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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.
"""
TODO: need to implement temporal reasoning:
https://huggingface.co/spaces/nvidia/ChronoEdit/blob/main/chronoedit_diffusers/pipeline_chronoedit.py
"""
from diffusers.modular_pipelines import (
ModularPipelineBlocks,
ComponentSpec,
BlockState,
PipelineState,
ModularPipeline,
InputParam,
LoopSequentialPipelineBlocks,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.guiders import ClassifierFreeGuidance
from typing import List
from diffusers import AutoModel, UniPCMultistepScheduler
import torch
from diffusers.modular_pipelines.wan.denoise import WanLoopAfterDenoiser, WanDenoiseLoopWrapper
class ChronoEditLoopBeforeDenoiser(ModularPipelineBlocks):
model_name = "chronoedit"
@property
def inputs(self) -> List[InputParam]:
return [
InputParam(
"latents",
required=True,
type_hint=torch.Tensor,
description="The initial latents to use for the denoising process. Can be generated in prepare_latent step.",
),
InputParam(
"condition",
required=True,
type_hint=torch.Tensor,
description="The conditioning latents to use for the denoising process. Can be generated in prepare_latent step.",
),
]
@torch.no_grad()
def __call__(self, components: ModularPipeline, block_state: BlockState, i: int, t: torch.Tensor):
latent_model_input = torch.cat([block_state.latents, block_state.condition], dim=1)
block_state.latent_model_input = latent_model_input.to(block_state.latents.dtype)
block_state.timestep = t.expand(block_state.latents.shape[0])
return components, block_state
class ChronoEditLoopDenoiser(ModularPipelineBlocks):
model_name = "chronoedit"
@property
def expected_components(self) -> List[ComponentSpec]:
return [
ComponentSpec(
"guider",
ClassifierFreeGuidance,
config=FrozenDict({"guidance_scale": 1.0}),
default_creation_method="from_config",
),
ComponentSpec("transformer", AutoModel),
]
@property
def inputs(self) -> List[InputParam]:
return [
InputParam("attention_kwargs"),
InputParam(
"latents",
required=True,
type_hint=torch.Tensor,
description="The initial latents to use for the denoising process. Can be generated in prepare_latent step.",
),
InputParam(
"condition",
required=True,
type_hint=torch.Tensor,
description="The conditioning latents to use for the denoising process. Can be generated in prepare_latent step.",
),
InputParam(
"image_embeds",
required=True,
type_hint=torch.Tensor,
description="The conditioning image embeddings to use for the denoising process. Can be generated in prepare_latent step.",
),
InputParam(
"num_inference_steps",
required=True,
type_hint=int,
description="The number of inference steps to use for the denoising process. Can be generated in set_timesteps step.",
),
InputParam(
kwargs_type="denoiser_input_fields",
description=(
"All conditional model inputs that need to be prepared with guider. "
"It should contain prompt_embeds/negative_prompt_embeds. "
"Please add `kwargs_type=denoiser_input_fields` to their parameter spec (`OutputParam`) when they are created and added to the pipeline state"
),
),
]
@torch.no_grad()
def __call__(self, components: ModularPipeline, block_state: BlockState, i: int, t: torch.Tensor) -> PipelineState:
# Map the keys we'll see on each `guider_state_batch` (e.g. guider_state_batch.prompt_embeds)
# to the corresponding (cond, uncond) fields on block_state. (e.g. block_state.prompt_embeds, block_state.negative_prompt_embeds)
guider_inputs = {
"prompt_embeds": (
getattr(block_state, "prompt_embeds", None),
getattr(block_state, "negative_prompt_embeds", None),
),
}
components.guider.set_state(step=i, num_inference_steps=block_state.num_inference_steps, timestep=t)
guider_state = components.guider.prepare_inputs(guider_inputs)
# run the denoiser for each guidance batch
for guider_state_batch in guider_state:
components.guider.prepare_models(components.transformer)
cond_kwargs = {input_name: getattr(guider_state_batch, input_name) for input_name in guider_inputs.keys()}
prompt_embeds = cond_kwargs.pop("prompt_embeds")
# Predict the noise residual
# store the noise_pred in guider_state_batch so that we can apply guidance across all batches
guider_state_batch.noise_pred = components.transformer(
hidden_states=block_state.latent_model_input,
timestep=block_state.timestep,
encoder_hidden_states=prompt_embeds,
encoder_hidden_states_image=block_state.image_embeds,
attention_kwargs=block_state.attention_kwargs,
return_dict=False,
)[0]
components.guider.cleanup_models(components.transformer)
# Perform guidance
block_state.noise_pred = components.guider(guider_state)[0]
return components, block_state
class ChronoEditDenoiseLoopWrapper(LoopSequentialPipelineBlocks):
model_name = "chronoedit"
@property
def loop_expected_components(self) -> List[ComponentSpec]:
return [
ComponentSpec(
"guider",
ClassifierFreeGuidance,
config=FrozenDict({"guidance_scale": 1.0}),
default_creation_method="from_config",
),
ComponentSpec("scheduler", UniPCMultistepScheduler),
ComponentSpec("transformer", AutoModel),
]
@property
def loop_inputs(self) -> List[InputParam]:
return [
InputParam(
"timesteps",
required=True,
type_hint=torch.Tensor,
description="The timesteps to use for the denoising process. Can be generated in set_timesteps step.",
),
InputParam(
"num_inference_steps",
required=True,
type_hint=int,
description="The number of inference steps to use for the denoising process. Can be generated in set_timesteps step.",
),
]
@torch.no_grad()
def __call__(self, components: ModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
block_state.num_warmup_steps = max(
len(block_state.timesteps) - block_state.num_inference_steps * components.scheduler.order, 0
)
with self.progress_bar(total=block_state.num_inference_steps) as progress_bar:
for i, t in enumerate(block_state.timesteps):
components, block_state = self.loop_step(components, block_state, i=i, t=t)
if i == len(block_state.timesteps) - 1 or (
(i + 1) > block_state.num_warmup_steps and (i + 1) % components.scheduler.order == 0
):
progress_bar.update()
self.set_block_state(state, block_state)
return components, state
class ChronoEditLoopAfterDenoiser(WanLoopAfterDenoiser):
model_name = "chronoedit"
class ChronoEditDenoiseStep(ChronoEditDenoiseLoopWrapper):
block_classes = [ChronoEditLoopBeforeDenoiser, ChronoEditLoopDenoiser, ChronoEditLoopAfterDenoiser]
block_names = ["before_denoiser", "denoiser", "after_denoiser"]
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