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186aa49 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 | # 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 ...models import ErnieImageTransformer2DModel
from ...schedulers import FlowMatchEulerDiscreteScheduler
from ...utils import logging
from ...utils.torch_utils import randn_tensor
from ..modular_pipeline import ModularPipelineBlocks, PipelineState
from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
from .modular_pipeline import ErnieImageModularPipeline
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def _pad_text(
text_hiddens: list[torch.Tensor], device: torch.device, dtype: torch.dtype, text_in_dim: int
) -> tuple[torch.Tensor, torch.Tensor]:
"""Pad a list of variable-length text hidden states to a common length and return (padded, lengths)."""
batch_size = len(text_hiddens)
if batch_size == 0:
return (
torch.zeros((0, 0, text_in_dim), device=device, dtype=dtype),
torch.zeros((0,), device=device, dtype=torch.long),
)
normalized = [t.squeeze(1).to(device).to(dtype) if t.dim() == 3 else t.to(device).to(dtype) for t in text_hiddens]
lengths = torch.tensor([t.shape[0] for t in normalized], device=device, dtype=torch.long)
max_length = int(lengths.max().item())
padded = torch.zeros((batch_size, max_length, text_in_dim), device=device, dtype=dtype)
for i, t in enumerate(normalized):
padded[i, : t.shape[0], :] = t
return padded, lengths
class ErnieImageTextInputStep(ModularPipelineBlocks):
model_name = "ernie-image"
@property
def description(self) -> str:
return (
"Input processing step that pads the variable-length text hidden states to a common length and "
"produces `text_bth` / `text_lens` tensors consumed by the denoiser."
)
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("transformer", ErnieImageTransformer2DModel)]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
"prompt_embeds",
required=True,
type_hint=list,
description="List of per-prompt text embeddings from the text encoder step.",
),
InputParam(
"negative_prompt_embeds",
type_hint=list,
description="List of per-prompt negative text embeddings from the text encoder step.",
),
InputParam(
"num_images_per_prompt",
type_hint=int,
default=1,
description="Number of images to generate per prompt.",
),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam("batch_size", type_hint=int, description="The number of prompts in the batch."),
OutputParam(
"text_bth",
type_hint=torch.Tensor,
kwargs_type="denoiser_input_fields",
description="Padded text hidden states of shape (B, T_max, H) fed into the transformer.",
),
OutputParam(
"text_lens",
type_hint=torch.Tensor,
kwargs_type="denoiser_input_fields",
description="Actual per-prompt text lengths used to build the transformer attention mask.",
),
OutputParam(
"negative_text_bth",
type_hint=torch.Tensor,
kwargs_type="denoiser_input_fields",
description="Padded negative text hidden states, when classifier-free guidance is enabled.",
),
OutputParam(
"negative_text_lens",
type_hint=torch.Tensor,
kwargs_type="denoiser_input_fields",
description="Actual per-prompt negative text lengths, when classifier-free guidance is enabled.",
),
]
@staticmethod
def _expand(hiddens: list[torch.Tensor], num_images_per_prompt: int) -> list[torch.Tensor]:
if num_images_per_prompt == 1:
return list(hiddens)
return [h for h in hiddens for _ in range(num_images_per_prompt)]
@torch.no_grad()
def __call__(self, components: ErnieImageModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
device = components._execution_device
dtype = components.transformer.dtype
text_in_dim = components.text_in_dim
num_images_per_prompt = block_state.num_images_per_prompt
prompt_embeds = block_state.prompt_embeds
block_state.batch_size = len(prompt_embeds)
prompt_embeds = self._expand(prompt_embeds, num_images_per_prompt)
text_bth, text_lens = _pad_text(prompt_embeds, device, dtype, text_in_dim)
block_state.text_bth = text_bth
block_state.text_lens = text_lens
negative_prompt_embeds = block_state.negative_prompt_embeds
if negative_prompt_embeds is not None:
negative_prompt_embeds = self._expand(negative_prompt_embeds, num_images_per_prompt)
negative_text_bth, negative_text_lens = _pad_text(negative_prompt_embeds, device, dtype, text_in_dim)
block_state.negative_text_bth = negative_text_bth
block_state.negative_text_lens = negative_text_lens
else:
block_state.negative_text_bth = None
block_state.negative_text_lens = None
self.set_block_state(state, block_state)
return components, state
class ErnieImageSetTimestepsStep(ModularPipelineBlocks):
model_name = "ernie-image"
@property
def description(self) -> str:
return "Step that sets the scheduler's timesteps for inference using a linear sigma schedule."
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("scheduler", FlowMatchEulerDiscreteScheduler)]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
"num_inference_steps",
type_hint=int,
default=50,
description="Number of denoising steps.",
),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam("timesteps", type_hint=torch.Tensor, description="The timesteps to use for inference."),
OutputParam("num_inference_steps", type_hint=int, description="The number of denoising steps."),
]
@torch.no_grad()
def __call__(self, components: ErnieImageModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
device = components._execution_device
num_inference_steps = block_state.num_inference_steps
sigmas = torch.linspace(1.0, 0.0, num_inference_steps + 1)[:-1]
components.scheduler.set_timesteps(sigmas=sigmas, device=device)
block_state.timesteps = components.scheduler.timesteps
block_state.num_inference_steps = num_inference_steps
self.set_block_state(state, block_state)
return components, state
class ErnieImagePrepareLatentsStep(ModularPipelineBlocks):
model_name = "ernie-image"
@property
def description(self) -> str:
return "Prepare random noise latents for the ErnieImage text-to-image denoising process."
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("transformer", ErnieImageTransformer2DModel)]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam("height", type_hint=int, description="The height in pixels of the generated image."),
InputParam("width", type_hint=int, description="The width in pixels of the generated image."),
InputParam(
"latents",
type_hint=torch.Tensor,
description="Pre-generated noisy latents. If provided, skips noise sampling.",
),
InputParam(
"generator",
type_hint=torch.Generator,
description="Torch generator for deterministic noise sampling.",
),
InputParam(
"text_bth",
required=True,
type_hint=torch.Tensor,
description="Padded text hidden states; used to derive the total batch size for the latents.",
),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam("latents", type_hint=torch.Tensor, description="The initial noise latents to denoise."),
OutputParam("height", type_hint=int, description="The resolved image height in pixels."),
OutputParam("width", type_hint=int, description="The resolved image width in pixels."),
]
@staticmethod
def _check_inputs(components: ErnieImageModularPipeline, height: int, width: int) -> None:
vae_scale_factor = components.vae_scale_factor
if height % vae_scale_factor != 0 or width % vae_scale_factor != 0:
raise ValueError(
f"`height` and `width` must be divisible by {vae_scale_factor}, got {height} and {width}."
)
@torch.no_grad()
def __call__(self, components: ErnieImageModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
device = components._execution_device
dtype = components.transformer.dtype
height = block_state.height or components.default_height
width = block_state.width or components.default_width
self._check_inputs(components, height, width)
total_batch_size = block_state.text_bth.shape[0]
latent_h = height // components.vae_scale_factor
latent_w = width // components.vae_scale_factor
num_channels_latents = components.num_channels_latents
shape = (total_batch_size, num_channels_latents, latent_h, latent_w)
if block_state.latents is None:
block_state.latents = randn_tensor(shape, generator=block_state.generator, device=device, dtype=dtype)
else:
block_state.latents = block_state.latents.to(device=device, dtype=dtype)
block_state.height = height
block_state.width = width
self.set_block_state(state, block_state)
return components, state
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