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import torch.nn as nn
import torch
import cv2
import numpy as np
import comfy.model_management
from comfy.model_patcher import ModelPatcher
from tqdm import tqdm
from typing import Optional, Tuple
from ..libs.utils import install_package
from packaging import version
try:
install_package("diffusers", "0.27.2", True, "0.25.0")
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.modeling_utils import ModelMixin
from diffusers import __version__
if __version__:
if version.parse(__version__) < version.parse("0.26.0"):
from diffusers.models.unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block
else:
from diffusers.models.unets.unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block
import functools
def zero_module(module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module
class LatentTransparencyOffsetEncoder(torch.nn.Module):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.blocks = torch.nn.Sequential(
torch.nn.Conv2d(4, 32, kernel_size=3, padding=1, stride=1),
nn.SiLU(),
torch.nn.Conv2d(32, 32, kernel_size=3, padding=1, stride=1),
nn.SiLU(),
torch.nn.Conv2d(32, 64, kernel_size=3, padding=1, stride=2),
nn.SiLU(),
torch.nn.Conv2d(64, 64, kernel_size=3, padding=1, stride=1),
nn.SiLU(),
torch.nn.Conv2d(64, 128, kernel_size=3, padding=1, stride=2),
nn.SiLU(),
torch.nn.Conv2d(128, 128, kernel_size=3, padding=1, stride=1),
nn.SiLU(),
torch.nn.Conv2d(128, 256, kernel_size=3, padding=1, stride=2),
nn.SiLU(),
torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=1),
nn.SiLU(),
zero_module(torch.nn.Conv2d(256, 4, kernel_size=3, padding=1, stride=1)),
)
def __call__(self, x):
return self.blocks(x)
# 1024 * 1024 * 3 -> 16 * 16 * 512 -> 1024 * 1024 * 3
class UNet1024(ModelMixin, ConfigMixin):
@register_to_config
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
down_block_types: Tuple[str] = (
"DownBlock2D",
"DownBlock2D",
"DownBlock2D",
"DownBlock2D",
"AttnDownBlock2D",
"AttnDownBlock2D",
"AttnDownBlock2D",
),
up_block_types: Tuple[str] = (
"AttnUpBlock2D",
"AttnUpBlock2D",
"AttnUpBlock2D",
"UpBlock2D",
"UpBlock2D",
"UpBlock2D",
"UpBlock2D",
),
block_out_channels: Tuple[int] = (32, 32, 64, 128, 256, 512, 512),
layers_per_block: int = 2,
mid_block_scale_factor: float = 1,
downsample_padding: int = 1,
downsample_type: str = "conv",
upsample_type: str = "conv",
dropout: float = 0.0,
act_fn: str = "silu",
attention_head_dim: Optional[int] = 8,
norm_num_groups: int = 4,
norm_eps: float = 1e-5,
):
super().__init__()
# input
self.conv_in = nn.Conv2d(
in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1)
)
self.latent_conv_in = zero_module(
nn.Conv2d(4, block_out_channels[2], kernel_size=1)
)
self.down_blocks = nn.ModuleList([])
self.mid_block = None
self.up_blocks = nn.ModuleList([])
# down
output_channel = block_out_channels[0]
for i, down_block_type in enumerate(down_block_types):
input_channel = output_channel
output_channel = block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1
down_block = get_down_block(
down_block_type,
num_layers=layers_per_block,
in_channels=input_channel,
out_channels=output_channel,
temb_channels=None,
add_downsample=not is_final_block,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
attention_head_dim=(
attention_head_dim
if attention_head_dim is not None
else output_channel
),
downsample_padding=downsample_padding,
resnet_time_scale_shift="default",
downsample_type=downsample_type,
dropout=dropout,
)
self.down_blocks.append(down_block)
# mid
self.mid_block = UNetMidBlock2D(
in_channels=block_out_channels[-1],
temb_channels=None,
dropout=dropout,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
output_scale_factor=mid_block_scale_factor,
resnet_time_scale_shift="default",
attention_head_dim=(
attention_head_dim
if attention_head_dim is not None
else block_out_channels[-1]
),
resnet_groups=norm_num_groups,
attn_groups=None,
add_attention=True,
)
# up
reversed_block_out_channels = list(reversed(block_out_channels))
output_channel = reversed_block_out_channels[0]
for i, up_block_type in enumerate(up_block_types):
prev_output_channel = output_channel
output_channel = reversed_block_out_channels[i]
input_channel = reversed_block_out_channels[
min(i + 1, len(block_out_channels) - 1)
]
is_final_block = i == len(block_out_channels) - 1
up_block = get_up_block(
up_block_type,
num_layers=layers_per_block + 1,
in_channels=input_channel,
out_channels=output_channel,
prev_output_channel=prev_output_channel,
temb_channels=None,
add_upsample=not is_final_block,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
attention_head_dim=(
attention_head_dim
if attention_head_dim is not None
else output_channel
),
resnet_time_scale_shift="default",
