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#
# 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 Any
import torch
from ...configuration_utils import FrozenDict
from ...image_processor import InpaintProcessor, VaeImageProcessor
from ...models import AutoencoderKLQwenImage
from ...utils import logging
from ..modular_pipeline import ModularPipelineBlocks, PipelineState
from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
from .modular_pipeline import QwenImageLayeredPachifier, QwenImageModularPipeline, QwenImagePachifier
logger = logging.get_logger(__name__)
# after denoising loop (unpack latents)
# auto_docstring
class QwenImageAfterDenoiseStep(ModularPipelineBlocks):
"""
Step that unpack the latents from 3D tensor (batch_size, sequence_length, channels) into 5D tensor (batch_size,
channels, 1, height, width)
Components:
pachifier (`QwenImagePachifier`)
Inputs:
height (`int`):
The height in pixels of the generated image.
width (`int`):
The width in pixels of the generated image.
latents (`Tensor`):
The latents to decode, can be generated in the denoise step.
Outputs:
latents (`Tensor`):
The denoisedlatents unpacked to B, C, 1, H, W
"""
model_name = "qwenimage"
@property
def description(self) -> str:
return "Step that unpack the latents from 3D tensor (batch_size, sequence_length, channels) into 5D tensor (batch_size, channels, 1, height, width)"
@property
def expected_components(self) -> list[ComponentSpec]:
components = [
ComponentSpec("pachifier", QwenImagePachifier, default_creation_method="from_config"),
]
return components
@property
def inputs(self) -> list[InputParam]:
return [
InputParam.template("height", required=True),
InputParam.template("width", required=True),
InputParam(
name="latents",
required=True,
type_hint=torch.Tensor,
description="The latents to decode, can be generated in the denoise step.",
),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
name="latents", type_hint=torch.Tensor, description="The denoisedlatents unpacked to B, C, 1, H, W"
),
]
@torch.no_grad()
def __call__(self, components: QwenImageModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
vae_scale_factor = components.vae_scale_factor
block_state.latents = components.pachifier.unpack_latents(
block_state.latents, block_state.height, block_state.width, vae_scale_factor=vae_scale_factor
)
self.set_block_state(state, block_state)
return components, state
# auto_docstring
class QwenImageLayeredAfterDenoiseStep(ModularPipelineBlocks):
"""
Unpack latents from (B, seq, C*4) to (B, C, layers+1, H, W) after denoising.
Components:
pachifier (`QwenImageLayeredPachifier`)
Inputs:
latents (`Tensor`):
The denoised latents to decode, can be generated in the denoise step.
height (`int`):
The height in pixels of the generated image.
width (`int`):
The width in pixels of the generated image.
layers (`int`, *optional*, defaults to 4):
Number of layers to extract from the image
Outputs:
latents (`Tensor`):
Denoised latents. (unpacked to B, C, layers+1, H, W)
"""
model_name = "qwenimage-layered"
@property
def description(self) -> str:
return "Unpack latents from (B, seq, C*4) to (B, C, layers+1, H, W) after denoising."
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("pachifier", QwenImageLayeredPachifier, default_creation_method="from_config"),
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
name="latents",
required=True,
type_hint=torch.Tensor,
description="The denoised latents to decode, can be generated in the denoise step.",
),
InputParam.template("height", required=True),
InputParam.template("width", required=True),
InputParam.template("layers"),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam.template("latents", note="unpacked to B, C, layers+1, H, W"),
]
@torch.no_grad()
def __call__(self, components, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
# Unpack: (B, seq, C*4) -> (B, C, layers+1, H, W)
block_state.latents = components.pachifier.unpack_latents(
block_state.latents,
block_state.height,
block_state.width,
block_state.layers,
components.vae_scale_factor,
)
self.set_block_state(state, block_state)
return components, state
# decode step
# auto_docstring
class QwenImageDecoderStep(ModularPipelineBlocks):
"""
Step that decodes the latents to images
Components:
vae (`AutoencoderKLQwenImage`)
Inputs:
latents (`Tensor`):
The denoised latents to decode, can be generated in the denoise step and unpacked in the after denoise
step.
