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Add movq3 VAE

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City
2024-04-06 22:13:32 +02:00
parent 18fb3f5674
commit 059c87ebb5
4 changed files with 638 additions and 1 deletions
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VAE/conf.py
+15
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@@ -126,5 +126,20 @@ vae_conf = {
"num_res_blocks" : 2,
"attn_resolutions" : [],
"video_kernel_size": [3, 1, 1]
},
# Kandinsky-3
"MoVQ3": {
"type" : "MoVQ3",
"embed_scale" : 8,
"embed_dim" : 4,
"double_z" : False,
"z_channels" : 4,
"resolution" : 256,
"in_channels" : 3,
"out_ch" : 3,
"ch" : 256,
"ch_mult" : [1, 2, 2, 4],
"num_res_blocks" : 2,
"attn_resolutions" : [32],
}
}
VAE/loader.py
+3
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@@ -29,6 +29,9 @@ class EXVAE(comfy.sd.VAE):
from .models.consistencydecoder import ConsistencyDecoder
model = ConsistencyDecoder()
sd = {f"model.{k}":v for k,v in sd.items()}
elif model_conf["type"] == "MoVQ3":
from .models.movq3 import MoVQ
model = MoVQ(model_conf)
else:
raise NotImplementedError(f"Unknown VAE type '{model_conf['type']}'")
VAE/models/LICENSE-Kandinsky-3
+201
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@@ -0,0 +1,201 @@
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VAE/models/movq3.py
+418
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@@ -0,0 +1,418 @@
import math
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
def nonlinearity(x):
return x*torch.sigmoid(x)
class SpatialNorm(nn.Module):
def __init__(
self, f_channels, zq_channels=None, norm_layer=nn.GroupNorm, freeze_norm_layer=False, add_conv=False, **norm_layer_params
):
super().__init__()
self.norm_layer = norm_layer(num_channels=f_channels, **norm_layer_params)
if zq_channels is not None:
if freeze_norm_layer:
for p in self.norm_layer.parameters:
p.requires_grad = False
self.add_conv = add_conv
if self.add_conv:
self.conv = nn.Conv2d(zq_channels, zq_channels, kernel_size=3, stride=1, padding=1)
self.conv_y = nn.Conv2d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0)
self.conv_b = nn.Conv2d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0)
def forward(self, f, zq=None):
norm_f = self.norm_layer(f)
if zq is not None:
f_size = f.shape[-2:]
zq = torch.nn.functional.interpolate(zq.float(), size=f_size, mode="nearest").to(zq.dtype)
if self.add_conv:
zq = self.conv(zq)
norm_f = norm_f * self.conv_y(zq) + self.conv_b(zq)
return norm_f
def Normalize(in_channels, zq_ch=None, add_conv=None):
return SpatialNorm(
in_channels, zq_ch, norm_layer=nn.GroupNorm,
freeze_norm_layer=False, add_conv=add_conv, num_groups=32, eps=1e-6, affine=True
)
class Upsample(nn.Module):
def __init__(self, in_channels, with_conv):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
self.conv = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=3,
stride=1,
padding=1)
def forward(self, x):
x = torch.nn.functional.interpolate(x.float(), scale_factor=2.0, mode="nearest").to(x.dtype)
if self.with_conv:
x = self.conv(x)
return x
class Downsample(nn.Module):
def __init__(self, in_channels, with_conv):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
self.conv = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=3,
stride=2,
padding=0)
def forward(self, x):
if self.with_conv:
pad = (0,1,0,1)
x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
x = self.conv(x)
else:
x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
return x
class ResnetBlock(nn.Module):
def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
dropout, temb_channels=512, zq_ch=None, add_conv=False):
super().__init__()
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.use_conv_shortcut = conv_shortcut
self.norm1 = Normalize(in_channels, zq_ch, add_conv=add_conv)
self.conv1 = torch.nn.Conv2d(in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1)
if temb_channels > 0:
self.temb_proj = torch.nn.Linear(temb_channels,
out_channels)
self.norm2 = Normalize(out_channels, zq_ch, add_conv=add_conv)
self.dropout = torch.nn.Dropout(dropout)
self.conv2 = torch.nn.Conv2d(out_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
self.conv_shortcut = torch.nn.Conv2d(in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1)
else:
self.nin_shortcut = torch.nn.Conv2d(in_channels,
out_channels,
kernel_size=1,
stride=1,
padding=0)
def forward(self, x, temb, zq=None):
h = x
h = self.norm1(h, zq)
h = nonlinearity(h)
h = self.conv1(h)
if temb is not None:
h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
h = self.norm2(h, zq)
h = nonlinearity(h)
h = self.dropout(h)
h = self.conv2(h)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
x = self.conv_shortcut(x)
else:
x = self.nin_shortcut(x)
return x+h
class AttnBlock(nn.Module):
def __init__(self, in_channels, zq_ch=None, add_conv=False):
super().__init__()
self.in_channels = in_channels
self.norm = Normalize(in_channels, zq_ch, add_conv=add_conv)
self.q = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.k = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.v = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.proj_out = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
def forward(self, x, zq=None):
h_ = x
h_ = self.norm(h_, zq)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
b,c,h,w = q.shape
q = q.reshape(b,c,h*w)
q = q.permute(0,2,1) # b,hw,c
k = k.reshape(b,c,h*w) # b,c,hw
w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
