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cd458ae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 | # Copyright 2026 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any
import torch
from ...configuration_utils import FrozenDict
from ...models import AutoencoderKLLTXVideo
from ...utils import logging
from ...utils.torch_utils import randn_tensor
from ...video_processor import VideoProcessor
from ..modular_pipeline import ModularPipelineBlocks, PipelineState
from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
from .modular_pipeline import LTXVideoPachifier
logger = logging.get_logger(__name__)
def _denormalize_latents(
latents: torch.Tensor, latents_mean: torch.Tensor, latents_std: torch.Tensor, scaling_factor: float = 1.0
) -> torch.Tensor:
# Denormalize latents across the channel dimension [B, C, F, H, W]
latents_mean = latents_mean.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype)
latents_std = latents_std.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype)
latents = latents * latents_std / scaling_factor + latents_mean
return latents
class LTXVaeDecoderStep(ModularPipelineBlocks):
model_name = "ltx"
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("vae", AutoencoderKLLTXVideo),
ComponentSpec(
"video_processor",
VideoProcessor,
config=FrozenDict({"vae_scale_factor": 32}),
default_creation_method="from_config",
),
ComponentSpec(
"pachifier",
LTXVideoPachifier,
config=FrozenDict({"patch_size": 1, "patch_size_t": 1}),
default_creation_method="from_config",
),
]
@property
def description(self) -> str:
return "Step that decodes the denoised latents into videos"
@property
def inputs(self) -> list[tuple[str, Any]]:
return [
InputParam.template("latents", required=True),
InputParam.template("output_type", default="np"),
InputParam.template("height", default=512),
InputParam.template("width", default=704),
InputParam("num_frames", type_hint=int, default=161),
InputParam("decode_timestep", default=0.0),
InputParam("decode_noise_scale", default=None),
InputParam.template("generator"),
InputParam.template("batch_size"),
InputParam.template("dtype", required=True),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [OutputParam.template("videos")]
@torch.no_grad()
def __call__(self, components, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
vae = components.vae
latents = block_state.latents
height = block_state.height
width = block_state.width
num_frames = block_state.num_frames
latent_num_frames = (num_frames - 1) // components.vae_temporal_compression_ratio + 1
latent_height = height // components.vae_spatial_compression_ratio
latent_width = width // components.vae_spatial_compression_ratio
latents = components.pachifier.unpack_latents(latents, latent_num_frames, latent_height, latent_width)
latents = _denormalize_latents(latents, vae.latents_mean, vae.latents_std, vae.config.scaling_factor)
latents = latents.to(block_state.dtype)
if not vae.config.timestep_conditioning:
timestep = None
else:
device = latents.device
batch_size = block_state.batch_size
decode_timestep = block_state.decode_timestep
decode_noise_scale = block_state.decode_noise_scale
noise = randn_tensor(latents.shape, generator=block_state.generator, device=device, dtype=latents.dtype)
if not isinstance(decode_timestep, list):
decode_timestep = [decode_timestep] * batch_size
if decode_noise_scale is None:
decode_noise_scale = decode_timestep
elif not isinstance(decode_noise_scale, list):
decode_noise_scale = [decode_noise_scale] * batch_size
timestep = torch.tensor(decode_timestep, device=device, dtype=latents.dtype)
decode_noise_scale = torch.tensor(decode_noise_scale, device=device, dtype=latents.dtype)[
:, None, None, None, None
]
latents = (1 - decode_noise_scale) * latents + decode_noise_scale * noise
latents = latents.to(vae.dtype)
video = vae.decode(latents, timestep, return_dict=False)[0]
block_state.videos = components.video_processor.postprocess_video(video, output_type=block_state.output_type)
self.set_block_state(state, block_state)
return components, state
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