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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 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 | # 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 torch
from ...configuration_utils import FrozenDict
from ...guiders import ClassifierFreeGuidance
from ...models.transformers import SD3Transformer2DModel
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 StableDiffusion3ModularPipeline
logger = logging.get_logger(__name__)
class StableDiffusion3LoopDenoiser(ModularPipelineBlocks):
model_name = "stable-diffusion-3"
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec(
"guider",
ClassifierFreeGuidance,
config=FrozenDict({"guidance_scale": 7.0}),
default_creation_method="from_config",
),
ComponentSpec("transformer", SD3Transformer2DModel),
]
@property
def description(self) -> str:
return "Step within the denoising loop that denoises the latents."
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
"joint_attention_kwargs",
type_hint=dict,
description="A kwargs dictionary passed along to the AttentionProcessor.",
),
InputParam(
"latents",
required=True,
type_hint=torch.Tensor,
description="The initial latents to use for the denoising process.",
),
InputParam(
"prompt_embeds",
required=True,
type_hint=torch.Tensor,
description="Text embeddings for guidance.",
),
InputParam(
"pooled_prompt_embeds",
required=True,
type_hint=torch.Tensor,
description="Pooled text embeddings for guidance.",
),
InputParam(
"negative_prompt_embeds",
type_hint=torch.Tensor,
description="Negative text embeddings for guidance.",
),
InputParam(
"negative_pooled_prompt_embeds",
type_hint=torch.Tensor,
description="Negative pooled text embeddings for guidance.",
),
InputParam(
"num_inference_steps",
type_hint=int,
description="The number of denoising steps.",
),
]
@torch.no_grad()
def __call__(
self,
components: StableDiffusion3ModularPipeline,
block_state: BlockState,
i: int,
t: torch.Tensor,
) -> PipelineState:
do_cfg = block_state.negative_prompt_embeds is not None
guider_inputs = {
"hidden_states": (block_state.latents, block_state.latents) if do_cfg else block_state.latents,
"encoder_hidden_states": (
block_state.prompt_embeds,
block_state.negative_prompt_embeds,
)
if do_cfg
else block_state.prompt_embeds,
"text_embeds": (
block_state.pooled_prompt_embeds,
block_state.negative_pooled_prompt_embeds,
)
if do_cfg
else block_state.pooled_prompt_embeds,
}
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)
latent_model_input = guider_state_batch.hidden_states
prompt_embeds = guider_state_batch.encoder_hidden_states
pooled_projections = getattr(guider_state_batch, "text_embeds", None)
timestep = t.expand(latent_model_input.shape[0])
guider_state_batch.noise_pred = components.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=prompt_embeds,
pooled_projections=pooled_projections,
joint_attention_kwargs=block_state.joint_attention_kwargs,
return_dict=False,
)[0]
components.guider.cleanup_models(components.transformer)
guider_output = components.guider(guider_state)
block_state.noise_pred = guider_output.pred
return components, block_state
class StableDiffusion3LoopAfterDenoiser(ModularPipelineBlocks):
model_name = "stable-diffusion-3"
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("scheduler", FlowMatchEulerDiscreteScheduler)]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
"latents",
type_hint=torch.Tensor,
description="The denoised latent tensors.",
)
]
@torch.no_grad()
def __call__(
self,
components: StableDiffusion3ModularPipeline,
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:
block_state.latents = block_state.latents.to(latents_dtype)
return components, block_state
class StableDiffusion3DenoiseLoopWrapper(LoopSequentialPipelineBlocks):
model_name = "stable-diffusion-3"
@property
def loop_expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("scheduler", FlowMatchEulerDiscreteScheduler),
ComponentSpec("transformer", SD3Transformer2DModel),
]
@property
def loop_inputs(self) -> list[InputParam]:
return [
InputParam("timesteps", required=True, type_hint=torch.Tensor),
InputParam("num_inference_steps", required=True, type_hint=int),
]
@torch.no_grad()
def __call__(self, components: StableDiffusion3ModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
block_state.num_warmup_steps = max(
len(block_state.timesteps) - block_state.num_inference_steps * components.scheduler.order,
0,
)
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)
if i == len(block_state.timesteps) - 1 or (
(i + 1) > block_state.num_warmup_steps and (i + 1) % components.scheduler.order == 0
):
progress_bar.update()
self.set_block_state(state, block_state)
return components, state
class StableDiffusion3DenoiseStep(StableDiffusion3DenoiseLoopWrapper):
block_classes = [StableDiffusion3LoopDenoiser, StableDiffusion3LoopAfterDenoiser]
block_names = ["denoiser", "after_denoiser"]
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