Outputs:
images (`list`):
Generated images. (tensor output of the vae decoder.)
"""
model_name = "qwenimage"
@property
def description(self) -> str:
return "Step that decodes the latents to images"
@property
def expected_components(self) -> list[ComponentSpec]:
components = [
ComponentSpec("vae", AutoencoderKLQwenImage),
]
return components
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
name="latents",
required=True,
type_hint=torch.Tensor,
description="The denoised latents to decode, can be generated in the denoise step and unpacked in the after denoise step.",
),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [OutputParam.template("images", note="tensor output of the vae decoder.")]
@torch.no_grad()
def __call__(self, components: QwenImageModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
# YiYi Notes: remove support for output_type = "latents', we can just skip decode/encode step in modular
if block_state.latents.ndim == 4:
block_state.latents = block_state.latents.unsqueeze(dim=1)
elif block_state.latents.ndim != 5:
raise ValueError(
f"expect latents to be a 4D or 5D tensor but got: {block_state.latents.shape}. Please make sure the latents are unpacked before decode step."
)
block_state.latents = block_state.latents.to(components.vae.dtype)
latents_mean = (
torch.tensor(components.vae.config.latents_mean)
.view(1, components.vae.config.z_dim, 1, 1, 1)
.to(block_state.latents.device, block_state.latents.dtype)
)
latents_std = 1.0 / torch.tensor(components.vae.config.latents_std).view(
1, components.vae.config.z_dim, 1, 1, 1
).to(block_state.latents.device, block_state.latents.dtype)
block_state.latents = block_state.latents / latents_std + latents_mean
block_state.images = components.vae.decode(block_state.latents, return_dict=False)[0][:, :, 0]
self.set_block_state(state, block_state)
return components, state
# auto_docstring
class QwenImageLayeredDecoderStep(ModularPipelineBlocks):
"""
Decode unpacked latents (B, C, layers+1, H, W) into layer images.
Components:
vae (`AutoencoderKLQwenImage`) image_processor (`VaeImageProcessor`)
Inputs:
latents (`Tensor`):
The denoised latents to decode, can be generated in the denoise step and unpacked in the after denoise
step.
output_type (`str`, *optional*, defaults to pil):
Output format: 'pil', 'np', 'pt'.
Outputs:
images (`list`):
Generated images.
"""
model_name = "qwenimage-layered"
@property
def description(self) -> str:
return "Decode unpacked latents (B, C, layers+1, H, W) into layer images."
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("vae", AutoencoderKLQwenImage),
ComponentSpec(
"image_processor",
VaeImageProcessor,
config=FrozenDict({"vae_scale_factor": 16}),
default_creation_method="from_config",
),
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
name="latents",
required=True,
type_hint=torch.Tensor,
description="The denoised latents to decode, can be generated in the denoise step and unpacked in the after denoise step.",
),
InputParam.template("output_type"),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [OutputParam.template("images")]
@torch.no_grad()
def __call__(self, components, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
latents = block_state.latents
# 1. VAE normalization
latents = latents.to(components.vae.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)
latents = latents / latents_std + latents_mean
# 2. Reshape for batch decoding: (B, C, layers+1, H, W) -> (B*layers, C, 1, H, W)
b, c, f, h, w = latents.shape
# 3. Remove first frame (composite), keep layers frames
latents = latents[:, :, 1:]
latents = latents.permute(0, 2, 1, 3, 4).reshape(-1, c, 1, h, w)
# 4. Decode: (B*layers, C, 1, H, W) -> (B*layers, C, H, W)
image = components.vae.decode(latents, return_dict=False)[0]
image = image.squeeze(2)
# 5. Postprocess - returns flat list of B*layers images
image = components.image_processor.postprocess(image, output_type=block_state.output_type)
# 6. Chunk into list per batch item
images = []
for bidx in range(b):
images.append(image[bidx * f : (bidx + 1) * f])
block_state.images = images
self.set_block_state(state, block_state)
return components, state
# postprocess the decoded images
# auto_docstring
class QwenImageProcessImagesOutputStep(ModularPipelineBlocks):
"""
postprocess the generated image
Components:
image_processor (`VaeImageProcessor`)
Inputs:
images (`Tensor`):
the generated image tensor from decoders step
output_type (`str`, *optional*, defaults to pil):
Output format: 'pil', 'np', 'pt'.
