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# 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 AutoPipelineBlocks, SequentialPipelineBlocks
from ..modular_pipeline_utils import InsertableDict
from .before_denoise import (
WanInputStep,
WanPrepareLatentsStep,
WanSetTimestepsStep,
)
from .decoders import WanDecodeStep
from .denoise import WanDenoiseStep
from .encoders import WanTextEncoderStep
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# before_denoise: text2vid
class WanBeforeDenoiseStep(SequentialPipelineBlocks):
block_classes = [
WanInputStep,
WanSetTimestepsStep,
WanPrepareLatentsStep,
]
block_names = ["input", "set_timesteps", "prepare_latents"]
@property
def description(self):
return (
"Before denoise step that prepare the inputs for the denoise step.\n"
+ "This is a sequential pipeline blocks:\n"
+ " - `WanInputStep` is used to adjust the batch size of the model inputs\n"
+ " - `WanSetTimestepsStep` is used to set the timesteps\n"
+ " - `WanPrepareLatentsStep` is used to prepare the latents\n"
)
# before_denoise: all task (text2vid,)
class WanAutoBeforeDenoiseStep(AutoPipelineBlocks):
block_classes = [
WanBeforeDenoiseStep,
]
block_names = ["text2vid"]
block_trigger_inputs = [None]
@property
def description(self):
return (
"Before denoise step that prepare the inputs for the denoise step.\n"
+ "This is an auto pipeline block that works for text2vid.\n"
+ " - `WanBeforeDenoiseStep` (text2vid) is used.\n"
)
# denoise: text2vid
class WanAutoDenoiseStep(AutoPipelineBlocks):
block_classes = [
WanDenoiseStep,
]
block_names = ["denoise"]
block_trigger_inputs = [None]
@property
def description(self) -> str:
return (
"Denoise step that iteratively denoise the latents. "
"This is a auto pipeline block that works for text2vid tasks.."
" - `WanDenoiseStep` (denoise) for text2vid tasks."
)
# decode: all task (text2img, img2img, inpainting)
class WanAutoDecodeStep(AutoPipelineBlocks):
block_classes = [WanDecodeStep]
block_names = ["non-inpaint"]
block_trigger_inputs = [None]
@property
def description(self):
return "Decode step that decode the denoised latents into videos outputs.\n - `WanDecodeStep`"
# text2vid
class WanAutoBlocks(SequentialPipelineBlocks):
block_classes = [
WanTextEncoderStep,
WanAutoBeforeDenoiseStep,
WanAutoDenoiseStep,
WanAutoDecodeStep,
]
block_names = [
"text_encoder",
"before_denoise",
"denoise",
"decoder",
]
@property
def description(self):
return (
"Auto Modular pipeline for text-to-video using Wan.\n"
+ "- for text-to-video generation, all you need to provide is `prompt`"
)
TEXT2VIDEO_BLOCKS = InsertableDict(
[
("text_encoder", WanTextEncoderStep),
("input", WanInputStep),
("set_timesteps", WanSetTimestepsStep),
("prepare_latents", WanPrepareLatentsStep),
("denoise", WanDenoiseStep),
("decode", WanDecodeStep),
]
)
AUTO_BLOCKS = InsertableDict(
[
("text_encoder", WanTextEncoderStep),
("before_denoise", WanAutoBeforeDenoiseStep),
("denoise", WanAutoDenoiseStep),
("decode", WanAutoDecodeStep),
]
)
ALL_BLOCKS = {
"text2video": TEXT2VIDEO_BLOCKS,
"auto": AUTO_BLOCKS,
}
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