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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 diffusers.modular_pipelines import (
ModularPipelineBlocks,
ComponentSpec,
PipelineState,
ModularPipeline,
OutputParam,
InputParam,
)
from diffusers.modular_pipelines.wan.before_denoise import retrieve_timesteps
from typing import Optional, List, Union, Tuple
from diffusers.image_processor import PipelineImageInput
from diffusers.utils.torch_utils import randn_tensor
import torch
from diffusers import AutoencoderKLWan, UniPCMultistepScheduler
# One needs Wan anyway to run ChronoEdit (`AutoencoderKLWan`).
from diffusers.pipelines.wan.pipeline_wan_i2v import retrieve_latents
class ChronoEditSetTimestepsStep(ModularPipelineBlocks):
model_name = "chronoedit"
@property
def expected_components(self) -> List[ComponentSpec]:
return [
ComponentSpec("scheduler", UniPCMultistepScheduler)
]
@property
def inputs(self) -> List[InputParam]:
return [
InputParam("num_inference_steps", default=50),
InputParam("timesteps"),
InputParam("sigmas")
]
@property
def intermediate_outputs(self) -> List[OutputParam]:
return [
OutputParam("timesteps", type_hint=torch.Tensor, description="The timesteps to use for inference"),
OutputParam(
"num_inference_steps",
type_hint=int,
description="The number of denoising steps to perform at inference time",
),
]
@torch.no_grad()
def __call__(self, components: ModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
block_state.device = components._execution_device
block_state.timesteps, block_state.num_inference_steps = retrieve_timesteps(
components.scheduler,
block_state.num_inference_steps,
block_state.device,
block_state.timesteps,
block_state.sigmas,
)
self.set_block_state(state, block_state)
return components, state
class ChronoEditPrepareLatentStep(ModularPipelineBlocks):
model_name = "chronoedit"
@property
def expected_components(self) -> List[ComponentSpec]:
return [ComponentSpec("vae", AutoencoderKLWan)]
@property
def inputs(self) -> List[InputParam]:
return [
InputParam("processed_image", type_hint=PipelineImageInput),
InputParam("image_embeds", type_hint=torch.Tensor),
InputParam("height", type_hint=int, default=480),
InputParam("width", type_hint=int, default=832),
InputParam("num_frames", type_hint=int, default=81),
InputParam("batch_size"),
InputParam("num_videos_per_prompt", type_hint=int, default=1),
InputParam("latents", type_hint=Optional[torch.Tensor]),
InputParam("generator"),
]
@property
def intermediate_outputs(self) -> List[OutputParam]:
return [
OutputParam(
"latents",
type_hint=torch.Tensor,
description="The initial latents to use for the denoising process.",
),
OutputParam(
"condition",
type_hint=torch.Tensor,
description="Conditioning latents for the denoising process.",
),
]
@staticmethod
def check_inputs(height, width):
if height % 16 != 0 or width % 16 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")
@staticmethod
def prepare_latents(
components,
image: PipelineImageInput,
batch_size: int,
num_channels_latents: int = 16,
height: int = 480,
width: int = 832,
num_frames: int = 81,
dtype: Optional[torch.dtype] = None,
device: Optional[torch.device] = None,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
num_latent_frames = (num_frames - 1) // components.vae_scale_factor_temporal + 1
latent_height = height // components.vae_scale_factor_spatial
latent_width = width // components.vae_scale_factor_spatial
shape = (batch_size, num_channels_latents, num_latent_frames, latent_height, latent_width)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device=device, dtype=dtype)
image = image.unsqueeze(2)
video_condition = torch.cat(
[image, image.new_zeros(image.shape[0], image.shape[1], num_frames - 1, height, width)], dim=2
)
video_condition = video_condition.to(device=device, dtype=dtype)
latents_mean = (
torch.tensor(components.vae.config.latents_mean)
.view(1, components.vae.config.z_dim, 1, 1, 1)
.to(latents.device, latents.dtype)
)
latents_std = 1.0 / torch.tensor(components.vae.config.latents_std).view(
1, components.vae.config.z_dim, 1, 1, 1
).to(latents.device, latents.dtype)
if isinstance(generator, list):
latent_condition = [
retrieve_latents(components.vae.encode(video_condition), sample_mode="argmax") for _ in generator
]
latent_condition = torch.cat(latent_condition)
else:
latent_condition = retrieve_latents(components.vae.encode(video_condition), sample_mode="argmax")
latent_condition = latent_condition.repeat(batch_size, 1, 1, 1, 1)
latent_condition = (latent_condition - latents_mean) * latents_std
mask_lat_size = torch.ones(batch_size, 1, num_frames, latent_height, latent_width)
mask_lat_size[:, :, list(range(1, num_frames))] = 0
first_frame_mask = mask_lat_size[:, :, 0:1]
first_frame_mask = torch.repeat_interleave(
first_frame_mask, dim=2, repeats=components.vae_scale_factor_temporal
)
mask_lat_size = torch.concat([first_frame_mask, mask_lat_size[:, :, 1:, :]], dim=2)
mask_lat_size = mask_lat_size.view(
batch_size, -1, components.vae_scale_factor_temporal, latent_height, latent_width
)
mask_lat_size = mask_lat_size.transpose(1, 2)
mask_lat_size = mask_lat_size.to(latent_condition.device)
return latents, torch.concat([mask_lat_size, latent_condition], dim=1)
@torch.no_grad()
def __call__(self, components: ModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
self.check_inputs(block_state.height, block_state.width)
block_state.device = components._execution_device
block_state.num_channels_latents = components.num_channels_latents
batch_size = block_state.batch_size * block_state.num_videos_per_prompt
block_state.latents, block_state.condition = self.prepare_latents(
components,
block_state.processed_image,
batch_size,
block_state.num_channels_latents,
block_state.height,
block_state.width,
block_state.num_frames,
torch.bfloat16,
block_state.device,
block_state.generator,
block_state.latents,
)
self.set_block_state(state, block_state)
return components, state
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