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# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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 Optional, List
from diffusers.modular_pipelines import (
ModularPipelineBlocks,
ComponentSpec,
InputParam,
OutputParam,
ModularPipeline,
PipelineState,
)
from diffusers.guiders import ClassifierFreeGuidance
from transformers import UMT5EncoderModel, AutoTokenizer
from diffusers.image_processor import PipelineImageInput
import torch
from diffusers.modular_pipelines.wan.encoders import WanTextEncoderStep
from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor
from diffusers.video_processor import VideoProcessor
from diffusers.configuration_utils import FrozenDict
class ChronoEditImageEncoderStep(ModularPipelineBlocks):
model_name = "chronoedit"
@property
def expected_components(self) -> List[ComponentSpec]:
return [
ComponentSpec("image_processor", CLIPImageProcessor),
ComponentSpec("image_encoder", CLIPVisionModelWithProjection),
]
@property
def inputs(self) -> List[InputParam]:
return [InputParam("image", type_hint=PipelineImageInput)]
@property
def intermediate_outputs(self) -> List[OutputParam]:
return [
OutputParam(
"image_embeds",
type_hint=torch.Tensor,
description="Image embeddings to use as conditions during the denoising process.",
)
]
@staticmethod
def encode_image(components, image: PipelineImageInput, device: Optional[torch.device] = None):
device = device or components.image_encoder.device
image = components.image_processor(images=image, return_tensors="pt").to(device)
image_embeds = components.image_encoder(**image, output_hidden_states=True)
return image_embeds.hidden_states[-2]
@torch.no_grad()
def __call__(self, components: ModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
block_state.image_embeds = self.encode_image(components, block_state.image, components._execution_device)
self.set_block_state(state, block_state)
return components, state
class ChronoEditProcessImageStep(ModularPipelineBlocks):
model_name = "chronoedit"
@property
def inputs(self) -> List[InputParam]:
return [
InputParam("image", type_hint=PipelineImageInput),
InputParam("image_embeds", type_hint=torch.Tensor, required=False),
InputParam("batch_size", type_hint=int, required=False),
InputParam("height", type_hint=int),
InputParam("width", type_hint=int),
]
@property
def intermediate_outputs(self) -> List[OutputParam]:
return [
OutputParam("processed_image", type_hint=PipelineImageInput),
OutputParam("image_embeds", type_hint=torch.Tensor)
]
@property
def expected_components(self) -> List[ComponentSpec]:
return [
ComponentSpec(
"video_processor",
VideoProcessor,
config=FrozenDict({"vae_scale_factor": 8}),
default_creation_method="from_config",
)
]
@torch.no_grad()
def __call__(self, components: ModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
image = block_state.image
device = components._execution_device
block_state.processed_image = components.video_processor.preprocess(
image, height=block_state.height, width=block_state.width
).to(device, dtype=torch.bfloat16)
if block_state.image_embeds is not None:
image_embeds = block_state.image_embeds
batch_size = block_state.batch_size
block_state.image_embeds = image_embeds.repeat(batch_size, 1, 1).to(torch.bfloat16)
self.set_block_state(state, block_state)
return components, state
# Configure CFG with a guidance scale of 1.
class ChronoEditTextEncoderStep(WanTextEncoderStep):
model_name = "chronoedit"
@property
def expected_components(self) -> List[ComponentSpec]:
return [
ComponentSpec("text_encoder", UMT5EncoderModel),
ComponentSpec("tokenizer", AutoTokenizer),
ComponentSpec(
"guider",
ClassifierFreeGuidance,
config=FrozenDict({"guidance_scale": 1.0}),
default_creation_method="from_config",
),
]
@torch.no_grad()
def __call__(self, components: ModularPipeline, state: PipelineState) -> PipelineState:
# Get inputs and intermediates
block_state = self.get_block_state(state)
self.check_inputs(block_state)
block_state.prepare_unconditional_embeds = components.guider.num_conditions > 1
block_state.device = components._execution_device
block_state.negative_prompt_embeds = None
# Encode input prompt
(
block_state.prompt_embeds,
block_state.negative_prompt_embeds,
) = self.encode_prompt(
components,
block_state.prompt,
block_state.device,
1,
block_state.prepare_unconditional_embeds,
block_state.negative_prompt,
prompt_embeds=None,
negative_prompt_embeds=block_state.negative_prompt_embeds,
)
# Add outputs
self.set_block_state(state, block_state)
return components, state
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