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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.
import torch
from ...configuration_utils import FrozenDict
from ...pipelines.flux2.image_processor import Flux2ImageProcessor
from ...utils import logging
from ..modular_pipeline import ModularPipelineBlocks, PipelineState
from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
from .modular_pipeline import Flux2ModularPipeline
logger = logging.get_logger(__name__)
class Flux2TextInputStep(ModularPipelineBlocks):
model_name = "flux2"
@property
def description(self) -> str:
return (
"This step:\n"
" 1. Determines `batch_size` and `dtype` based on `prompt_embeds`\n"
" 2. Ensures all text embeddings have consistent batch sizes (batch_size * num_images_per_prompt)"
)
@property
def inputs(self) -> list[InputParam]:
return [
InputParam("num_images_per_prompt", default=1),
InputParam(
"prompt_embeds",
required=True,
kwargs_type="denoiser_input_fields",
type_hint=torch.Tensor,
description="Pre-generated text embeddings. Can be generated from text_encoder step.",
),
]
@property
def intermediate_outputs(self) -> list[str]:
return [
OutputParam(
"batch_size",
type_hint=int,
description="Number of prompts, the final batch size of model inputs should be batch_size * num_images_per_prompt",
),
OutputParam(
"dtype",
type_hint=torch.dtype,
description="Data type of model tensor inputs (determined by `prompt_embeds`)",
),
OutputParam(
"prompt_embeds",
type_hint=torch.Tensor,
kwargs_type="denoiser_input_fields",
description="Text embeddings used to guide the image generation",
),
]
@torch.no_grad()
def __call__(self, components: Flux2ModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
block_state.batch_size = block_state.prompt_embeds.shape[0]
block_state.dtype = block_state.prompt_embeds.dtype
_, seq_len, _ = block_state.prompt_embeds.shape
block_state.prompt_embeds = block_state.prompt_embeds.repeat(1, block_state.num_images_per_prompt, 1)
block_state.prompt_embeds = block_state.prompt_embeds.view(
block_state.batch_size * block_state.num_images_per_prompt, seq_len, -1
)
self.set_block_state(state, block_state)
return components, state
class Flux2KleinBaseTextInputStep(ModularPipelineBlocks):
model_name = "flux2-klein"
@property
def description(self) -> str:
return (
"This step:\n"
" 1. Determines `batch_size` and `dtype` based on `prompt_embeds`\n"
" 2. Ensures all text embeddings have consistent batch sizes (batch_size * num_images_per_prompt)"
)
@property
def inputs(self) -> list[InputParam]:
return [
InputParam("num_images_per_prompt", default=1),
InputParam(
"prompt_embeds",
required=True,
kwargs_type="denoiser_input_fields",
type_hint=torch.Tensor,
description="Pre-generated text embeddings. Can be generated from text_encoder step.",
),
InputParam(
"negative_prompt_embeds",
required=False,
kwargs_type="denoiser_input_fields",
type_hint=torch.Tensor,
description="Pre-generated negative text embeddings. Can be generated from text_encoder step.",
),
]
@property
def intermediate_outputs(self) -> list[str]:
return [
OutputParam(
"batch_size",
type_hint=int,
description="Number of prompts, the final batch size of model inputs should be batch_size * num_images_per_prompt",
),
OutputParam(
"dtype",
type_hint=torch.dtype,
description="Data type of model tensor inputs (determined by `prompt_embeds`)",
),
OutputParam(
"prompt_embeds",
type_hint=torch.Tensor,
kwargs_type="denoiser_input_fields",
description="Text embeddings used to guide the image generation",
),
OutputParam(
"negative_prompt_embeds",
type_hint=torch.Tensor,
kwargs_type="denoiser_input_fields",
description="Negative text embeddings used to guide the image generation",
),
]
@torch.no_grad()
def __call__(self, components: Flux2ModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
block_state.batch_size = block_state.prompt_embeds.shape[0]
block_state.dtype = block_state.prompt_embeds.dtype
_, seq_len, _ = block_state.prompt_embeds.shape
block_state.prompt_embeds = block_state.prompt_embeds.repeat(1, block_state.num_images_per_prompt, 1)
block_state.prompt_embeds = block_state.prompt_embeds.view(
block_state.batch_size * block_state.num_images_per_prompt, seq_len, -1
)
if block_state.negative_prompt_embeds is not None:
_, seq_len, _ = block_state.negative_prompt_embeds.shape
block_state.negative_prompt_embeds = block_state.negative_prompt_embeds.repeat(
1, block_state.num_images_per_prompt, 1
)
block_state.negative_prompt_embeds = block_state.negative_prompt_embeds.view(
block_state.batch_size * block_state.num_images_per_prompt, seq_len, -1
)
self.set_block_state(state, block_state)
return components, state
class Flux2ProcessImagesInputStep(ModularPipelineBlocks):
model_name = "flux2"
@property
def description(self) -> str:
return "Image preprocess step for Flux2. Validates and preprocesses reference images."
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec(
"image_processor",
Flux2ImageProcessor,
config=FrozenDict({"vae_scale_factor": 16, "vae_latent_channels": 32}),
default_creation_method="from_config",
),
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam("image"),
InputParam("height"),
InputParam("width"),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [OutputParam(name="condition_images", type_hint=list[torch.Tensor])]
@torch.no_grad()
def __call__(self, components: Flux2ModularPipeline, state: PipelineState):
block_state = self.get_block_state(state)
images = block_state.image
if images is None:
block_state.condition_images = None
self.set_block_state(state, block_state)
return components, state
if not isinstance(images, list):
images = [images]
condition_images = []
for img in images:
components.image_processor.check_image_input(img)
image_width, image_height = img.size
if image_width * image_height > 1024 * 1024:
img = components.image_processor._resize_to_target_area(img, 1024 * 1024)
image_width, image_height = img.size
multiple_of = components.vae_scale_factor * 2
image_width = (image_width // multiple_of) * multiple_of
image_height = (image_height // multiple_of) * multiple_of
condition_img = components.image_processor.preprocess(
img, height=image_height, width=image_width, resize_mode="crop"
)
condition_images.append(condition_img)
if block_state.height is None:
block_state.height = image_height
if block_state.width is None:
block_state.width = image_width
block_state.condition_images = condition_images
self.set_block_state(state, block_state)
return components, state
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