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# Copyright (c) 2025 Wan and Hugging Face Teams. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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 diffusers.configuration_utils import FrozenDict
from diffusers.modular_pipelines import ModularPipelineBlocks, ComponentSpec, InputParam, OutputParam, PipelineState
from typing import List, Union
from diffusers import AutoencoderKLWan
from diffusers.video_processor import VideoProcessor
import torch
import PIL
import numpy as np
class ChronoEditDecodeStep(ModularPipelineBlocks):
model_name = "chronoedit"
@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 description(self) -> str:
return "Step that decodes the denoised latents into images"
@property
def inputs(self) -> List[InputParam]:
return [
InputParam(
"latents",
required=True,
type_hint=torch.Tensor,
description="The denoised latents from the denoising step",
),
InputParam("output_type", default="pil"),
]
@property
def intermediate_outputs(self) -> List[str]:
return [
OutputParam(
"videos",
type_hint=Union[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_dtype = components.vae.dtype
if not block_state.output_type == "latent":
latents = block_state.latents
latents_mean = (
torch.tensor(components.vae.config.latents_mean)
.view(1, components.vae.config.z_dim, 1, 1, 1)
.to(latents.device, latents.dtype)
)
latents_std = 1.0 / torch.tensor(components.vae.config.latents_std).view(
1, components.vae.config.z_dim, 1, 1, 1
).to(latents.device, latents.dtype)
latents = latents / latents_std + latents_mean
latents = latents.to(vae_dtype)
block_state.videos = components.vae.decode(latents, return_dict=False)[0]
else:
block_state.videos = block_state.latents
block_state.videos = components.video_processor.postprocess_video(
block_state.videos, output_type=block_state.output_type
)
self.set_block_state(state, block_state)
return components, state
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