File size: 4,916 Bytes
5b1c701 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 |
# 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
|