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3db1cbc | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 | # 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 ...utils import logging
from ..modular_pipeline import SequentialPipelineBlocks
from ..modular_pipeline_utils import OutputParam
from .before_denoise import (
WanPrepareLatentsStep,
WanSetTimestepsStep,
WanTextInputStep,
WanVaceAdditionalInputsStep,
)
from .decoders import WanVaceTrimReferenceLatentsStep, WanVaeDecoderStep
from .denoise import (
Wan22VaceDenoiseStep,
)
from .encoders import (
WanTextEncoderStep,
WanVaceEncoderStep,
)
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# ====================
# 1. DENOISE
# ====================
# inputs (text + control_hidden_states) -> additional_inputs -> set_timesteps -> prepare_latents -> denoise -> trim_latents
# auto_docstring
class Wan22VaceCoreDenoiseStep(SequentialPipelineBlocks):
"""
denoise block that takes encoded text and vace conditioning latents and runs the denoising process.
Components:
transformer (`WanTransformer3DModel`) scheduler (`UniPCMultistepScheduler`) guider (`ClassifierFreeGuidance`)
guider_2 (`ClassifierFreeGuidance`) transformer_2 (`WanVACETransformer3DModel`)
Configs:
boundary_ratio (default: 0.875): The boundary ratio to divide the denoising loop into high noise and low
noise stages.
Inputs:
num_videos_per_prompt (`None`, *optional*, defaults to 1):
TODO: Add description.
prompt_embeds (`Tensor`):
Pre-generated text embeddings. Can be generated from text_encoder step.
negative_prompt_embeds (`Tensor`, *optional*):
Pre-generated negative text embeddings. Can be generated from text_encoder step.
num_frames (`int`, *optional*):
TODO: Add description.
num_reference_images (`int`, *optional*, defaults to 0):
Number of reference images prepended on the frame dimension of the conditioning latents. Can be generated
in vace_encoder step.
num_inference_steps (`None`, *optional*, defaults to 50):
TODO: Add description.
timesteps (`None`, *optional*):
TODO: Add description.
sigmas (`None`, *optional*):
TODO: Add description.
height (`int`, *optional*):
TODO: Add description.
width (`int`, *optional*):
TODO: Add description.
latents (`Tensor | NoneType`, *optional*):
TODO: Add description.
generator (`None`, *optional*):
TODO: Add description.
attention_kwargs (`None`, *optional*):
TODO: Add description.
**denoiser_input_fields (`None`, *optional*):
conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
Outputs:
latents (`Tensor`):
Denoised latents.
"""
model_name = "wan-vace"
block_classes = [
WanTextInputStep,
WanVaceAdditionalInputsStep,
WanSetTimestepsStep,
WanPrepareLatentsStep,
Wan22VaceDenoiseStep,
WanVaceTrimReferenceLatentsStep,
]
block_names = [
"input",
"additional_inputs",
"set_timesteps",
"prepare_latents",
"denoise",
"trim_latents",
]
@property
def description(self):
return "denoise block that takes encoded text and vace conditioning latents and runs the denoising process."
@property
def outputs(self):
return [OutputParam.template("latents")]
# ====================
# 2. BLOCKS (Wan2.2 VACE)
# ====================
# auto_docstring
class Wan22VaceBlocks(SequentialPipelineBlocks):
"""
Modular pipeline for controllable video generation using Wan2.2 VACE.
Components:
text_encoder (`UMT5EncoderModel`) tokenizer (`AutoTokenizer`) guider (`ClassifierFreeGuidance`) vae
(`AutoencoderKLWan`) video_processor (`VideoProcessor`) transformer (`WanTransformer3DModel`) scheduler
(`UniPCMultistepScheduler`) guider_2 (`ClassifierFreeGuidance`) transformer_2 (`WanVACETransformer3DModel`)
Configs:
boundary_ratio (default: 0.875): The boundary ratio to divide the denoising loop into high noise and low
noise stages.
Inputs:
prompt (`None`, *optional*):
TODO: Add description.
negative_prompt (`None`, *optional*):
TODO: Add description.
max_sequence_length (`None`, *optional*, defaults to 512):
TODO: Add description.
video (`list`, *optional*):
The control video to condition the generation on. If not provided, an empty video is used.
mask (`list`, *optional*):
The mask that defines which video regions to condition on (black) and which to generate (white). Can only
be passed if `video` is passed as well.
reference_images (`Image | list`, *optional*):
One or more reference images as extra conditioning for the generation.
conditioning_scale (`float | list | Tensor`, *optional*, defaults to 1.0):
The conditioning scale applied in each control layer of the model. If a float, it is applied uniformly to
all layers; a list or tensor must have the same length as the number of control layers.
height (`None`, *optional*):
TODO: Add description.
width (`None`, *optional*):
TODO: Add description.
num_frames (`int`, *optional*, defaults to 81):
TODO: Add description.
generator (`None`, *optional*):
TODO: Add description.
num_videos_per_prompt (`None`, *optional*, defaults to 1):
TODO: Add description.
num_inference_steps (`None`, *optional*, defaults to 50):
TODO: Add description.
timesteps (`None`, *optional*):
TODO: Add description.
sigmas (`None`, *optional*):
TODO: Add description.
latents (`Tensor | NoneType`, *optional*):
TODO: Add description.
attention_kwargs (`None`, *optional*):
TODO: Add description.
**denoiser_input_fields (`None`, *optional*):
conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
output_type (`str`, *optional*, defaults to np):
The output type of the decoded videos
Outputs:
videos (`list`):
The generated videos.
"""
model_name = "wan-vace"
block_classes = [
WanTextEncoderStep,
WanVaceEncoderStep,
Wan22VaceCoreDenoiseStep,
WanVaeDecoderStep,
]
block_names = [
"text_encoder",
"vace_encoder",
"denoise",
"decode",
]
@property
def description(self):
return "Modular pipeline for controllable video generation using Wan2.2 VACE."
@property
def outputs(self):
return [OutputParam.template("videos")]
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