# pytorch_diffusion + derived encoder decoder
import math
import torch
import torch.nn as nn
import numpy as np
import logging
from comfy import model_management
import comfy.ops
ops = comfy.ops.disable_weight_init
if model_management.xformers_enabled_vae():
import xformers
import xformers.ops
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attention Is All You Need".
"""
assert len(timesteps.shape) == 1
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
emb = emb.to(device=timesteps.device)
emb = timesteps.float()[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if embedding_dim % 2 == 1: # zero pad
emb = torch.nn.functional.pad(emb, (0,1,0,0))
return emb
def nonlinearity(x):
# swish
return x*torch.sigmoid(x)
def Normalize(in_channels, num_groups=32):
return ops.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
class VideoConv3d(nn.Module):
def __init__(self, n_channels, out_channels, kernel_size, stride=1, dilation=1, padding_mode='replicate', padding=1, **kwargs):
super().__init__()
self.padding_mode = padding_mode
if padding != 0:
padding = (padding, padding, padding, padding, kernel_size - 1, 0)
else:
kwargs["padding"] = padding
self.padding = padding
self.conv = ops.Conv3d(n_channels, out_channels, kernel_size, stride=stride, dilation=dilation, **kwargs)
def forward(self, x):
if self.padding != 0:
x = torch.nn.functional.pad(x, self.padding, mode=self.padding_mode)
return self.conv(x)
def interpolate_up(x, scale_factor):
try:
return torch.nn.functional.interpolate(x, scale_factor=scale_factor, mode="nearest")
except: #operation not implemented for bf16
orig_shape = list(x.shape)
out_shape = orig_shape[:2]
for i in range(len(orig_shape) - 2):
out_shape.append(round(orig_shape[i + 2] * scale_factor[i]))
out = torch.empty(out_shape, dtype=x.dtype, layout=x.layout, device=x.device)
split = 8
l = out.shape[1] // split
for i in range(0, out.shape[1], l):
out[:,i:i+l] = torch.nn.functional.interpolate(x[:,i:i+l].to(torch.float32), scale_factor=scale_factor, mode="nearest").to(x.dtype)
return out
class Upsample(nn.Module):
def __init__(self, in_channels, with_conv, conv_op=ops.Conv2d, scale_factor=2.0):
super().__init__()
self.with_conv = with_conv
self.scale_factor = scale_factor
if self.with_conv:
self.conv = conv_op(in_channels,
in_channels,
kernel_size=3,
stride=1,
padding=1)
def forward(self, x):
scale_factor = self.scale_factor
if isinstance(scale_factor, (int, float)):
scale_factor = (scale_factor,) * (x.ndim - 2)
if x.ndim == 5 and scale_factor[0] > 1.0:
t = x.shape[2]
if t > 1:
a, b = x.split((1, t - 1), dim=2)
del x
b = interpolate_up(b, scale_factor)
else:
a = x
a = interpolate_up(a.squeeze(2), scale_factor=scale_factor[1:]).unsqueeze(2)
if t > 1:
x = torch.cat((a, b), dim=2)
else:
x = a
else:
x = interpolate_up(x, scale_factor)
if self.with_conv:
x = self.conv(x)
return x
class Downsample(nn.Module):
def __init__(self, in_channels, with_conv, stride=2, conv_op=ops.Conv2d):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
# no asymmetric padding in torch conv, must do it ourselves
self.conv = conv_op(in_channels,
in_channels,
kernel_size=3,
stride=stride,
padding=0)
def forward(self, x):
if self.with_conv:
if x.ndim == 4:
pad = (0, 1, 0, 1)
mode = "constant"
x = torch.nn.functional.pad(x, pad, mode=mode, value=0)
elif x.ndim == 5:
pad = (1, 1, 1, 1, 2, 0)
mode = "replicate"
x = torch.nn.functional.pad(x, pad, mode=mode)
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, conv_op=ops.Conv2d):
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.swish = torch.nn.SiLU(inplace=True)
self.norm1 = Normalize(in_channels)
self.conv1 = conv_op(in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1)
if temb_channels > 0:
self.temb_proj = ops.Linear(temb_channels,
out_channels)
self.norm2 = Normalize(out_channels)
self.dropout = torch.nn.Dropout(dropout, inplace=True)
self.conv2 = conv_op(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 = conv_op(in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1)
else:
self.nin_shortcut = conv_op(in_channels,
out_channels,
kernel_size=1,
stride=1,
padding=0)
def forward(self, x, temb):
h = x
h = self.norm1(h)
h = self.swish(h)
h = self.conv1(h)
if temb is not None:
h = h + self.temb_proj(self.swish(temb))[:,:,None,None]
h = self.norm2(h)
h = self.swish(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
def slice_attention(q, k, v):
r1 = torch.zeros_like(k, device=q.device)
scale = (int(q.shape[-1])**(-0.5))
mem_free_total = model_management.get_free_memory(q.device)
tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
modifier = 3 if q.element_size() == 2 else 2.5
