diff --git a/.github/workflows/windows_release_nightly_pytorch.yml b/.github/workflows/windows_release_nightly_pytorch.yml
index c7ef93ce1..319942e7c 100644
--- a/.github/workflows/windows_release_nightly_pytorch.yml
+++ b/.github/workflows/windows_release_nightly_pytorch.yml
@@ -31,7 +31,7 @@ jobs:
echo 'import site' >> ./python311._pth
curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
./python.exe get-pip.py
- python -m pip wheel torch torchvision torchaudio aiohttp==3.8.4 --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu121 -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir
+ python -m pip wheel torch torchvision torchaudio aiohttp==3.8.5 --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu121 -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir
ls ../temp_wheel_dir
./python.exe -s -m pip install --pre ../temp_wheel_dir/*
sed -i '1i../ComfyUI' ./python311._pth
diff --git a/comfy/cldm/cldm.py b/comfy/cldm/cldm.py
index 46fbf0a69..5201b3c26 100644
--- a/comfy/cldm/cldm.py
+++ b/comfy/cldm/cldm.py
@@ -6,8 +6,6 @@ import torch as th
import torch.nn as nn
from ..ldm.modules.diffusionmodules.util import (
- conv_nd,
- linear,
zero_module,
timestep_embedding,
)
@@ -15,7 +13,7 @@ from ..ldm.modules.diffusionmodules.util import (
from ..ldm.modules.attention import SpatialTransformer
from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample
from ..ldm.util import exists
-
+import comfy.ops
class ControlledUnetModel(UNetModel):
#implemented in the ldm unet
@@ -55,6 +53,8 @@ class ControlNet(nn.Module):
use_linear_in_transformer=False,
adm_in_channels=None,
transformer_depth_middle=None,
+ device=None,
+ operations=comfy.ops,
):
super().__init__()
assert use_spatial_transformer == True, "use_spatial_transformer has to be true"
@@ -117,9 +117,9 @@ class ControlNet(nn.Module):
time_embed_dim = model_channels * 4
self.time_embed = nn.Sequential(
- linear(model_channels, time_embed_dim),
+ operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),
nn.SiLU(),
- linear(time_embed_dim, time_embed_dim),
+ operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
)
if self.num_classes is not None:
@@ -132,9 +132,9 @@ class ControlNet(nn.Module):
assert adm_in_channels is not None
self.label_emb = nn.Sequential(
nn.Sequential(
- linear(adm_in_channels, time_embed_dim),
+ operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
nn.SiLU(),
- linear(time_embed_dim, time_embed_dim),
+ operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
)
)
else:
@@ -143,28 +143,28 @@ class ControlNet(nn.Module):
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(
- conv_nd(dims, in_channels, model_channels, 3, padding=1)
+ operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)
)
]
)
- self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)])
+ self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels, operations=operations)])
self.input_hint_block = TimestepEmbedSequential(
- conv_nd(dims, hint_channels, 16, 3, padding=1),
+ operations.conv_nd(dims, hint_channels, 16, 3, padding=1),
nn.SiLU(),
- conv_nd(dims, 16, 16, 3, padding=1),
+ operations.conv_nd(dims, 16, 16, 3, padding=1),
nn.SiLU(),
- conv_nd(dims, 16, 32, 3, padding=1, stride=2),
+ operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2),
nn.SiLU(),
- conv_nd(dims, 32, 32, 3, padding=1),
+ operations.conv_nd(dims, 32, 32, 3, padding=1),
nn.SiLU(),
- conv_nd(dims, 32, 96, 3, padding=1, stride=2),
+ operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2),
nn.SiLU(),
- conv_nd(dims, 96, 96, 3, padding=1),
+ operations.conv_nd(dims, 96, 96, 3, padding=1),
nn.SiLU(),
- conv_nd(dims, 96, 256, 3, padding=1, stride=2),
+ operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2),
nn.SiLU(),
- zero_module(conv_nd(dims, 256, model_channels, 3, padding=1))
+ zero_module(operations.conv_nd(dims, 256, model_channels, 3, padding=1))
)
self._feature_size = model_channels
@@ -182,6 +182,7 @@ class ControlNet(nn.Module):
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
+ operations=operations
)
]
ch = mult * model_channels
@@ -204,11 +205,11 @@ class ControlNet(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
+ use_checkpoint=use_checkpoint, operations=operations
)
)
self.input_blocks.append(TimestepEmbedSequential(*layers))
- self.zero_convs.append(self.make_zero_conv(ch))
+ self.zero_convs.append(self.make_zero_conv(ch, operations=operations))
self._feature_size += ch
input_block_chans.append(ch)
if level != len(channel_mult) - 1:
@@ -224,16 +225,17 @@ class ControlNet(nn.Module):
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
down=True,
+ operations=operations
)
if resblock_updown
else Downsample(
- ch, conv_resample, dims=dims, out_channels=out_ch
+ ch, conv_resample, dims=dims, out_channels=out_ch, operations=operations
)
)
)
ch = out_ch
input_block_chans.append(ch)
- self.zero_convs.append(self.make_zero_conv(ch))
+ self.zero_convs.append(self.make_zero_conv(ch, operations=operations))
ds *= 2
self._feature_size += ch
@@ -253,11 +255,12 @@ class ControlNet(nn.Module):
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
+ 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
+ use_checkpoint=use_checkpoint, operations=operations