upsample_type=upsample_type,
dropout=dropout,
)
self.up_blocks.append(up_block)
prev_output_channel = output_channel
# out
self.conv_norm_out = nn.GroupNorm(
num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
)
self.conv_act = nn.SiLU()
self.conv_out = nn.Conv2d(
block_out_channels[0], out_channels, kernel_size=3, padding=1
)
def forward(self, x, latent):
sample_latent = self.latent_conv_in(latent)
sample = self.conv_in(x)
emb = None
down_block_res_samples = (sample,)
for i, downsample_block in enumerate(self.down_blocks):
if i == 3:
sample = sample + sample_latent
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
down_block_res_samples += res_samples
sample = self.mid_block(sample, emb)
for upsample_block in self.up_blocks:
res_samples = down_block_res_samples[-len(upsample_block.resnets):]
down_block_res_samples = down_block_res_samples[
: -len(upsample_block.resnets)
]
sample = upsample_block(sample, res_samples, emb)
sample = self.conv_norm_out(sample)
sample = self.conv_act(sample)
sample = self.conv_out(sample)
return sample
def checkerboard(shape):
return np.indices(shape).sum(axis=0) % 2
def fill_checkerboard_bg(y: torch.Tensor) -> torch.Tensor:
alpha = y[..., :1]
fg = y[..., 1:]
B, H, W, C = fg.shape
cb = checkerboard(shape=(H // 64, W // 64))
cb = cv2.resize(cb, (W, H), interpolation=cv2.INTER_NEAREST)
cb = (0.5 + (cb - 0.5) * 0.1)[None, ..., None]
cb = torch.from_numpy(cb).to(fg)
vis = fg * alpha + cb * (1 - alpha)
return vis
class TransparentVAEDecoder:
def __init__(self, sd, device, dtype):
self.load_device = device
self.dtype = dtype
model = UNet1024(in_channels=3, out_channels=4)
model.load_state_dict(sd, strict=True)
model.to(self.load_device, dtype=self.dtype)
model.eval()
self.model = model
@torch.no_grad()
def estimate_single_pass(self, pixel, latent):
y = self.model(pixel, latent)
return y
@torch.no_grad()
def estimate_augmented(self, pixel, latent):
args = [
[False, 0],
[False, 1],
[False, 2],
[False, 3],
[True, 0],
[True, 1],
[True, 2],
[True, 3],
]
result = []
for flip, rok in tqdm(args):
feed_pixel = pixel.clone()
feed_latent = latent.clone()
if flip:
feed_pixel = torch.flip(feed_pixel, dims=(3,))
feed_latent = torch.flip(feed_latent, dims=(3,))
feed_pixel = torch.rot90(feed_pixel, k=rok, dims=(2, 3))
feed_latent = torch.rot90(feed_latent, k=rok, dims=(2, 3))
eps = self.estimate_single_pass(feed_pixel, feed_latent).clip(0, 1)
eps = torch.rot90(eps, k=-rok, dims=(2, 3))
if flip:
eps = torch.flip(eps, dims=(3,))
result += [eps]
result = torch.stack(result, dim=0)
median = torch.median(result, dim=0).values
return median
@torch.no_grad()
def decode_pixel(
self, pixel: torch.TensorType, latent: torch.TensorType
) -> torch.TensorType:
# pixel.shape = [B, C=3, H, W]
assert pixel.shape[1] == 3
pixel_device = pixel.device
pixel_dtype = pixel.dtype
pixel = pixel.to(device=self.load_device, dtype=self.dtype)
latent = latent.to(device=self.load_device, dtype=self.dtype)
# y.shape = [B, C=4, H, W]
y = self.estimate_augmented(pixel, latent)
y = y.clip(0, 1)
assert y.shape[1] == 4
# Restore image to original device of input image.
return y.to(pixel_device, dtype=pixel_dtype)
def calculate_weight_adjust_channel(func):
"""Patches ComfyUI's LoRA weight application to accept multi-channel inputs."""
@functools.wraps(func)
def calculate_weight(
patches, weight: torch.Tensor, key: str, intermediate_type=torch.float32
) -> torch.Tensor:
weight = func(patches, weight, key, intermediate_type)
for p in patches:
alpha = p[0]
v = p[1]
# The recursion call should be handled in the main func call.
if isinstance(v, list):
continue
if len(v) == 1:
patch_type = "diff"
elif len(v) == 2:
patch_type = v[0]
v = v[1]
if patch_type == "diff":
w1 = v[0]
if all(
(
alpha != 0.0,
w1.shape != weight.shape,
w1.ndim == weight.ndim == 4,
)
):
new_shape = [max(n, m) for n, m in zip(weight.shape, w1.shape)]
print(
f"Merged with {key} channel changed from {weight.shape} to {new_shape}"
)
new_diff = alpha * comfy.model_management.cast_to_device(
w1, weight.device, weight.dtype
)
new_weight = torch.zeros(size=new_shape).to(weight)
new_weight[
: weight.shape[0],
: weight.shape[1],
: weight.shape[2],
: weight.shape[3],
] = weight
new_weight[
: new_diff.shape[0],
: new_diff.shape[1],
: new_diff.shape[2],
: new_diff.shape[3],
] += new_diff
new_weight = new_weight.contiguous().clone()
weight = new_weight
return weight
return calculate_weight
except ImportError:
ModelMixin = None
ConfigMixin = None
TransparentVAEDecoder = None
calculate_weight_adjust_channel = None
print("\33[33mModule 'diffusers' load failed. If you don't have it installed, do it:\033[0m")
print("\33[33mpip install diffusers\033[0m")
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