w_ = w_ * (int(c)**(-0.5))
w_ = torch.nn.functional.softmax(w_, dim=2)
# attend to values
v = v.reshape(b,c,h*w)
w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q)
h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
h_ = h_.reshape(b,c,h,w)
h_ = self.proj_out(h_)
return x+h_
class Encoder(nn.Module):
def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
resolution, z_channels, double_z=True, **ignore_kwargs):
super().__init__()
self.ch = ch
self.temb_ch = 0
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
self.resolution = resolution
self.in_channels = in_channels
# downsampling
self.conv_in = torch.nn.Conv2d(in_channels,
self.ch,
kernel_size=3,
stride=1,
padding=1)
curr_res = resolution
in_ch_mult = (1,)+tuple(ch_mult)
self.down = nn.ModuleList()
for i_level in range(self.num_resolutions):
block = nn.ModuleList()
attn = nn.ModuleList()
block_in = ch*in_ch_mult[i_level]
block_out = ch*ch_mult[i_level]
for i_block in range(self.num_res_blocks):
block.append(ResnetBlock(in_channels=block_in,
out_channels=block_out,
temb_channels=self.temb_ch,
dropout=dropout))
block_in = block_out
if curr_res in attn_resolutions:
attn.append(AttnBlock(block_in))
down = nn.Module()
down.block = block
down.attn = attn
if i_level != self.num_resolutions-1:
down.downsample = Downsample(block_in, resamp_with_conv)
curr_res = curr_res // 2
self.down.append(down)
# middle
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout)
self.mid.attn_1 = AttnBlock(block_in)
self.mid.block_2 = ResnetBlock(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout)
# end
self.norm_out = Normalize(block_in)
self.conv_out = torch.nn.Conv2d(block_in,
2*z_channels if double_z else z_channels,
kernel_size=3,
stride=1,
padding=1)
def forward(self, x):
temb = None
# downsampling
hs = [self.conv_in(x)]
for i_level in range(self.num_resolutions):
for i_block in range(self.num_res_blocks):
h = self.down[i_level].block[i_block](hs[-1], temb)
if len(self.down[i_level].attn) > 0:
h = self.down[i_level].attn[i_block](h)
hs.append(h)
if i_level != self.num_resolutions-1:
hs.append(self.down[i_level].downsample(hs[-1]))
# middle
h = hs[-1]
h = self.mid.block_1(h, temb)
h = self.mid.attn_1(h)
h = self.mid.block_2(h, temb)
# end
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h)
return h
class Decoder(nn.Module):
def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
resolution, z_channels, give_pre_end=False, zq_ch=None, add_conv=False, **ignorekwargs):
super().__init__()
self.ch = ch
self.temb_ch = 0
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
self.resolution = resolution
self.in_channels = in_channels
self.give_pre_end = give_pre_end
# compute in_ch_mult, block_in and curr_res at lowest res
in_ch_mult = (1,)+tuple(ch_mult)
block_in = ch*ch_mult[self.num_resolutions-1]
curr_res = resolution // 2**(self.num_resolutions-1)
self.z_shape = (1,z_channels,curr_res,curr_res)
# z to block_in
self.conv_in = torch.nn.Conv2d(z_channels,
block_in,
kernel_size=3,
stride=1,
padding=1)
# middle
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout,
zq_ch=zq_ch,
add_conv=add_conv)
self.mid.attn_1 = AttnBlock(block_in, zq_ch, add_conv=add_conv)
self.mid.block_2 = ResnetBlock(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout,
zq_ch=zq_ch,
add_conv=add_conv)
# upsampling
self.up = nn.ModuleList()
for i_level in reversed(range(self.num_resolutions)):
block = nn.ModuleList()
attn = nn.ModuleList()
block_out = ch*ch_mult[i_level]
for i_block in range(self.num_res_blocks+1):
block.append(ResnetBlock(in_channels=block_in,
out_channels=block_out,
temb_channels=self.temb_ch,
dropout=dropout,
zq_ch=zq_ch,
add_conv=add_conv))
block_in = block_out
if curr_res in attn_resolutions:
attn.append(AttnBlock(block_in, zq_ch, add_conv=add_conv))
up = nn.Module()
up.block = block
up.attn = attn
if i_level != 0:
up.upsample = Upsample(block_in, resamp_with_conv)
curr_res = curr_res * 2
self.up.insert(0, up) # prepend to get consistent order
# end
self.norm_out = Normalize(block_in, zq_ch, add_conv=add_conv)
self.conv_out = torch.nn.Conv2d(block_in,
out_ch,
kernel_size=3,
stride=1,
padding=1)
def forward(self, z, zq):
#assert z.shape[1:] == self.z_shape[1:]
self.last_z_shape = z.shape
# timestep embedding
temb = None
# z to block_in
h = self.conv_in(z)
# middle
h = self.mid.block_1(h, temb, zq)
h = self.mid.attn_1(h, zq)
h = self.mid.block_2(h, temb, zq)
# upsampling
for i_level in reversed(range(self.num_resolutions)):
for i_block in range(self.num_res_blocks+1):
h = self.up[i_level].block[i_block](h, temb, zq)
if len(self.up[i_level].attn) > 0:
h = self.up[i_level].attn[i_block](h, zq)
if i_level != 0:
h = self.up[i_level].upsample(h)
# end
if self.give_pre_end:
return h
h = self.norm_out(h, zq)
h = nonlinearity(h)
h = self.conv_out(h)
return h
class MoVQ(nn.Module):
def __init__(self, generator_params):
super().__init__()
z_channels = generator_params["z_channels"]
self.encoder = Encoder(**generator_params)
self.quant_conv = torch.nn.Conv2d(z_channels, z_channels, 1)
self.post_quant_conv = torch.nn.Conv2d(z_channels, z_channels, 1)
self.decoder = Decoder(zq_ch=z_channels, **generator_params)
@torch.no_grad()
def encode(self, x):
h = self.encoder(x)
h = self.quant_conv(h)
return h
@torch.no_grad()
def decode(self, quant):
decoder_input = self.post_quant_conv(quant)
decoded = self.decoder(decoder_input, quant)
return decoded