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# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# 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 ...utils import logging
from ..modular_pipeline import AutoPipelineBlocks, SequentialPipelineBlocks
from ..modular_pipeline_utils import InsertableDict
from .before_denoise import (
FluxImg2ImgPrepareLatentsStep,
FluxImg2ImgSetTimestepsStep,
FluxInputStep,
FluxPrepareLatentsStep,
FluxSetTimestepsStep,
)
from .decoders import FluxDecodeStep
from .denoise import FluxDenoiseStep
from .encoders import FluxTextEncoderStep, FluxVaeEncoderStep
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# vae encoder (run before before_denoise)
class FluxAutoVaeEncoderStep(AutoPipelineBlocks):
block_classes = [FluxVaeEncoderStep]
block_names = ["img2img"]
block_trigger_inputs = ["image"]
@property
def description(self):
return (
"Vae encoder step that encode the image inputs into their latent representations.\n"
+ "This is an auto pipeline block that works for img2img tasks.\n"
+ " - `FluxVaeEncoderStep` (img2img) is used when only `image` is provided."
+ " - if `image` is provided, step will be skipped."
)
# before_denoise: text2img, img2img
class FluxBeforeDenoiseStep(SequentialPipelineBlocks):
block_classes = [
FluxInputStep,
FluxPrepareLatentsStep,
FluxSetTimestepsStep,
]
block_names = ["input", "prepare_latents", "set_timesteps"]
@property
def description(self):
return (
"Before denoise step that prepare the inputs for the denoise step.\n"
+ "This is a sequential pipeline blocks:\n"
+ " - `FluxInputStep` is used to adjust the batch size of the model inputs\n"
+ " - `FluxPrepareLatentsStep` is used to prepare the latents\n"
+ " - `FluxSetTimestepsStep` is used to set the timesteps\n"
)
# before_denoise: img2img
class FluxImg2ImgBeforeDenoiseStep(SequentialPipelineBlocks):
block_classes = [FluxInputStep, FluxImg2ImgSetTimestepsStep, FluxImg2ImgPrepareLatentsStep]
block_names = ["input", "set_timesteps", "prepare_latents"]
@property
def description(self):
return (
"Before denoise step that prepare the inputs for the denoise step for img2img task.\n"
+ "This is a sequential pipeline blocks:\n"
+ " - `FluxInputStep` is used to adjust the batch size of the model inputs\n"
+ " - `FluxImg2ImgSetTimestepsStep` is used to set the timesteps\n"
+ " - `FluxImg2ImgPrepareLatentsStep` is used to prepare the latents\n"
)
# before_denoise: all task (text2img, img2img)
class FluxAutoBeforeDenoiseStep(AutoPipelineBlocks):
block_classes = [FluxBeforeDenoiseStep, FluxImg2ImgBeforeDenoiseStep]
block_names = ["text2image", "img2img"]
block_trigger_inputs = [None, "image_latents"]
@property
def description(self):
return (
"Before denoise step that prepare the inputs for the denoise step.\n"
+ "This is an auto pipeline block that works for text2image.\n"
+ " - `FluxBeforeDenoiseStep` (text2image) is used.\n"
+ " - `FluxImg2ImgBeforeDenoiseStep` (img2img) is used when only `image_latents` is provided.\n"
)
# denoise: text2image
class FluxAutoDenoiseStep(AutoPipelineBlocks):
block_classes = [FluxDenoiseStep]
block_names = ["denoise"]
block_trigger_inputs = [None]
@property
def description(self) -> str:
return (
"Denoise step that iteratively denoise the latents. "
"This is a auto pipeline block that works for text2image and img2img tasks."
" - `FluxDenoiseStep` (denoise) for text2image and img2img tasks."
)
# decode: all task (text2img, img2img, inpainting)
class FluxAutoDecodeStep(AutoPipelineBlocks):
block_classes = [FluxDecodeStep]
block_names = ["non-inpaint"]
block_trigger_inputs = [None]
@property
def description(self):
return "Decode step that decode the denoised latents into image outputs.\n - `FluxDecodeStep`"
# text2image
class FluxAutoBlocks(SequentialPipelineBlocks):
block_classes = [
FluxTextEncoderStep,
FluxAutoVaeEncoderStep,
FluxAutoBeforeDenoiseStep,
FluxAutoDenoiseStep,
FluxAutoDecodeStep,
]
block_names = ["text_encoder", "image_encoder", "before_denoise", "denoise", "decoder"]
@property
def description(self):
return (
"Auto Modular pipeline for text-to-image and image-to-image using Flux.\n"
+ "- for text-to-image generation, all you need to provide is `prompt`\n"
+ "- for image-to-image generation, you need to provide either `image` or `image_latents`"
)
TEXT2IMAGE_BLOCKS = InsertableDict(
[
("text_encoder", FluxTextEncoderStep),
("input", FluxInputStep),
("prepare_latents", FluxPrepareLatentsStep),
("set_timesteps", FluxSetTimestepsStep),
("denoise", FluxDenoiseStep),
("decode", FluxDecodeStep),
]
)
IMAGE2IMAGE_BLOCKS = InsertableDict(
[
("text_encoder", FluxTextEncoderStep),
("image_encoder", FluxVaeEncoderStep),
("input", FluxInputStep),
("set_timesteps", FluxImg2ImgSetTimestepsStep),
("prepare_latents", FluxImg2ImgPrepareLatentsStep),
("denoise", FluxDenoiseStep),
("decode", FluxDecodeStep),
]
)
AUTO_BLOCKS = InsertableDict(
[
("text_encoder", FluxTextEncoderStep),
("image_encoder", FluxAutoVaeEncoderStep),
("before_denoise", FluxAutoBeforeDenoiseStep),
("denoise", FluxAutoDenoiseStep),
("decode", FluxAutoDecodeStep),
]
)
ALL_BLOCKS = {"text2image": TEXT2IMAGE_BLOCKS, "img2img": IMAGE2IMAGE_BLOCKS, "auto": AUTO_BLOCKS}
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