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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 diffusers.configuration_utils import FrozenDict
from diffusers.image_processor import InpaintProcessor, VaeImageProcessor
from diffusers.models import AutoencoderKLQwenImage
from diffusers.utils import logging
from diffusers.modular_pipelines.modular_pipeline import ModularPipelineBlocks, PipelineState
from diffusers.modular_pipelines.modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
from .modular_pipeline import Krea2ModularPipeline, Krea2Pachifier
logger = logging.get_logger(__name__)
# after denoising loop (unpack latents)
class Krea2AfterDenoiseStep(ModularPipelineBlocks):
model_name = "krea2"
@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", Krea2Pachifier, 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 denoised latents unpacked to B, C, 1, H, W"
),
]
@torch.no_grad()
def __call__(self, components: Krea2ModularPipeline, 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
# decode step
class Krea2DecoderStep(ModularPipelineBlocks):
model_name = "krea2"
@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: Krea2ModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
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
# postprocess the decoded images
class Krea2ProcessImagesOutputStep(ModularPipelineBlocks):
model_name = "krea2"
@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: Krea2ModularPipeline, 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
class Krea2InpaintProcessImagesOutputStep(ModularPipelineBlocks):
model_name = "krea2"
@property
def description(self) -> str:
return "postprocess the generated image, optionally apply the mask overlay 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 Krea2InpaintProcessImagesInputStep.",
),
]
@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: Krea2ModularPipeline, 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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