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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 | # 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.
import numpy as np
import PIL
import torch
from ...configuration_utils import FrozenDict
from ...models import AutoencoderKLWan
from ...utils import logging
from ...video_processor import VideoProcessor
from ..modular_pipeline import ModularPipelineBlocks, PipelineState
from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class HeliosDecodeStep(ModularPipelineBlocks):
"""Decode all chunk latents with VAE, trim frames, and postprocess into final video output."""
model_name = "helios"
@property
def description(self) -> str:
return (
"Decodes all chunk latents with the VAE, concatenates them, "
"trims to the target frame count, and postprocesses into the final video output."
)
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("vae", AutoencoderKLWan),
ComponentSpec(
"video_processor",
VideoProcessor,
config=FrozenDict({"vae_scale_factor": 8}),
default_creation_method="from_config",
),
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
"latent_chunks", required=True, type_hint=list, description="List of per-chunk denoised latent tensors"
),
InputParam("num_frames", required=True, type_hint=int, description="The target number of output frames"),
InputParam.template("output_type", default="np"),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
"videos",
type_hint=list[list[PIL.Image.Image]] | list[torch.Tensor] | list[np.ndarray],
description="The generated videos, can be a PIL.Image.Image, torch.Tensor or a numpy array",
),
]
@torch.no_grad()
def __call__(self, components, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
vae = components.vae
device = components._execution_device
decode_dtype = vae.dtype
latents_mean = (
torch.tensor(vae.config.latents_mean).view(1, vae.config.z_dim, 1, 1, 1).to(device, decode_dtype)
)
latents_std = 1.0 / torch.tensor(vae.config.latents_std).view(1, vae.config.z_dim, 1, 1, 1).to(
device, decode_dtype
)
history_video = None
for chunk_latents in block_state.latent_chunks:
current_latents = chunk_latents.to(device=device, dtype=decode_dtype) / latents_std + latents_mean
current_video = vae.decode(current_latents, return_dict=False)[0]
if history_video is None:
history_video = current_video
else:
history_video = torch.cat([history_video, current_video], dim=2)
# Trim to proper frame count
generated_frames = history_video.size(2)
generated_frames = (
generated_frames - 1
) // components.vae_scale_factor_temporal * components.vae_scale_factor_temporal + 1
history_video = history_video[:, :, :generated_frames]
block_state.videos = components.video_processor.postprocess_video(
history_video, output_type=block_state.output_type
)
self.set_block_state(state, block_state)
return components, state
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