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Tiny AutoEncoder for Hunyuan Video (Decoder-only, pruned)
- Encoder removed
- Transplant/widening helpers removed
- Deepening (IdentityConv2d+ReLU) is now built into the decoder structure itself
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from tqdm.auto import tqdm
from collections import namedtuple
from einops import rearrange
import torch.nn.init as init
DecoderResult = namedtuple("DecoderResult", ("frame", "memory"))
TWorkItem = namedtuple("TWorkItem", ("input_tensor", "block_index"))
# ----------------------------
# Utility / building blocks
# ----------------------------
class IdentityConv2d(nn.Conv2d):
"""Same-shape Conv2d initialized to identity (Dirac)."""
def __init__(self, C, kernel_size=3, bias=False):
pad = kernel_size // 2
super().__init__(C, C, kernel_size, padding=pad, bias=bias)
with torch.no_grad():
init.dirac_(self.weight)
if self.bias is not None:
self.bias.zero_()
def conv(n_in, n_out, **kwargs):
return nn.Conv2d(n_in, n_out, 3, padding=1, **kwargs)
class Clamp(nn.Module):
def forward(self, x):
return torch.tanh(x / 3) * 3
class MemBlock(nn.Module):
def __init__(self, n_in, n_out):
super().__init__()
self.conv = nn.Sequential(
conv(n_in * 2, n_out), nn.ReLU(inplace=True),
conv(n_out, n_out), nn.ReLU(inplace=True),
conv(n_out, n_out)
)
self.skip = nn.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()
self.act = nn.ReLU(inplace=True)
def forward(self, x, past):
return self.act(self.conv(torch.cat([x, past], 1)) + self.skip(x))
class TPool(nn.Module):
def __init__(self, n_f, stride):
super().__init__()
self.stride = stride
self.conv = nn.Conv2d(n_f*stride, n_f, 1, bias=False)
def forward(self, x):
_NT, C, H, W = x.shape
return self.conv(x.reshape(-1, self.stride * C, H, W))
class TGrow(nn.Module):
def __init__(self, n_f, stride):
super().__init__()
self.stride = stride
self.conv = nn.Conv2d(n_f, n_f*stride, 1, bias=False)
def forward(self, x):
_NT, C, H, W = x.shape
x = self.conv(x)
return x.reshape(-1, C, H, W)
class PixelShuffle3d(nn.Module):
def __init__(self, ff, hh, ww):
super().__init__()
self.ff = ff
self.hh = hh
self.ww = ww
def forward(self, x):
# x: (B, C, F, H, W)
B, C, F, H, W = x.shape
if F % self.ff != 0:
first_frame = x[:, :, 0:1, :, :].repeat(1, 1, self.ff - F % self.ff, 1, 1)
x = torch.cat([first_frame, x], dim=2)
return rearrange(
x,
'b c (f ff) (h hh) (w ww) -> b (c ff hh ww) f h w',
ff=self.ff, hh=self.hh, ww=self.ww
).transpose(1, 2)
# ----------------------------
# Generic NTCHW graph executor (kept; used by decoder)
# ----------------------------
def apply_model_with_memblocks(model, x, parallel, show_progress_bar, mem=None):
"""
Apply a sequential model with memblocks to the given input.
Args:
- model: nn.Sequential of blocks to apply
- x: input data, of dimensions NTCHW
- parallel: if True, parallelize over timesteps (fast but uses O(T) memory)
if False, each timestep will be processed sequentially (slow but uses O(1) memory)
- show_progress_bar: if True, enables tqdm progressbar display
Returns NTCHW tensor of output data.
"""
assert x.ndim == 5, f"TAEHV operates on NTCHW tensors, but got {x.ndim}-dim tensor"
N, T, C, H, W = x.shape
if parallel:
x = x.reshape(N*T, C, H, W)
for b in tqdm(model, disable=not show_progress_bar):
if isinstance(b, MemBlock):
NT, C, H, W = x.shape
T = NT // N
_x = x.reshape(N, T, C, H, W)
mem = F.pad(_x, (0,0,0,0,0,0,1,0), value=0)[:,:T].reshape(x.shape)
x = b(x, mem)
else:
x = b(x)
NT, C, H, W = x.shape
T = NT // N
x = x.view(N, T, C, H, W)
else:
out = []
work_queue = [TWorkItem(xt, 0) for t, xt in enumerate(x.reshape(N, T * C, H, W).chunk(T, dim=1))]
progress_bar = tqdm(range(T), disable=not show_progress_bar)
while work_queue:
xt, i = work_queue.pop(0)
if i == 0:
progress_bar.update(1)
if i == len(model):
out.append(xt)
else:
b = model[i]
if isinstance(b, MemBlock):
if mem[i] is None:
xt_new = b(xt, xt * 0)
mem[i] = xt
else:
xt_new = b(xt, mem[i])
mem[i].copy_(xt)
work_queue.insert(0, TWorkItem(xt_new, i+1))
elif isinstance(b, TPool):
if mem[i] is None:
mem[i] = []
mem[i].append(xt)
if len(mem[i]) > b.stride:
raise ValueError("TPool internal state invalid.")
