725 lines
27 KiB
Python
725 lines
27 KiB
Python
# pytorch_diffusion + derived encoder decoder
|
|
import math
|
|
import torch
|
|
import torch.nn as nn
|
|
import numpy as np
|
|
from einops import rearrange
|
|
from typing import Optional, Any
|
|
|
|
from comfy import model_management
|
|
import comfy.ops
|
|
|
|
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 torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, 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 = comfy.ops.Conv2d(in_channels,
|
|
in_channels,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=1)
|
|
|
|
def forward(self, x):
|
|
try:
|
|
x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
|
|
except: #operation not implemented for bf16
|
|
b, c, h, w = x.shape
|
|
out = torch.empty((b, c, h*2, w*2), 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=2.0, mode="nearest").to(x.dtype)
|
|
del x
|
|
x = out
|
|
|
|
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:
|
|
# no asymmetric padding in torch conv, must do it ourselves
|
|
self.conv = comfy.ops.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):
|
|
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 = comfy.ops.Conv2d(in_channels,
|
|
out_channels,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=1)
|
|
if temb_channels > 0:
|
|
self.temb_proj = comfy.ops.Linear(temb_channels,
|
|
out_channels)
|
|
self.norm2 = Normalize(out_channels)
|
|
self.dropout = torch.nn.Dropout(dropout, inplace=True)
|
|
self.conv2 = comfy.ops.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 = comfy.ops.Conv2d(in_channels,
|
|
out_channels,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=1)
|
|
else:
|
|
self.nin_shortcut = comfy.ops.Conv2d(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)
|
|
|
|
gb = 1024 ** 3
|
|
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
|
|
print("out of memory error, increasing steps and trying again", steps)
|
|
|
|
return r1
|
|
|
|
class AttnBlock(nn.Module):
|
|
def __init__(self, in_channels):
|
|
super().__init__()
|
|
self.in_channels = in_channels
|
|
|
|
self.norm = Normalize(in_channels)
|
|
self.q = comfy.ops.Conv2d(in_channels,
|
|
in_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0)
|
|
self.k = comfy.ops.Conv2d(in_channels,
|
|
in_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0)
|
|
self.v = comfy.ops.Conv2d(in_channels,
|
|
in_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0)
|
|
self.proj_out = comfy.ops.Conv2d(in_channels,
|
|
in_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0)
|
|
|
|
def forward(self, x):
|
|
h_ = x
|
|
h_ = self.norm(h_)
|
|
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
|
|
v = v.reshape(b,c,h*w)
|
|
|
|
r1 = slice_attention(q, k, v)
|
|
h_ = r1.reshape(b,c,h,w)
|
|
del r1
|
|
h_ = self.proj_out(h_)
|
|
|
|
return x+h_
|
|
|
|
class MemoryEfficientAttnBlock(nn.Module):
|
|
"""
|
|
Uses xformers efficient implementation,
|
|
see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
|
|
Note: this is a single-head self-attention operation
|
|
"""
|
|
#
|
|
def __init__(self, in_channels):
|
|
super().__init__()
|
|
self.in_channels = in_channels
|
|
|
|
self.norm = Normalize(in_channels)
|
|
self.q = comfy.ops.Conv2d(in_channels,
|
|
in_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0)
|
|
self.k = comfy.ops.Conv2d(in_channels,
|
|
in_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0)
|
|
self.v = comfy.ops.Conv2d(in_channels,
|
|
in_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0)
|
|
self.proj_out = comfy.ops.Conv2d(in_channels,
|
|
in_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0)
|
|
self.attention_op: Optional[Any] = None
|
|
|
|
def forward(self, x):
|
|
h_ = x
|
|
h_ = self.norm(h_)
|
|
q = self.q(h_)
|
|
k = self.k(h_)
|
|
v = self.v(h_)
|
|
|
|
# compute attention
|
|
B, C, H, W = q.shape
|
|
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, op=self.attention_op)
|
|
out = out.transpose(1, 2).reshape(B, C, H, W)
|
|
