File size: 5,330 Bytes
4f4376a | 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 | # 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 ...utils import logging
from ..modular_pipeline import SequentialPipelineBlocks
from ..modular_pipeline_utils import OutputParam
from .before_denoise import (
WanPrepareLatentsStep,
WanSetTimestepsStep,
WanTextInputStep,
)
from .decoders import WanVaeDecoderStep
from .denoise import (
WanDenoiseStep,
)
from .encoders import (
WanTextEncoderStep,
)
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# ====================
# 1. DENOISE
# ====================
# inputs(text) -> set_timesteps -> prepare_latents -> denoise
# auto_docstring
class WanCoreDenoiseStep(SequentialPipelineBlocks):
"""
denoise block that takes encoded conditions and runs the denoising process.
Components:
transformer (`WanTransformer3DModel`) scheduler (`UniPCMultistepScheduler`) guider (`ClassifierFreeGuidance`)
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_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.
num_frames (`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.
Outputs:
latents (`Tensor`):
Denoised latents.
"""
model_name = "wan"
block_classes = [
WanTextInputStep,
WanSetTimestepsStep,
WanPrepareLatentsStep,
WanDenoiseStep,
]
block_names = ["input", "set_timesteps", "prepare_latents", "denoise"]
@property
def description(self):
return "denoise block that takes encoded conditions and runs the denoising process."
@property
def outputs(self):
return [OutputParam.template("latents")]
# ====================
# 2. BLOCKS (Wan2.1 text2video)
# ====================
# auto_docstring
class WanBlocks(SequentialPipelineBlocks):
"""
Modular pipeline blocks for Wan2.1.
Components:
text_encoder (`UMT5EncoderModel`) tokenizer (`AutoTokenizer`) guider (`ClassifierFreeGuidance`) transformer
(`WanTransformer3DModel`) scheduler (`UniPCMultistepScheduler`) vae (`AutoencoderKLWan`) video_processor
(`VideoProcessor`)
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.
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.
height (`int`, *optional*):
TODO: Add description.
width (`int`, *optional*):
TODO: Add description.
num_frames (`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.
output_type (`str`, *optional*, defaults to np):
The output type of the decoded videos
Outputs:
videos (`list`):
The generated videos.
"""
model_name = "wan"
block_classes = [
WanTextEncoderStep,
WanCoreDenoiseStep,
WanVaeDecoderStep,
]
block_names = ["text_encoder", "denoise", "decode"]
@property
def description(self):
return "Modular pipeline blocks for Wan2.1."
@property
def outputs(self):
return [OutputParam.template("videos")]
|