mem_required = tensor_size * modifier
steps = 1
if mem_required > mem_free_total:
steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
while True:
try:
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
for i in range(0, q.shape[1], slice_size):
end = i + slice_size
s1 = torch.bmm(q[:, i:end], k) * scale
s2 = torch.nn.functional.softmax(s1, dim=2).permute(0,2,1)
del s1
r1[:, :, i:end] = torch.bmm(v, s2)
del s2
break
except model_management.OOM_EXCEPTION as e:
model_management.soft_empty_cache(True)
steps *= 2
if steps > 128:
raise e
logging.warning("out of memory error, increasing steps and trying again {}".format(steps))
return r1
def normal_attention(q, k, v):
# compute attention
orig_shape = q.shape
b = orig_shape[0]
c = orig_shape[1]
q = q.reshape(b, c, -1)
q = q.permute(0, 2, 1) # b,hw,c
k = k.reshape(b, c, -1) # b,c,hw
v = v.reshape(b, c, -1)
r1 = slice_attention(q, k, v)
h_ = r1.reshape(orig_shape)
del r1
return h_
def xformers_attention(q, k, v):
# compute attention
orig_shape = q.shape
B = orig_shape[0]
C = orig_shape[1]
q, k, v = map(
lambda t: t.view(B, C, -1).transpose(1, 2).contiguous(),
(q, k, v),
)
try:
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
out = out.transpose(1, 2).reshape(orig_shape)
except NotImplementedError:
out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(orig_shape)
return out
def pytorch_attention(q, k, v):
# compute attention
orig_shape = q.shape
B = orig_shape[0]
C = orig_shape[1]
q, k, v = map(
lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(),
(q, k, v),
)
try:
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
out = out.transpose(2, 3).reshape(orig_shape)
except model_management.OOM_EXCEPTION:
logging.warning("scaled_dot_product_attention OOMed: switched to slice attention")
out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(orig_shape)
return out
def vae_attention():
if model_management.xformers_enabled_vae():
logging.info("Using xformers attention in VAE")
return xformers_attention
elif model_management.pytorch_attention_enabled_vae():
logging.info("Using pytorch attention in VAE")
return pytorch_attention
else:
logging.info("Using split attention in VAE")
return normal_attention
class AttnBlock(nn.Module):
def __init__(self, in_channels, conv_op=ops.Conv2d):
super().__init__()
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = conv_op(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.k = conv_op(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.v = conv_op(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.proj_out = conv_op(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.optimized_attention = vae_attention()
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
h_ = self.optimized_attention(q, k, v)
h_ = self.proj_out(h_)
return x+h_
def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None, conv_op=ops.Conv2d):
return AttnBlock(in_channels, conv_op=conv_op)
class Model(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, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
super().__init__()
if use_linear_attn: attn_type = "linear"
self.ch = ch
self.temb_ch = self.ch*4
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
self.resolution = resolution
self.in_channels = in_channels
self.use_timestep = use_timestep
if self.use_timestep:
# timestep embedding
self.temb = nn.Module()
self.temb.dense = nn.ModuleList([
ops.Linear(self.ch,
self.temb_ch),
ops.Linear(self.temb_ch,
self.temb_ch),
])
# downsampling
self.conv_in = ops.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(make_attn(block_in, attn_type=attn_type))
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 = make_attn(block_in, attn_type=attn_type)
self.mid.block_2 = ResnetBlock(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout)
# 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]
skip_in = ch*ch_mult[i_level]
for i_block in range(self.num_res_blocks+1):
if i_block == self.num_res_blocks:
skip_in = ch*in_ch_mult[i_level]
block.append(ResnetBlock(in_channels=block_in+skip_in,
out_channels=block_out,
temb_channels=self.temb_ch,
dropout=dropout))
block_in = block_out
if curr_res in attn_resolutions:
attn.append(make_attn(block_in, attn_type=attn_type))
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)
self.conv_out = ops.Conv2d(block_in,
out_ch,
kernel_size=3,
stride=1,
padding=1)
def forward(self, x, t=None, context=None):
#assert x.shape[2] == x.shape[3] == self.resolution
if context is not None:
# assume aligned context, cat along channel axis
x = torch.cat((x, context), dim=1)
if self.use_timestep:
# timestep embedding
assert t is not None
temb = get_timestep_embedding(t, self.ch)
