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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 typing import Any, List, Tuple, Union
import numpy as np
import PIL
import torch
from ...configuration_utils import FrozenDict
from ...image_processor import VaeImageProcessor
from ...models import AutoencoderKL
from ...models.attention_processor import AttnProcessor2_0, XFormersAttnProcessor
from ...utils import logging
from ..modular_pipeline import (
ModularPipelineBlocks,
PipelineState,
)
from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class StableDiffusionXLDecodeStep(ModularPipelineBlocks):
model_name = "stable-diffusion-xl"
@property
def expected_components(self) -> List[ComponentSpec]:
return [
ComponentSpec("vae", AutoencoderKL),
ComponentSpec(
"image_processor",
VaeImageProcessor,
config=FrozenDict({"vae_scale_factor": 8}),
default_creation_method="from_config",
),
]
@property
def description(self) -> str:
return "Step that decodes the denoised latents into images"
@property
def inputs(self) -> List[Tuple[str, Any]]:
return [
InputParam("output_type", default="pil"),
InputParam(
"latents",
required=True,
type_hint=torch.Tensor,
description="The denoised latents from the denoising step",
),
]
@property
def intermediate_outputs(self) -> List[str]:
return [
OutputParam(
"images",
type_hint=Union[List[PIL.Image.Image], List[torch.Tensor], List[np.array]],
description="The generated images, can be a PIL.Image.Image, torch.Tensor or a numpy array",
)
]
@staticmethod
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae with self->components
def upcast_vae(components):
dtype = components.vae.dtype
components.vae.to(dtype=torch.float32)
use_torch_2_0_or_xformers = isinstance(
components.vae.decoder.mid_block.attentions[0].processor,
(
AttnProcessor2_0,
XFormersAttnProcessor,
),
)
# if xformers or torch_2_0 is used attention block does not need
# to be in float32 which can save lots of memory
if use_torch_2_0_or_xformers:
components.vae.post_quant_conv.to(dtype)
components.vae.decoder.conv_in.to(dtype)
components.vae.decoder.mid_block.to(dtype)
@torch.no_grad()
def __call__(self, components, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
if not block_state.output_type == "latent":
latents = block_state.latents
# make sure the VAE is in float32 mode, as it overflows in float16
block_state.needs_upcasting = components.vae.dtype == torch.float16 and components.vae.config.force_upcast
if block_state.needs_upcasting:
self.upcast_vae(components)
latents = latents.to(next(iter(components.vae.post_quant_conv.parameters())).dtype)
elif latents.dtype != components.vae.dtype:
if torch.backends.mps.is_available():
# some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
components.vae = components.vae.to(latents.dtype)
# unscale/denormalize the latents
# denormalize with the mean and std if available and not None
block_state.has_latents_mean = (
hasattr(components.vae.config, "latents_mean") and components.vae.config.latents_mean is not None
)
block_state.has_latents_std = (
hasattr(components.vae.config, "latents_std") and components.vae.config.latents_std is not None
)
if block_state.has_latents_mean and block_state.has_latents_std:
block_state.latents_mean = (
torch.tensor(components.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)
)
block_state.latents_std = (
torch.tensor(components.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)
)
latents = (
latents * block_state.latents_std / components.vae.config.scaling_factor + block_state.latents_mean
)
else:
latents = latents / components.vae.config.scaling_factor
block_state.images = components.vae.decode(latents, return_dict=False)[0]
# cast back to fp16 if needed
if block_state.needs_upcasting:
components.vae.to(dtype=torch.float16)
else:
block_state.images = block_state.latents
# apply watermark if available
if hasattr(components, "watermark") and components.watermark is not None:
block_state.images = components.watermark.apply_watermark(block_state.images)
block_state.images = components.image_processor.postprocess(
block_state.images, output_type=block_state.output_type
)
self.set_block_state(state, block_state)
return components, state
class StableDiffusionXLInpaintOverlayMaskStep(ModularPipelineBlocks):
model_name = "stable-diffusion-xl"
@property
def description(self) -> str:
return (
"A post-processing step that overlays the mask on the image (inpainting task only).\n"
+ "only needed when you are using the `padding_mask_crop` option when pre-processing the image and mask"
)
@property
def expected_components(self) -> List[ComponentSpec]:
return [
ComponentSpec(
"image_processor",
VaeImageProcessor,
config=FrozenDict({"vae_scale_factor": 8}),
default_creation_method="from_config",
),
]
@property
def inputs(self) -> List[Tuple[str, Any]]:
return [
InputParam("image"),
InputParam("mask_image"),
InputParam("padding_mask_crop"),
InputParam(
"images",
type_hint=Union[List[PIL.Image.Image], List[torch.Tensor], List[np.array]],
description="The generated images from the decode step",
),
InputParam(
"crops_coords",
type_hint=Tuple[int, int],
description="The crop coordinates to use for preprocess/postprocess the image and mask, for inpainting task only. Can be generated in vae_encode step.",
),
]
@torch.no_grad()
def __call__(self, components, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
if block_state.padding_mask_crop is not None and block_state.crops_coords is not None:
block_state.images = [
components.image_processor.apply_overlay(
block_state.mask_image, block_state.image, i, block_state.crops_coords
)
for i in block_state.images
]
self.set_block_state(state, block_state)
return components, state
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