),
ResBlock(
ch,
@@ -266,13 +269,14 @@ class ControlNet(nn.Module):
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
+ operations=operations
),
)
- self.middle_block_out = self.make_zero_conv(ch)
+ self.middle_block_out = self.make_zero_conv(ch, operations=operations)
self._feature_size += ch
- def make_zero_conv(self, channels):
- return TimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0)))
+ def make_zero_conv(self, channels, operations=None):
+ return TimestepEmbedSequential(zero_module(operations.conv_nd(self.dims, channels, channels, 1, padding=0)))
def forward(self, x, hint, timesteps, context, y=None, **kwargs):
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
diff --git a/comfy/clip_vision.py b/comfy/clip_vision.py
index 2c8603bbe..a887e51b5 100644
--- a/comfy/clip_vision.py
+++ b/comfy/clip_vision.py
@@ -50,18 +50,22 @@ def convert_to_transformers(sd, prefix):
if "{}proj".format(prefix) in sd_k:
sd['visual_projection.weight'] = sd.pop("{}proj".format(prefix)).transpose(0, 1)
- sd = transformers_convert(sd, prefix, "vision_model.", 32)
+ sd = transformers_convert(sd, prefix, "vision_model.", 48)
return sd
def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
if convert_keys:
sd = convert_to_transformers(sd, prefix)
- if "vision_model.encoder.layers.30.layer_norm1.weight" in sd:
+ if "vision_model.encoder.layers.47.layer_norm1.weight" in sd:
+ json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_g.json")
+ elif "vision_model.encoder.layers.30.layer_norm1.weight" in sd:
json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json")
else:
json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json")
clip = ClipVisionModel(json_config)
m, u = clip.load_sd(sd)
+ if len(m) > 0:
+ print("missing clip vision:", m)
u = set(u)
keys = list(sd.keys())
for k in keys:
@@ -72,4 +76,7 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
def load(ckpt_path):
sd = load_torch_file(ckpt_path)
- return load_clipvision_from_sd(sd)
+ if "visual.transformer.resblocks.0.attn.in_proj_weight" in sd:
+ return load_clipvision_from_sd(sd, prefix="visual.", convert_keys=True)
+ else:
+ return load_clipvision_from_sd(sd)
diff --git a/comfy/clip_vision_config_g.json b/comfy/clip_vision_config_g.json
new file mode 100644
index 000000000..708e7e21a
--- /dev/null
+++ b/comfy/clip_vision_config_g.json
@@ -0,0 +1,18 @@
+{
+ "attention_dropout": 0.0,
+ "dropout": 0.0,
+ "hidden_act": "gelu",
+ "hidden_size": 1664,
+ "image_size": 224,
+ "initializer_factor": 1.0,
+ "initializer_range": 0.02,
+ "intermediate_size": 8192,
+ "layer_norm_eps": 1e-05,
+ "model_type": "clip_vision_model",
+ "num_attention_heads": 16,
+ "num_channels": 3,
+ "num_hidden_layers": 48,
+ "patch_size": 14,
+ "projection_dim": 1280,
+ "torch_dtype": "float32"
+}
diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py
index beaa623f3..eb088d92b 100644
--- a/comfy/k_diffusion/sampling.py
+++ b/comfy/k_diffusion/sampling.py
@@ -649,7 +649,7 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
s_in = x.new_ones([x.shape[0]])
denoised_1, denoised_2 = None, None
- h_1, h_2 = None, None
+ h, h_1, h_2 = None, None, None
for i in trange(len(sigmas) - 1, disable=disable):
denoised = model(x, sigmas[i] * s_in, **extra_args)
diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py
index 573cea6ac..973619bf2 100644
--- a/comfy/ldm/modules/attention.py
+++ b/comfy/ldm/modules/attention.py
@@ -10,13 +10,14 @@ 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
import xformers.ops
from comfy.cli_args import args
+import comfy.ops
+
# CrossAttn precision handling
if args.dont_upcast_attention:
print("disabling upcasting of attention")
@@ -52,9 +53,9 @@ 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=comfy.ops):
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 +63,19 @@ 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=comfy.ops):
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 +149,7 @@ 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=comfy.ops):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
@@ -156,12 +157,12 @@ 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 +246,7 @@ 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=comfy.ops):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
@@ -253,12 +254,12 @@ 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 +344,7 @@ 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=comfy.ops):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
@@ -351,12 +352,12 @@ 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 +400,7 @@ 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=comfy.ops):
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 +410,11 @@ 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 +451,7 @@ 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=comfy.ops):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
@@ -458,11 +459,11 @@ 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 +509,14 @@ 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=comfy.ops):
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 +649,7 @@ 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=comfy.ops):
super().__init__()
