UNetModel(nn.Module):
|
||||
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
|
||||
ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations
|
||||
)
|
||||
)
|
||||
)
|
||||
@ -498,11 +500,12 @@ class UNetModel(nn.Module):
|
||||
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
|
||||
use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
|
||||
),
|
||||
ResBlock(
|
||||
ch,
|
||||
@ -513,6 +516,7 @@ class UNetModel(nn.Module):
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
dtype=self.dtype,
|
||||
device=device,
|
||||
operations=operations
|
||||
),
|
||||
)
|
||||
self._feature_size += ch
|
||||
@ -532,6 +536,7 @@ class UNetModel(nn.Module):
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
dtype=self.dtype,
|
||||
device=device,
|
||||
operations=operations
|
||||
)
|
||||
]
|
||||
ch = model_channels * mult
|
||||
@ -554,7 +559,7 @@ class UNetModel(nn.Module):
|
||||
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
|
||||
use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
|
||||
)
|
||||
)
|
||||
if level and i == self.num_res_blocks[level]:
|
||||
@ -571,9 +576,10 @@ class UNetModel(nn.Module):
|
||||
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)
|
||||
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))
|
||||
@ -582,12 +588,12 @@ class UNetModel(nn.Module):
|
||||
self.out = nn.Sequential(
|
||||
nn.GroupNorm(32, ch, dtype=self.dtype, device=device),
|
||||
nn.SiLU(),
|
||||
zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device)),
|
||||
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),
|
||||
conv_nd(dims, model_channels, n_embed, 1, 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
|
||||
)
|
||||
|
||||
|
||||
@ -21,6 +21,11 @@ class Conv2d(torch.nn.Conv2d):
|
||||
def reset_parameters(self):
|
||||
return None
|
||||
|
||||
def conv_nd(dims, *args, **kwargs):
|
||||
if dims == 2:
|
||||
return Conv2d(*args, **kwargs)
|
||||
else:
|
||||
raise ValueError(f"unsupported dimensions: {dims}")
|
||||
|
||||
@contextmanager
|
||||
def use_comfy_ops(): # Kind of an ugly hack but I can't think of a better way
|
||||
|
||||
@ -478,7 +478,7 @@ def pre_run_control(model, conds):
|
||||
timestep_end = None
|
||||
percent_to_timestep_function = lambda a: model.sigma_to_t(model.t_to_sigma(torch.tensor(a) * 999.0))
|
||||
if 'control' in x[1]:
|
||||
x[1]['control'].pre_run(model.inner_model, percent_to_timestep_function)
|
||||
x[1]['control'].pre_run(model.inner_model.inner_model, percent_to_timestep_function)
|
||||
|
||||
def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func):
|
||||
cond_cnets = []
|
||||
|
||||
110
comfy/sd.py
110
comfy/sd.py
@ -844,9 +844,119 @@ class ControlNet(ControlBase):
|
||||
out.append(self.control_model_wrapped)
|
||||
return out
|
||||
|
||||
class ControlLoraOps:
|
||||
class Linear(torch.nn.Module):
|
||||
def __init__(self, in_features: int, out_features: int, bias: bool = True,
|
||||
device=None, dtype=None) -> None:
|
||||
factory_kwargs = {'device': device, 'dtype': dtype}
|
||||
super().__init__()
|
||||
self.in_features = in_features
|
||||
self.out_features = out_features
|
||||
self.weight = None
|
||||
self.up = None
|
||||
self.down = None
|
||||
self.bias = None
|
||||
|
||||
def forward(self, input):
|
||||
if self.up is not None:
|
||||
return torch.nn.functional.linear(input, self.weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(self.weight.dtype), self.bias)
|
||||
else:
|
||||
return torch.nn.functional.linear(input, self.weight, self.bias)
|
||||
|
||||
class Conv2d(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
kernel_size,
|
||||
stride=1,
|
||||
padding=0,
|
||||
dilation=1,
|
||||
groups=1,
|
||||
bias=True,
|
||||
padding_mode='zeros',
|
||||
device=None,
|
||||
dtype=None
|
||||
):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = out_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.stride = stride
|
||||
self.padding = padding
|
||||
self.dilation = dilation
|
||||
self.transposed = False
|
||||
self.output_padding = 0
|
||||
self.groups = groups
|
||||
self.padding_mode = padding_mode
|
||||
|
||||
self.weight = None
|
||||
self.bias = None
|
||||
self.up = None
|
||||
self.down = None
|
||||
|
||||
|
||||
def forward(self, input):
|
||||
if self.up is not None:
|
||||
return torch.nn.functional.conv2d(input, self.weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(self.weight.dtype), self.bias, self.stride, self.padding, self.dilation, self.groups)
|
||||
else:
|
||||
return torch.nn.functional.conv2d(input, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
|
||||
|
||||
def conv_nd(self, dims, *args, **kwargs):
|
||||
if dims == 2:
|
||||
return self.Conv2d(*args, **kwargs)
|
||||
else:
|
||||
raise ValueError(f"unsupported dimensions: {dims}")
|
||||
|
||||
|
||||
class ControlLora(ControlNet):
|
||||
def __init__(self, control_weights, global_average_pooling=False, device=None):
|
||||
ControlBase.__init__(self, device)
|
||||
self.control_weights = control_weights
|
||||
self.global_average_pooling = global_average_pooling
|
||||
|
||||
def pre_run(self, model, percent_to_timestep_function):
|
||||
super().pre_run(model, percent_to_timestep_function)
|
||||
controlnet_config = model.model_config.unet_config.copy()
|
||||
controlnet_config.pop("out_channels")
|
||||
controlnet_config["hint_channels"] = self.control_weights["input_hint_block.0.weight"].shape[1]
|
||||
controlnet_config["operations"] = ControlLoraOps()
|
||||
self.control_model = cldm.ControlNet(**controlnet_config)
|
||||
if model_management.should_use_fp16():
|
||||
self.control_model.half()
|
||||
self.control_model.to(model_management.get_torch_device())
|
||||
diffusion_model = model.diffusion_model
|
||||
sd = diffusion_model.state_dict()
|
||||
cm = self.control_model.state_dict()
|
||||
|
||||
for k in sd:
|
||||
try:
|
||||
set_attr(self.control_model, k, sd[k])
|
||||
except:
|
||||
pass
|
||||
|
||||
for k in self.control_weights:
|
||||
if k not in {"lora_controlnet"}:
|
||||
set_attr(self.control_model, k, self.control_weights[k].to(model_management.get_torch_device()))
|
||||
|
||||
def copy(self):
|
||||
c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling)
|
||||
self.copy_to(c)
|
||||
return c
|
||||
|
||||
def cleanup(self):
|
||||
del self.control_model
|
||||
self.control_model = None
|
||||
super().cleanup()
|
||||
|
||||
def get_models(self):
|
||||
out = ControlBase.get_models(self)
|
||||
return out
|
||||
|
||||
def load_controlnet(ckpt_path, model=None):
|
||||
controlnet_data = utils.load_torch_file(ckpt_path, safe_load=True)
|
||||
if "lora_controlnet" in controlnet_data:
|
||||
return ControlLora(controlnet_data)
|
||||
|
||||
controlnet_config = None
|
||||
if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format
|
||||
|
||||
Loading…
Reference in New Issue
Block a user