Outputs:
images (`list`):
Generated images.
"""
model_name = "qwenimage"
@property
def description(self) -> str:
return "postprocess the generated image"
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec(
"image_processor",
VaeImageProcessor,
config=FrozenDict({"vae_scale_factor": 16}),
default_creation_method="from_config",
),
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
name="images",
required=True,
type_hint=torch.Tensor,
description="the generated image tensor from decoders step",
),
InputParam.template("output_type"),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [OutputParam.template("images")]
@staticmethod
def check_inputs(output_type):
if output_type not in ["pil", "np", "pt"]:
raise ValueError(f"Invalid output_type: {output_type}")
@torch.no_grad()
def __call__(self, components: QwenImageModularPipeline, state: PipelineState):
block_state = self.get_block_state(state)
self.check_inputs(block_state.output_type)
block_state.images = components.image_processor.postprocess(
image=block_state.images,
output_type=block_state.output_type,
)
self.set_block_state(state, block_state)
return components, state
# auto_docstring
class QwenImageInpaintProcessImagesOutputStep(ModularPipelineBlocks):
"""
postprocess the generated image, optional apply the mask overally to the original image..
Components:
image_mask_processor (`InpaintProcessor`)
Inputs:
images (`Tensor`):
the generated image tensor from decoders step
output_type (`str`, *optional*, defaults to pil):
Output format: 'pil', 'np', 'pt'.
mask_overlay_kwargs (`dict`, *optional*):
The kwargs for the postprocess step to apply the mask overlay. generated in
InpaintProcessImagesInputStep.
Outputs:
images (`list`):
Generated images.
"""
model_name = "qwenimage"
@property
def description(self) -> str:
return "postprocess the generated image, optional apply the mask overally to the original image.."
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec(
"image_mask_processor",
InpaintProcessor,
config=FrozenDict({"vae_scale_factor": 16}),
default_creation_method="from_config",
),
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
name="images",
required=True,
type_hint=torch.Tensor,
description="the generated image tensor from decoders step",
),
InputParam.template("output_type"),
InputParam(
name="mask_overlay_kwargs",
type_hint=dict[str, Any],
description="The kwargs for the postprocess step to apply the mask overlay. generated in InpaintProcessImagesInputStep.",
),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [OutputParam.template("images")]
@staticmethod
def check_inputs(output_type, mask_overlay_kwargs):
if output_type not in ["pil", "np", "pt"]:
raise ValueError(f"Invalid output_type: {output_type}")
if mask_overlay_kwargs and output_type != "pil":
raise ValueError("only support output_type 'pil' for mask overlay")
@torch.no_grad()
def __call__(self, components: QwenImageModularPipeline, state: PipelineState):
block_state = self.get_block_state(state)
self.check_inputs(block_state.output_type, block_state.mask_overlay_kwargs)
if block_state.mask_overlay_kwargs is None:
mask_overlay_kwargs = {}
else:
mask_overlay_kwargs = block_state.mask_overlay_kwargs
block_state.images = components.image_mask_processor.postprocess(
image=block_state.images,
**mask_overlay_kwargs,
)
self.set_block_state(state, block_state)
return components, state
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