decoders.py

# Copyright 2025 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, List, Tuple, Union

import numpy as np
import PIL
import torch

from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKLWan
from diffusers.utils import logging
from diffusers.video_processor import VideoProcessor
from diffusers.modular_pipelines import ModularPipelineBlocks, PipelineState
from diffusers.modular_pipelines.modular_pipeline_utils import (
    ComponentSpec,
    InputParam,
    OutputParam,
)
import types


logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


class WanRTDecodeStep(ModularPipelineBlocks):
    model_name = "WanRT"
    decoder_cache = []

    @property
    def expected_components(self) -> List[ComponentSpec]:
        return [
            ComponentSpec(
                "vae",
                AutoencoderKLWan,
                repo="Wan-AI/Wan2.1-T2V-14B-Diffusers",
                subfolder="vae",
            ),
            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[Tuple[str, Any]]:
        return [
            InputParam("output_type", default="pil"),
            InputParam(
                "latents",
                required=True,
                type_hint=torch.Tensor,
                description="The denoised latents from the denoising step",
            ),
            InputParam(
                "frame_cache_context",
                description="The denoised latents from the denoising step",
            ),
            InputParam(
                "block_idx",
                description="The denoised latents from the denoising step",
            ),
            InputParam(
                "decoder_cache",
                description="The denoised latents from the denoising step",
            ),
        ]

    @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

        # Disable clearing cache
        if block_state.block_idx == 0:
            components.vae.clear_cache()
            components.vae.clear_cache = lambda: None
            components.vae._feat_map = [None] * 55

        if block_state.block_idx != 0:
            components.vae._feat_map = block_state.decoder_cache

        if not block_state.output_type == "latent":
            latents = block_state.latents.to(components.vae.device)

            # Create tensors directly on target device and dtype to avoid redundant conversions
            latents_mean = torch.tensor(
                components.vae.config.latents_mean,
                device=latents.device,
                dtype=latents.dtype,
            ).view(1, components.vae.config.z_dim, 1, 1, 1)
            latents_std = 1.0 / torch.tensor(
                components.vae.config.latents_std,
                device=latents.device,
                dtype=latents.dtype,
            ).view(1, components.vae.config.z_dim, 1, 1, 1)

            latents = latents / latents_std + latents_mean
            latents = latents.to(vae_dtype)

            videos = components.vae.decode(latents, return_dict=False)[0]

        else:
            block_state.videos = block_state.latents

        block_state.decoder_cache = components.vae._feat_map
        block_state.frame_cache_context.extend(videos.split(1, dim=2))

        videos = components.video_processor.postprocess_video(
            videos, output_type=block_state.output_type
        )
        block_state.videos = videos

        self.set_block_state(state, block_state)

        return components, state