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ComfyUI/comfy/ldm/modules/diffusionmodules/model.py
rattus 0fd1b78736
Reduce LTX2 VAE VRAM consumption (#12028) 
* causal_video_ae: Remove attention ResNet

This attention_head_dim argument does not exist on this constructor so
this is dead code. Remove as generic attention mid VAE conflicts with
temporal roll.

* ltx-vae: consoldate causal/non-causal code paths

* ltx-vae: add cache rolling adder

* ltx-vae: use cached adder for resnet

* ltx-vae: Implement rolling VAE

Implement a temporal rolling VAE for the LTX2 VAE.

Usually when doing temporal rolling VAEs you can just chunk on time relying
on causality and cache behind you as you go. The LTX VAE is however
non-causal.

So go whole hog and implement per layer run ahead and backpressure between
the decoder layers using recursive state beween the layers.

Operations are ammended with temporal_cache_state{} which they can use to
hold any state then need for partial execution. Convolutions cache their
inputs behind the up to N-1 frames, and skip connections need to cache the
mismatch between convolution input and output that happens due to missing
future (non-causal) input.

Each call to run_up() processes a layer accross a range on input that
may or may not be complete. It goes depth first to process as much as
possible to try and digest frames to the final output ASAP. If layers run
out of input due to convolution losses, they simply return without action
effectively applying back-pressure to the earlier layers. As the earlier
layers do more work and caller deeper, the partial states are reconciled
and output continues to digest depth first as much as possible.

Chunking is done using a size quota rather than a fixed frame length and
any layer can initiate chunking, and multiple layers can chunk at different
granulatiries. This remove the old limitation of always having to process
1 latent frame to entirety and having to hold 8 full decoded frames as
the VRAM peak.
2026-01-22 16:54:18 -05:00