temb = self.temb.dense[0](temb)
temb = nonlinearity(temb)
temb = self.temb.dense[1](temb)
else:
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)
# 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](
torch.cat([h, hs.pop()], dim=1), temb)
if len(self.up[i_level].attn) > 0:
h = self.up[i_level].attn[i_block](h)
if i_level != 0:
h = self.up[i_level].upsample(h)
# end
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h)
return h
def get_last_layer(self):
return self.conv_out.weight
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, use_linear_attn=False, attn_type="vanilla",
conv3d=False, time_compress=None,
**ignore_kwargs):
super().__init__()
if use_linear_attn: attn_type = "linear"
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
if conv3d:
conv_op = VideoConv3d
mid_attn_conv_op = ops.Conv3d
else:
conv_op = ops.Conv2d
mid_attn_conv_op = ops.Conv2d
# downsampling
self.conv_in = conv_op(in_channels,
self.ch,
kernel_size=3,
stride=1,
padding=1)
curr_res = resolution
in_ch_mult = (1,)+tuple(ch_mult)
self.in_ch_mult = in_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,
conv_op=conv_op))
block_in = block_out
if curr_res in attn_resolutions:
attn.append(make_attn(block_in, attn_type=attn_type, conv_op=conv_op))
down = nn.Module()
down.block = block
down.attn = attn
if i_level != self.num_resolutions-1:
stride = 2
if time_compress is not None:
if (self.num_resolutions - 1 - i_level) > math.log2(time_compress):
stride = (1, 2, 2)
down.downsample = Downsample(block_in, resamp_with_conv, stride=stride, conv_op=conv_op)
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,
conv_op=conv_op)
self.mid.attn_1 = make_attn(block_in, attn_type=attn_type, conv_op=mid_attn_conv_op)
self.mid.block_2 = ResnetBlock(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout,
conv_op=conv_op)
# end
self.norm_out = Normalize(block_in)
self.conv_out = conv_op(block_in,
2*z_channels if double_z else z_channels,
kernel_size=3,
stride=1,
padding=1)
def forward(self, x):
# timestep embedding
temb = None
# downsampling
h = 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](h, temb)
if len(self.down[i_level].attn) > 0:
h = self.down[i_level].attn[i_block](h)
if i_level != self.num_resolutions-1:
h = self.down[i_level].downsample(h)
# middle
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, tanh_out=False, use_linear_attn=False,
conv_out_op=ops.Conv2d,
resnet_op=ResnetBlock,
attn_op=AttnBlock,
conv3d=False,
time_compress=None,
**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
self.tanh_out = tanh_out
if conv3d:
conv_op = VideoConv3d
conv_out_op = VideoConv3d
mid_attn_conv_op = ops.Conv3d
else:
conv_op = ops.Conv2d
mid_attn_conv_op = ops.Conv2d
# compute block_in and curr_res at lowest res
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)
logging.debug("Working with z of shape {} = {} dimensions.".format(
self.z_shape, np.prod(self.z_shape)))
# z to block_in
self.conv_in = conv_op(z_channels,
block_in,
kernel_size=3,
stride=1,
padding=1)
# middle
self.mid = nn.Module()
self.mid.block_1 = resnet_op(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout,
conv_op=conv_op)
self.mid.attn_1 = attn_op(block_in, conv_op=mid_attn_conv_op)
self.mid.block_2 = resnet_op(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout,
conv_op=conv_op)
# 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(resnet_op(in_channels=block_in,
out_channels=block_out,
temb_channels=self.temb_ch,
dropout=dropout,
conv_op=conv_op))
block_in = block_out
if curr_res in attn_resolutions:
attn.append(attn_op(block_in, conv_op=conv_op))
up = nn.Module()
up.block = block
up.attn = attn
if i_level != 0:
scale_factor = 2.0
if time_compress is not None:
if i_level > math.log2(time_compress):
scale_factor = (1.0, 2.0, 2.0)
up.upsample = Upsample(block_in, resamp_with_conv, conv_op=conv_op, scale_factor=scale_factor)
curr_res = curr_res * 2
self.up.insert(0, up) # prepend to get consistent order
# end
self.norm_out = Normalize(block_in)
self.conv_out = conv_out_op(block_in,
out_ch,
kernel_size=3,
stride=1,
padding=1)
def forward(self, z, **kwargs):
# timestep embedding
temb = None
# z to block_in
h = self.conv_in(z)
# middle
h = self.mid.block_1(h, temb, **kwargs)
h = self.mid.attn_1(h, **kwargs)
h = self.mid.block_2(h, temb, **kwargs)
# 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, **kwargs)
if len(self.up[i_level].attn) > 0:
h = self.up[i_level].attn[i_block](h, **kwargs)
if i_level != 0:
h = self.up[i_level].upsample(h)
# end
if self.give_pre_end:
return h
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h, **kwargs)
if self.tanh_out:
h = torch.tanh(h)
return h