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# Portions adapted from Hugging Face diffusers:
# Copyright 2026 The HuggingFace Team. Licensed under Apache-2.0.
"""Monkey-patch stock diffusers so pruned LTX-2.3 decoder weights load correctly.
Stock diffusers builds the decoder graph from ``decoder_block_out_channels`` using
the *nominal* width of each up block (divided by ``upsample_factor``). It assumes the
tensor leaving block N always matches that nominal width.
PrunaVAED keeps wider skip connections between blocks and only prunes inside each
block. Example on ``up_blocks.2``:
- tensor arriving from ``up_blocks.1``: **384** ch (after 512→384 projection)
- ResNets inside ``up_blocks.2``: **128** ch (50% pruned vs LTX-2.3 decoder 256)
- ``conv_in`` projection inside the block: **384 → 256** (feeds the upsampler)
Stock diffusers wires ``up_blocks.2`` as 192 → 128 and never creates the 384→256
``conv_in``, so ``from_pretrained`` fails with shape mismatches on PrunaVAED weights.
Fix (two ``__init__`` replacements; forward/decode unchanged)
------------------------------------------------------------
1. ``LTX2VideoDecoder3d``: track ``current_channels`` — the actual tensor width
between blocks — instead of recomputing from the previous block's nominal width.
2. ``LTX2VideoUpBlock3d``: insert ``conv_in`` when ``in_channels != out_channels *
upscale_factor`` (pre-upsampler width), not when ``in_channels != out_channels``.
The LTX-2.3 decoder is unaffected (no extra projections). Safe to call before any
``AutoencoderKLLTX2Video.from_pretrained``.
"""
from __future__ import annotations
import torch
import torch.nn as nn
from diffusers.models.autoencoders import autoencoder_kl_ltx2 as ltx2
def patch_pruna_ltx2_decoder() -> None:
if getattr(ltx2, "_PRUNA_LTX2_DECODER_PATCH", False):
return
Resnet = ltx2.LTX2VideoResnetBlock3d
Upsampler = ltx2.LTX2VideoUpsampler3d
CausalConv = ltx2.LTX2VideoCausalConv3d
MidBlock = ltx2.LTX2VideoMidBlock3d
UpBlock = ltx2.LTX2VideoUpBlock3d
TimeEmb = ltx2.PixArtAlphaCombinedTimestepSizeEmbeddings
RMSNorm = ltx2.PerChannelRMSNorm
def up_init(
self,
in_channels,
out_channels=None,
num_layers=1,
dropout=0.0,
resnet_eps=1e-6,
resnet_act_fn="swish",
spatio_temporal_scale=True,
upsample_type="spatiotemporal",
inject_noise=False,
timestep_conditioning=False,
upsample_residual=False,
upscale_factor=1,
spatial_padding_mode="zeros",
):
nn.Module.__init__(self)