elif len(mem[i]) == b.stride:
N_, C_, H_, W_ = xt.shape
xt = b(torch.cat(mem[i], 1).view(N_*b.stride, C_, H_, W_))
mem[i] = []
work_queue.insert(0, TWorkItem(xt, i+1))
elif isinstance(b, TGrow):
xt = b(xt)
NT, C_, H_, W_ = xt.shape
for xt_next in reversed(xt.view(N, b.stride*C_, H_, W_).chunk(b.stride, 1)):
work_queue.insert(0, TWorkItem(xt_next, i+1))
else:
xt = b(xt)
work_queue.insert(0, TWorkItem(xt, i+1))
progress_bar.close()
x = torch.stack(out, 1)
return x, mem
# ----------------------------
# Decoder-only TAEHV
# ----------------------------
class TAEHV(nn.Module):
image_channels = 3
def __init__(
self,
decoder_time_upscale=(True, True),
decoder_space_upscale=(True, True, True),
channels = [256, 128, 64, 64],
latent_channels = 16,
dtype=torch.float32
):
"""Initialize TAEHV (decoder-only) with built-in deepening after every ReLU.
Deepening config: how_many_each=1, k=3 (fixed as requested).
"""
super().__init__()
self.dtype = dtype
self.latent_channels = latent_channels
n_f = channels
self.frames_to_trim = 2**sum(decoder_time_upscale) - 1
# Build the decoder "skeleton"
base_decoder = nn.Sequential(
Clamp(), conv(self.latent_channels, n_f[0]), nn.ReLU(inplace=True),
MemBlock(n_f[0], n_f[0]), MemBlock(n_f[0], n_f[0]), MemBlock(n_f[0], n_f[0]),
nn.Upsample(scale_factor=2 if decoder_space_upscale[0] else 1),
TGrow(n_f[0], 1),
conv(n_f[0], n_f[1], bias=False),
MemBlock(n_f[1], n_f[1]), MemBlock(n_f[1], n_f[1]), MemBlock(n_f[1], n_f[1]),
nn.Upsample(scale_factor=2 if decoder_space_upscale[1] else 1),
TGrow(n_f[1], 2 if decoder_time_upscale[0] else 1),
conv(n_f[1], n_f[2], bias=False),
MemBlock(n_f[2], n_f[2]), MemBlock(n_f[2], n_f[2]), MemBlock(n_f[2], n_f[2]),
nn.Upsample(scale_factor=2 if decoder_space_upscale[2] else 1),
TGrow(n_f[2], 2 if decoder_time_upscale[1] else 1),
conv(n_f[2], n_f[3], bias=False),
nn.ReLU(inplace=True), conv(n_f[3], TAEHV.image_channels),
)
# Inline deepening: insert (IdentityConv2d(k=3) + ReLU) after every ReLU
self.decoder = self._apply_identity_deepen(base_decoder, how_many_each=1, k=3)
self.pixel_shuffle = PixelShuffle3d(4, 8, 8)
# Initialize decoder mem state
self.clean_mem()
@staticmethod
def _apply_identity_deepen(decoder: nn.Sequential, how_many_each=1, k=3) -> nn.Sequential:
"""Return a new Sequential where every nn.ReLU is followed by how_many_each*(IdentityConv2d(k)+ReLU)."""
new_layers = []
for b in decoder:
new_layers.append(b)
if isinstance(b, nn.ReLU):
# Deduce channel count from preceding layer
C = None
if len(new_layers) >= 2 and isinstance(new_layers[-2], nn.Conv2d):
C = new_layers[-2].out_channels
elif len(new_layers) >= 2 and isinstance(new_layers[-2], MemBlock):
C = new_layers[-2].conv[-1].out_channels
if C is not None:
for _ in range(how_many_each):
new_layers.append(IdentityConv2d(C, kernel_size=k, bias=False))
new_layers.append(nn.ReLU(inplace=True))
return nn.Sequential(*new_layers)
def decode_video(self, x, parallel=False, show_progress_bar=False, cond=None):
"""Decode a sequence of frames from latents.
x: NTCHW latent tensor; returns NTCHW RGB in ~[0, 1].
"""
trim_flag = self.mem[-8] is None # keeps original relative check
if cond is not None:
shuffled = self.pixel_shuffle(cond.to(x))
x = torch.cat([shuffled[:, :x.shape[1]], x], dim=2)
x, self.mem = apply_model_with_memblocks(self.decoder, x, parallel, show_progress_bar, mem=self.mem)
self.clean_mem()
if trim_flag:
return x[:, self.frames_to_trim:]
return x
def clean_mem(self):
self.mem = [None] * len(self.decoder)
def build_tcdecoder(new_channels = [512, 256, 128, 128], device="cuda", dtype=torch.bfloat16, new_latent_channels=None):
big = TAEHV(channels=new_channels, latent_channels=new_latent_channels, dtype=dtype).to(device).to(dtype)
return big
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