except NotImplementedError as e:
|
|
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(B, C, H, W)
|
|
|
|
out = self.proj_out(out)
|
|
return x+out
|
|
|
|
class MemoryEfficientAttnBlockPytorch(nn.Module):
|
|
def __init__(self, in_channels):
|
|
super().__init__()
|
|
self.in_channels = in_channels
|
|
|
|
self.norm = Normalize(in_channels)
|
|
self.q = comfy.ops.Conv2d(in_channels,
|
|
in_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0)
|
|
self.k = comfy.ops.Conv2d(in_channels,
|
|
in_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0)
|
|
self.v = comfy.ops.Conv2d(in_channels,
|
|
in_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0)
|
|
self.proj_out = comfy.ops.Conv2d(in_channels,
|
|
in_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0)
|
|
self.attention_op: Optional[Any] = None
|
|
|
|
def forward(self, x):
|
|
h_ = x
|
|
h_ = self.norm(h_)
|
|
q = self.q(h_)
|
|
k = self.k(h_)
|
|
v = self.v(h_)
|
|
|
|
# compute attention
|
|
B, C, H, W = q.shape
|
|
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(B, C, H, W)
|
|
except model_management.OOM_EXCEPTION as e:
|
|
print("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(B, C, H, W)
|
|
|
|
out = self.proj_out(out)
|
|
return x+out
|
|
|
|
def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
|
|
assert attn_type in ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none"], f'attn_type {attn_type} unknown'
|
|
if model_management.xformers_enabled_vae() and attn_type == "vanilla":
|
|
attn_type = "vanilla-xformers"
|
|
if model_management.pytorch_attention_enabled() and attn_type == "vanilla":
|
|
attn_type = "vanilla-pytorch"
|
|
print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
|
|
if attn_type == "vanilla":
|
|
assert attn_kwargs is None
|
|
return AttnBlock(in_channels)
|
|
elif attn_type == "vanilla-xformers":
|
|
print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...")
|
|
return MemoryEfficientAttnBlock(in_channels)
|
|
elif attn_type == "vanilla-pytorch":
|
|
return MemoryEfficientAttnBlockPytorch(in_channels)
|
|
elif attn_type == "none":
|
|
return nn.Identity(in_channels)
|
|
else:
|
|
raise NotImplementedError()
|
|
|
|
|
|
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([
|
|
comfy.ops.Linear(self.ch,
|
|
self.temb_ch),
|
|
comfy.ops.Linear(self.temb_ch,
|
|
self.temb_ch),
|
|
])
|
|
|
|
# downsampling
|
|
self.conv_in = comfy.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 = comfy.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",
|
|
**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
|
|
|
|
# downsampling
|
|
self.conv_in = comfy.ops.Conv2d(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))
|
|
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)
|
|
|
|
# end
|
|
self.norm_out = Normalize(block_in)
|
|
self.conv_out = comfy.ops.Conv2d(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,
|
|
attn_type="vanilla", **ignorekwargs):
|
|
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
|
|
self.give_pre_end = give_pre_end
|
|
self.tanh_out = tanh_out
|
|
|
|
# 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)
|
|
print("Working with z of shape {} = {} dimensions.".format(
|
|
self.z_shape, np.prod(self.z_shape)))
|
|
|
|
# z to block_in
|
|
self.conv_in = comfy.ops.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)
|
|
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]
|
|
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))
|
|
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 = comfy.ops.Conv2d(block_in,
|
|
out_ch,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=1)
|
|
|
|
def forward(self, z):
|
|
#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)
|
|
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](h, 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
|
|
if self.give_pre_end:
|
|
return h
|
|
|
|
h = self.norm_out(h)
|
|
h = nonlinearity(h)
|
|
h = self.conv_out(h)
|
|
if self.tanh_out:
|
|
h = torch.tanh(h)
|
|
return h
|