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# 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 torch_cat_if_needed(xl, dim):
xl = [x for x in xl if x is not None and x.shape[dim] > 0]
if len(xl) > 1:
return torch.cat(xl, dim)
elif len(xl) == 1:
return xl[0]
else:
return None
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 torch.nn.functional.silu(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 CarriedConv3d(nn.Module):
def __init__(self, n_channels, out_channels, kernel_size, stride=1, dilation=1, padding=0, **kwargs):
super().__init__()
self.conv = ops.Conv3d(n_channels, out_channels, kernel_size, stride=stride, dilation=dilation, **kwargs)
def forward(self, x):
return self.conv(x)
def conv_carry_causal_3d(xl, op, conv_carry_in=None, conv_carry_out=None):
x = xl[0]
xl.clear()
if isinstance(op, CarriedConv3d):
if conv_carry_in is None:
x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2, 0), mode = 'replicate')
else:
carry_len = conv_carry_in[0].shape[2]
x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2 - carry_len, 0), mode = 'replicate')
x = torch.cat([conv_carry_in.pop(0), x], dim=2)
if conv_carry_out is not None:
to_push = x[:, :, -2:, :, :].clone()
conv_carry_out.append(to_push)
out = op(x)
return out
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, conv_carry_in=None, conv_carry_out=None):
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:
results = []
if conv_carry_in is None:
first = x[:, :, :1, :, :]
results.append(interpolate_up(first.squeeze(2), scale_factor=scale_factor[1:]).unsqueeze(2))
x = x[:, :, 1:, :, :]
if x.shape[2] > 0:
results.append(interpolate_up(x, scale_factor))
x = torch_cat_if_needed(results, dim=2)
else:
x = interpolate_up(x, scale_factor)
if self.with_conv:
x = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out)
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, conv_carry_in=None, conv_carry_out=None):
if self.with_conv:
if isinstance(self.conv, CarriedConv3d):
x = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out)
elif x.ndim == 4:
pad = (0, 1, 0, 1)
mode = "constant"
x = torch.nn.functional.pad(x, pad, mode=mode, value=0)
x = self.conv(x)
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=0.0, temb_channels=512, conv_op=ops.Conv2d, norm_op=Normalize):
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 = norm_op(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 = norm_op(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=None, conv_carry_in=None, conv_carry_out=None):
h = x
h = self.norm1(h)
h = [ self.swish(h) ]
h = conv_carry_causal_3d(h, self.conv1, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
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 = conv_carry_causal_3d(h, self.conv2, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
x = conv_carry_causal_3d([x], self.conv_shortcut, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
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]
oom_fallback = False
q, k, v = map(
lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(),
(q, k, v),
)
try:
out = comfy.ops.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")
oom_fallback = True
if oom_fallback:
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, norm_op=Normalize):
super().__init__()
self.in_channels = in_channels
self.norm = norm_op(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
self.carried = False
if conv3d:
if not attn_resolutions:
conv_op = CarriedConv3d
self.carried = True
else:
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)
self.time_compress = 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)
else:
self.time_compress *= 2
down.downsample = Downsample(block_in, resamp_with_conv, stride=stride, conv_op=conv_op)
curr_res = curr_res // 2
self.down.append(down)
if time_compress is not None:
self.time_compress = time_compress
# 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
if self.carried:
xl = [x[:, :, :1, :, :]]
if x.shape[2] > self.time_compress:
tc = self.time_compress
xl += torch.split(x[:, :, 1: 1 + ((x.shape[2] - 1) // tc) * tc, :, :], tc * 2, dim = 2)
x = xl
else:
x = [x]
out = []
conv_carry_in = None
for i, x1 in enumerate(x):
conv_carry_out = []
if i == len(x) - 1:
conv_carry_out = None
# downsampling
x1 = [ x1 ]
h1 = conv_carry_causal_3d(x1, self.conv_in, conv_carry_in, conv_carry_out)
for i_level in range(self.num_resolutions):
for i_block in range(self.num_res_blocks):
h1 = self.down[i_level].block[i_block](h1, temb, conv_carry_in, conv_carry_out)
if len(self.down[i_level].attn) > 0:
assert i == 0 #carried should not happen if attn exists
h1 = self.down[i_level].attn[i_block](h1)
if i_level != self.num_resolutions-1:
h1 = self.down[i_level].downsample(h1, conv_carry_in, conv_carry_out)
out.append(h1)
conv_carry_in = conv_carry_out
h = torch_cat_if_needed(out, dim=2)
del out
# 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 = conv_carry_causal_3d(h, self.conv_out)
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, 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.tanh_out = tanh_out
self.carried = False
if conv3d:
if not attn_resolutions and resnet_op == ResnetBlock:
conv_op = CarriedConv3d
conv_out_op = CarriedConv3d
self.carried = True
else:
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 = conv_carry_causal_3d([z], self.conv_in)
# middle
h = self.mid.block_1(h, temb, **kwargs)
h = self.mid.attn_1(h, **kwargs)
h = self.mid.block_2(h, temb, **kwargs)
if self.carried:
h = torch.split(h, 2, dim=2)
else:
h = [ h ]
out = []
conv_carry_in = None
# upsampling
for i, h1 in enumerate(h):
conv_carry_out = []
if i == len(h) - 1:
conv_carry_out = None
for i_level in reversed(range(self.num_resolutions)):
for i_block in range(self.num_res_blocks+1):
h1 = self.up[i_level].block[i_block](h1, temb, conv_carry_in, conv_carry_out, **kwargs)
if len(self.up[i_level].attn) > 0:
assert i == 0 #carried should not happen if attn exists
h1 = self.up[i_level].attn[i_block](h1, **kwargs)
if i_level != 0:
h1 = self.up[i_level].upsample(h1, conv_carry_in, conv_carry_out)
h1 = self.norm_out(h1)
h1 = [ nonlinearity(h1) ]
h1 = conv_carry_causal_3d(h1, self.conv_out, conv_carry_in, conv_carry_out)
if self.tanh_out:
h1 = torch.tanh(h1)
out.append(h1)
conv_carry_in = conv_carry_out
out = torch_cat_if_needed(out, dim=2)
return out
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