out_channels = out_channels or in_channels
# Width right before the upsampler (ResNet width × upscale_factor).
# conv_in projects the incoming skip (e.g. 384) down to this width (e.g. 256).
pre = out_channels * upscale_factor
self.time_embedder = TimeEmb(in_channels * 4, 0) if timestep_conditioning else None
self.conv_in = None
if in_channels != pre: # stock diffusers compares in_channels != out_channels
self.conv_in = Resnet(
in_channels=in_channels,
out_channels=pre,
dropout=dropout,
eps=resnet_eps,
non_linearity=resnet_act_fn,
inject_noise=inject_noise,
timestep_conditioning=timestep_conditioning,
spatial_padding_mode=spatial_padding_mode,
)
self.upsamplers = None
if spatio_temporal_scale:
stride = {
"spatial": (1, 2, 2),
"temporal": (2, 1, 1),
"spatiotemporal": (2, 2, 2),
}[upsample_type]
self.upsamplers = nn.ModuleList(
[
Upsampler(
in_channels=pre,
stride=stride,
residual=upsample_residual,
upscale_factor=upscale_factor,
spatial_padding_mode=spatial_padding_mode,
),
]
)
self.resnets = nn.ModuleList(
[
Resnet(
in_channels=out_channels,
out_channels=out_channels,
dropout=dropout,
eps=resnet_eps,
non_linearity=resnet_act_fn,
inject_noise=inject_noise,
timestep_conditioning=timestep_conditioning,
spatial_padding_mode=spatial_padding_mode,
)
for _ in range(num_layers)
]
)
self.gradient_checkpointing = False
def decoder_init(
self,
in_channels=128,
out_channels=3,
block_out_channels=(256, 512, 1024),
spatio_temporal_scaling=(True, True, True),
layers_per_block=(5, 5, 5, 5),
upsample_type=("spatiotemporal", "spatiotemporal", "spatiotemporal"),
patch_size=4,
patch_size_t=1,
resnet_norm_eps=1e-6,
is_causal=False,
inject_noise=(False, False, False),
timestep_conditioning=False,
upsample_residual=(True, True, True),
upsample_factor=(2, 2, 2),
spatial_padding_mode="reflect",
):
nn.Module.__init__(self)
n = len(layers_per_block)
if isinstance(spatio_temporal_scaling, bool):
spatio_temporal_scaling = (spatio_temporal_scaling,) * (n - 1)
if isinstance(inject_noise, bool):
inject_noise = (inject_noise,) * n
if isinstance(upsample_residual, bool):
upsample_residual = (upsample_residual,) * (n - 1)
self.patch_size, self.patch_size_t = patch_size, patch_size_t
self.out_channels = out_channels * patch_size**2
self.is_causal = is_causal
ch = tuple(reversed(block_out_channels))
spatio_temporal_scaling = tuple(reversed(spatio_temporal_scaling))
layers_per_block = tuple(reversed(layers_per_block))
inject_noise = tuple(reversed(inject_noise))
upsample_residual = tuple(reversed(upsample_residual))
upsample_factor = tuple(reversed(upsample_factor))
upsample_type = tuple(upsample_type)
if len(upsample_type) == len(ch) - 1:
upsample_type = tuple(reversed(upsample_type))
width = ch[0]
self.conv_in = CausalConv(
in_channels=in_channels,
out_channels=width,
kernel_size=3,
stride=1,
spatial_padding_mode=spatial_padding_mode,
)
self.mid_block = MidBlock(
in_channels=width,
num_layers=layers_per_block[0],
resnet_eps=resnet_norm_eps,
inject_noise=inject_noise[0],
timestep_conditioning=timestep_conditioning,
spatial_padding_mode=spatial_padding_mode,
)
# Stock: input_channel = previous_nominal // factor (loses pruned skip widths).
# Patched: pass the real tensor width from the previous block as in_channels.
self.up_blocks = nn.ModuleList()
current = width
for i in range(len(ch)):
resnet_w = ch[i] // upsample_factor[i]
self.up_blocks.append(
UpBlock(
in_channels=current,
out_channels=resnet_w,
num_layers=layers_per_block[i + 1],
resnet_eps=resnet_norm_eps,
spatio_temporal_scale=spatio_temporal_scaling[i],
upsample_type=upsample_type[i],
inject_noise=inject_noise[i + 1],
timestep_conditioning=timestep_conditioning,
upsample_residual=upsample_residual[i],
upscale_factor=upsample_factor[i],
spatial_padding_mode=spatial_padding_mode,
)
)
current = resnet_w
self.norm_out = RMSNorm()
self.conv_act = nn.SiLU()
self.conv_out = CausalConv(
in_channels=current,
out_channels=self.out_channels,
kernel_size=3,
stride=1,
spatial_padding_mode=spatial_padding_mode,
)
self.time_embedder = self.scale_shift_table = self.timestep_scale_multiplier = None
if timestep_conditioning:
self.timestep_scale_multiplier = nn.Parameter(torch.tensor(1000.0))
self.time_embedder = TimeEmb(width * 2, 0)
self.scale_shift_table = nn.Parameter(torch.randn(2, width) / width**0.5)
self.gradient_checkpointing = False
UpBlock.__init__ = up_init
ltx2.LTX2VideoDecoder3d.__init__ = decoder_init
ltx2._PRUNA_LTX2_DECODER_PATCH = True
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