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cd458ae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | # Copyright 2026 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.
import PIL
import torch
from ...configuration_utils import FrozenDict
from ...image_processor import VaeImageProcessor
from ...models import AutoencoderKL
from ...utils import logging
from ..modular_pipeline import ModularPipelineBlocks, PipelineState
from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
logger = logging.get_logger(__name__)
class StableDiffusion3DecodeStep(ModularPipelineBlocks):
model_name = "stable-diffusion-3"
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("vae", AutoencoderKL),
ComponentSpec(
"image_processor",
VaeImageProcessor,
config=FrozenDict({"vae_scale_factor": 8, "vae_latent_channels": 16}),
default_creation_method="from_config",
),
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
"output_type",
default="pil",
description="The output format of the generated image (e.g., 'pil', 'pt', 'np').",
),
InputParam(
"latents",
required=True,
type_hint=torch.Tensor,
description="The denoised latents to be decoded.",
),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [OutputParam("images", type_hint=list[PIL.Image.Image] | torch.Tensor)]
@torch.no_grad()
def __call__(self, components, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
vae = components.vae
if not block_state.output_type == "latent":
latents = (block_state.latents / vae.config.scaling_factor) + vae.config.shift_factor
block_state.images = vae.decode(latents, return_dict=False)[0]
block_state.images = components.image_processor.postprocess(
block_state.images, output_type=block_state.output_type
)
else:
block_state.images = block_state.latents
self.set_block_state(state, block_state)
return components, state
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