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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 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 | # 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 ...guiders import ClassifierFreeGuidance
from ...models import ErnieImageTransformer2DModel
from ...schedulers import FlowMatchEulerDiscreteScheduler
from ...utils import logging
from ..modular_pipeline import (
BlockState,
LoopSequentialPipelineBlocks,
ModularPipelineBlocks,
PipelineState,
)
from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
from .modular_pipeline import ErnieImageModularPipeline
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class ErnieImageLoopBeforeDenoiser(ModularPipelineBlocks):
model_name = "ernie-image"
@property
def description(self) -> str:
return (
"Step within the denoising loop that prepares the latent model input and timestep tensor. "
"This block should be used to compose the `sub_blocks` attribute of a `LoopSequentialPipelineBlocks` "
"object (e.g. `ErnieImageDenoiseLoopWrapper`)."
)
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("transformer", ErnieImageTransformer2DModel)]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
"latents",
required=True,
type_hint=torch.Tensor,
description="The latents to denoise.",
),
]
@torch.no_grad()
def __call__(self, components: ErnieImageModularPipeline, block_state: BlockState, i: int, t: torch.Tensor):
latents = block_state.latents
block_state.latent_model_input = latents.to(components.transformer.dtype)
block_state.timestep = t.expand(latents.shape[0]).to(components.transformer.dtype)
return components, block_state
class ErnieImageLoopDenoiser(ModularPipelineBlocks):
model_name = "ernie-image"
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("transformer", ErnieImageTransformer2DModel),
ComponentSpec(
"guider",
ClassifierFreeGuidance,
config=FrozenDict({"guidance_scale": 4.0}),
default_creation_method="from_config",
),
]
@property
def description(self) -> str:
return (
"Step within the denoising loop that runs the ErnieImage transformer with classifier-free guidance via "
"the configured guider."
)
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
"text_bth",
required=True,
type_hint=torch.Tensor,
description="Padded text hidden states fed into the transformer.",
),
InputParam(
"text_lens",
required=True,
type_hint=torch.Tensor,
description="Per-prompt text lengths used by the transformer attention mask.",
),
InputParam(
"negative_text_bth",
type_hint=torch.Tensor,
description="Padded negative text hidden states for classifier-free guidance.",
),
InputParam(
"negative_text_lens",
type_hint=torch.Tensor,
description="Per-prompt negative text lengths for classifier-free guidance.",
),
InputParam(
"num_inference_steps",
required=True,
type_hint=int,
description="Total number of denoising steps. Used by the guider for step-aware scheduling.",
),
]
@torch.no_grad()
def __call__(self, components: ErnieImageModularPipeline, block_state: BlockState, i: int, t: torch.Tensor):
guider_inputs = {
"text_bth": (block_state.text_bth, block_state.negative_text_bth),
"text_lens": (block_state.text_lens, block_state.negative_text_lens),
}
components.guider.set_state(step=i, num_inference_steps=block_state.num_inference_steps, timestep=t)
guider_state = components.guider.prepare_inputs(guider_inputs)
for guider_state_batch in guider_state:
components.guider.prepare_models(components.transformer)
cond_kwargs = {name: getattr(guider_state_batch, name) for name in guider_inputs.keys()}
noise_pred = components.transformer(
hidden_states=block_state.latent_model_input,
timestep=block_state.timestep,
return_dict=False,
**cond_kwargs,
)[0]
guider_state_batch.noise_pred = noise_pred
components.guider.cleanup_models(components.transformer)
block_state.noise_pred = components.guider(guider_state)[0]
return components, block_state
class ErnieImageLoopAfterDenoiser(ModularPipelineBlocks):
model_name = "ernie-image"
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("scheduler", FlowMatchEulerDiscreteScheduler)]
@property
def description(self) -> str:
return "Step within the denoising loop that updates the latents using the scheduler step."
@torch.no_grad()
def __call__(self, components: ErnieImageModularPipeline, block_state: BlockState, i: int, t: torch.Tensor):
latents_dtype = block_state.latents.dtype
block_state.latents = components.scheduler.step(
block_state.noise_pred, t, block_state.latents, return_dict=False
)[0]
if block_state.latents.dtype != latents_dtype and torch.backends.mps.is_available():
block_state.latents = block_state.latents.to(latents_dtype)
return components, block_state
class ErnieImageDenoiseLoopWrapper(LoopSequentialPipelineBlocks):
model_name = "ernie-image"
@property
def description(self) -> str:
return (
"Pipeline block that iteratively denoises the latents over `timesteps`. "
"The specific steps within each iteration can be customized with `sub_blocks` attribute."
)
@property
def loop_expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("scheduler", FlowMatchEulerDiscreteScheduler),
ComponentSpec("transformer", ErnieImageTransformer2DModel),
]
@property
def loop_inputs(self) -> list[InputParam]:
return [
InputParam(
"timesteps",
required=True,
type_hint=torch.Tensor,
description="The timesteps to use for inference.",
),
InputParam(
"num_inference_steps",
required=True,
type_hint=int,
description="The number of denoising steps.",
),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [OutputParam("latents", type_hint=torch.Tensor, description="The denoised latents.")]
@torch.no_grad()
def __call__(self, components: ErnieImageModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
with self.progress_bar(total=block_state.num_inference_steps) as progress_bar:
for i, t in enumerate(block_state.timesteps):
components, block_state = self.loop_step(components, block_state, i=i, t=t)
progress_bar.update()
self.set_block_state(state, block_state)
return components, state
class ErnieImageDenoiseStep(ErnieImageDenoiseLoopWrapper):
block_classes = [
ErnieImageLoopBeforeDenoiser,
ErnieImageLoopDenoiser,
ErnieImageLoopAfterDenoiser,
]
block_names = ["before_denoiser", "denoiser", "after_denoiser"]
@property
def description(self) -> str:
return (
"Denoise step that iteratively denoises the latents. At each iteration it runs:\n"
" - `ErnieImageLoopBeforeDenoiser`\n"
" - `ErnieImageLoopDenoiser`\n"
" - `ErnieImageLoopAfterDenoiser`"
)
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