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Support for Control Loras.

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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
2023-08-18 02:46:11 -04:00
parent 39ac856a33
commit d6e4b342e6
6 changed files with 216 additions and 92 deletions
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83
comfy/ldm/modules/attention.py
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@@ -10,7 +10,6 @@ from .diffusionmodules.util import checkpoint
from .sub_quadratic_attention import efficient_dot_product_attention
from comfy import model_management
import comfy.ops
if model_management.xformers_enabled():
import xformers
@@ -52,9 +51,9 @@ def init_(tensor):
# feedforward
class GEGLU(nn.Module):
def __init__(self, dim_in, dim_out, dtype=None, device=None):
def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=None):
super().__init__()
self.proj = comfy.ops.Linear(dim_in, dim_out * 2, dtype=dtype, device=device)
self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device)
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=-1)
@@ -62,19 +61,19 @@ class GEGLU(nn.Module):
class FeedForward(nn.Module):
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None):
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=None):
super().__init__()
inner_dim = int(dim * mult)
dim_out = default(dim_out, dim)
project_in = nn.Sequential(
comfy.ops.Linear(dim, inner_dim, dtype=dtype, device=device),
operations.Linear(dim, inner_dim, dtype=dtype, device=device),
nn.GELU()
) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device)
) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device, operations=operations)
self.net = nn.Sequential(
project_in,
nn.Dropout(dropout),
comfy.ops.Linear(inner_dim, dim_out, dtype=dtype, device=device)
operations.Linear(inner_dim, dim_out, dtype=dtype, device=device)
)
def forward(self, x):
@@ -148,7 +147,7 @@ class SpatialSelfAttention(nn.Module):
class CrossAttentionBirchSan(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=None):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
@@ -156,12 +155,12 @@ class CrossAttentionBirchSan(nn.Module):
self.scale = dim_head ** -0.5
self.heads = heads
self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_out = nn.Sequential(
comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device),
operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
nn.Dropout(dropout)
)
@@ -245,7 +244,7 @@ class CrossAttentionBirchSan(nn.Module):
class CrossAttentionDoggettx(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=None):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
@@ -253,12 +252,12 @@ class CrossAttentionDoggettx(nn.Module):
self.scale = dim_head ** -0.5
self.heads = heads
self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_out = nn.Sequential(
comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device),
operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
nn.Dropout(dropout)
)
@@ -343,7 +342,7 @@ class CrossAttentionDoggettx(nn.Module):
return self.to_out(r2)
class CrossAttention(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=None):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
@@ -351,12 +350,12 @@ class CrossAttention(nn.Module):
self.scale = dim_head ** -0.5
self.heads = heads
self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_out = nn.Sequential(
comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device),
operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
nn.Dropout(dropout)
)
@@ -399,7 +398,7 @@ class CrossAttention(nn.Module):
class MemoryEfficientCrossAttention(nn.Module):
# https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, dtype=None, device=None):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, dtype=None, device=None, operations=None):
super().__init__()
print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
f"{heads} heads.")
@@ -409,11 +408,11 @@ class MemoryEfficientCrossAttention(nn.Module):
self.heads = heads
self.dim_head = dim_head
self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
self.attention_op: Optional[Any] = None
def forward(self, x, context=None, value=None, mask=None):
@@ -450,7 +449,7 @@ class MemoryEfficientCrossAttention(nn.Module):
return self.to_out(out)
class CrossAttentionPytorch(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=None):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
@@ -458,11 +457,11 @@ class CrossAttentionPytorch(nn.Module):
self.heads = heads
self.dim_head = dim_head
self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
self.attention_op: Optional[Any] = None
def forward(self, x, context=None, value=None, mask=None):
@@ -508,14 +507,14 @@ else:
class BasicTransformerBlock(nn.Module):
def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
disable_self_attn=False, dtype=None, device=None):
disable_self_attn=False, dtype=None, device=None, operations=None):
super().__init__()
self.disable_self_attn = disable_self_attn
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={}):