if exists(context_dim) and not isinstance(context_dim, list):
context_dim = [context_dim] * depth
@@ -656,26 +657,26 @@ 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={}):
diff --git a/comfy/ldm/modules/diffusionmodules/openaimodel.py b/comfy/ldm/modules/diffusionmodules/openaimodel.py
index 90c153465..11cec0eda 100644
--- a/comfy/ldm/modules/diffusionmodules/openaimodel.py
+++ b/comfy/ldm/modules/diffusionmodules/openaimodel.py
@@ -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=comfy.ops):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.dims = dims
if use_conv:
- self.conv = 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=comfy.ops):
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=comfy.ops
):
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
)
diff --git a/comfy/model_base.py b/comfy/model_base.py
index ad661ec7d..979e2c65e 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -148,13 +148,20 @@ class SDInpaint(BaseModel):
super().__init__(model_config, model_type, device=device)
self.concat_keys = ("mask", "masked_image")
+def sdxl_pooled(args, noise_augmentor):
+ if "unclip_conditioning" in args:
+ return unclip_adm(args.get("unclip_conditioning", None), args["device"], noise_augmentor)[:,:1280]
+ else:
+ return args["pooled_output"]
+
class SDXLRefiner(BaseModel):
def __init__(self, model_config, model_type=ModelType.EPS, device=None):
super().__init__(model_config, model_type, device=device)
self.embedder = Timestep(256)
+ self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280})
def encode_adm(self, **kwargs):
- clip_pooled = kwargs["pooled_output"]
+ clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor)
width = kwargs.get("width", 768)
height = kwargs.get("height", 768)
crop_w = kwargs.get("crop_w", 0)
@@ -178,9 +185,10 @@ class SDXL(BaseModel):
def __init__(self, model_config, model_type=ModelType.EPS, device=None):
super().__init__(model_config, model_type, device=device)
self.embedder = Timestep(256)
+ self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280})
def encode_adm(self, **kwargs):
- clip_pooled = kwargs["pooled_output"]
+ clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor)
width = kwargs.get("width", 768)
height = kwargs.get("height", 768)
crop_w = kwargs.get("crop_w", 0)
diff --git a/comfy/ops.py b/comfy/ops.py
index 2e72030bd..678c2c6d0 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -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
diff --git a/comfy/sample.py b/comfy/sample.py
index 1dfca4204..d7292024e 100644
--- a/comfy/sample.py
+++ b/comfy/sample.py
@@ -53,7 +53,7 @@ def get_models_from_cond(cond, model_type):
def get_additional_models(positive, negative):
"""loads additional models in positive and negative conditioning"""
- control_nets = get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control")
+ control_nets = set(get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control"))
control_models = []
for m in control_nets:
@@ -78,7 +78,7 @@ def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative
real_model = None
models = get_additional_models(positive, negative)
- comfy.model_management.load_models_gpu([model] + models, comfy.model_management.batch_area_memory(noise.shape[2] * noise.shape[3]))
+ comfy.model_management.load_models_gpu([model] + models, comfy.model_management.batch_area_memory(noise.shape[0] * noise.shape[2] * noise.shape[3]))
real_model = model.model
noise = noise.to(device)
diff --git a/comfy/samplers.py b/comfy/samplers.py
index ee37913e6..134336de6 100644
--- a/comfy/samplers.py
+++ b/comfy/samplers.py
@@ -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 = []
diff --git a/comfy/sd.py b/comfy/sd.py
index 461c234db..48b1a8ce7 100644
--- a/comfy/sd.py
+++ b/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
diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py
index a138b292e..51bdb24fa 100644
--- a/comfy_extras/nodes_post_processing.py
+++ b/comfy_extras/nodes_post_processing.py
@@ -2,6 +2,7 @@ import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
+import math
import comfy.utils
@@ -209,9 +210,36 @@ class Sharpen:
return (result,)
+class ImageScaleToTotalPixels:
+ upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic"]
+ crop_methods = ["disabled", "center"]
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,),
+ "megapixels": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 16.0, "step": 0.01}),
+ }}
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "upscale"
+
+ CATEGORY = "image/upscaling"
+
+ def upscale(self, image, upscale_method, megapixels):
+ samples = image.movedim(-1,1)
+ total = int(megapixels * 1024 * 1024)
+
+ scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
+ width = round(samples.shape[3] * scale_by)
+ height = round(samples.shape[2] * scale_by)
+
+ s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled")
+ s = s.movedim(1,-1)
+ return (s,)
+
NODE_CLASS_MAPPINGS = {
"ImageBlend": Blend,
"ImageBlur": Blur,
"ImageQuantize": Quantize,
"ImageSharpen": Sharpen,
+ "ImageScaleToTotalPixels": ImageScaleToTotalPixels,
}
diff --git a/web/index.html b/web/index.html
index 71067d993..41bc246c0 100644
--- a/web/index.html
+++ b/web/index.html
@@ -6,6 +6,7 @@
+