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Running on Zero
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b8c861f | 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 81 82 83 84 85 86 87 88 89 90 91 92 93 | # Copyright 2025 Baidu ERNIE-Image Team and 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 torch
from ...configuration_utils import FrozenDict
from ...image_processor import VaeImageProcessor
from ...models import AutoencoderKLFlux2
from ...utils import logging
from ..modular_pipeline import ModularPipelineBlocks, PipelineState
from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
from .modular_pipeline import ErnieImageModularPipeline, ErnieImagePachifier
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class ErnieImageVaeDecoderStep(ModularPipelineBlocks):
model_name = "ernie-image"
@property
def description(self) -> str:
return "Step that decodes the denoised latents into images (unpachify, BN denormalization, VAE decode)."
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("vae", AutoencoderKLFlux2),
ComponentSpec(
"pachifier",
ErnieImagePachifier,
config=FrozenDict({"patch_size": 2}),
default_creation_method="from_config",
),
ComponentSpec(
"image_processor",
VaeImageProcessor,
config=FrozenDict({"vae_scale_factor": 16}),
default_creation_method="from_config",
),
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
"latents",
required=True,
type_hint=torch.Tensor,
description="The latents to decode into images.",
),
InputParam(
"output_type",
type_hint=str,
default="pil",
description="Output format: 'pil', 'np', or 'pt'.",
),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [OutputParam("images", type_hint=list, description="The generated images.")]
@torch.no_grad()
def __call__(self, components: ErnieImageModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
vae = components.vae
device = block_state.latents.device
latents = block_state.latents
bn_mean = vae.bn.running_mean.view(1, -1, 1, 1).to(device=device, dtype=latents.dtype)
bn_std = torch.sqrt(vae.bn.running_var.view(1, -1, 1, 1) + 1e-5).to(device=device, dtype=latents.dtype)
latents = latents * bn_std + bn_mean
latents = components.pachifier.unpack_latents(latents)
images = vae.decode(latents.to(vae.dtype), return_dict=False)[0]
block_state.images = components.image_processor.postprocess(images, output_type=block_state.output_type)
self.set_block_state(state, block_state)
return components, state
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