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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 | # 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.
from typing import Any
import torch
from ...configuration_utils import FrozenDict
from ...guiders import ClassifierFreeGuidance
from ...models import CosmosTransformer3DModel
from ...schedulers import FlowMatchEulerDiscreteScheduler
from ..modular_pipeline import BlockState, LoopSequentialPipelineBlocks, ModularPipelineBlocks, PipelineState
from ..modular_pipeline_utils import ComponentSpec, InputParam
from .modular_pipeline import AnimaModularPipeline
class AnimaLoopBeforeDenoiser(ModularPipelineBlocks):
model_name = "anima"
@property
def description(self) -> str:
return "Step within the denoising loop that prepares Anima latent and timestep inputs."
@property
def inputs(self) -> list[InputParam]:
return [
InputParam("latents", required=True, type_hint=torch.Tensor, description="Current Anima latents."),
InputParam("dtype", required=True, type_hint=torch.dtype, description="Dtype used by the Anima denoiser."),
]
@torch.no_grad()
def __call__(self, components: AnimaModularPipeline, block_state: BlockState, i: int, t: torch.Tensor):
block_state.latent_model_input = block_state.latents.to(block_state.dtype)
timestep = t.expand(block_state.latents.shape[0]).to(block_state.dtype)
block_state.timestep = timestep / components.scheduler.config.num_train_timesteps
return components, block_state
class AnimaLoopDenoiser(ModularPipelineBlocks):
model_name = "anima"
def __init__(
self,
guider_input_fields: dict[str, Any] | None = None,
):
if guider_input_fields is None:
guider_input_fields = {"encoder_hidden_states": ("prompt_embeds", "negative_prompt_embeds")}
if not isinstance(guider_input_fields, dict):
raise ValueError(f"`guider_input_fields` must be a dictionary but is {type(guider_input_fields)}")
self._guider_input_fields = guider_input_fields
super().__init__()
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec(
"guider",
ClassifierFreeGuidance,
config=FrozenDict({"guidance_scale": 4.0}),
default_creation_method="from_config",
),
ComponentSpec("transformer", CosmosTransformer3DModel),
]
@property
def description(self) -> str:
return "Step within the denoising loop that predicts Anima noise with guidance."
@property
def inputs(self) -> list[InputParam]:
inputs = [
InputParam(
"num_inference_steps",
required=True,
type_hint=int,
description="Number of denoising steps.",
),
InputParam(
"padding_mask",
required=True,
type_hint=torch.Tensor,
description="Cosmos padding mask for image latents.",
),
InputParam(
kwargs_type="denoiser_input_fields",
description="The conditional model inputs for the Anima denoiser.",
),
]
guider_input_names = []
uncond_guider_input_names = []
for value in self._guider_input_fields.values():
if isinstance(value, tuple):
guider_input_names.append(value[0])
uncond_guider_input_names.append(value[1])
else:
guider_input_names.append(value)
for name in guider_input_names:
inputs.append(InputParam(name=name, required=True))
for name in uncond_guider_input_names:
inputs.append(InputParam(name=name))
return inputs
@torch.no_grad()
def __call__(
self, components: AnimaModularPipeline, block_state: BlockState, i: int, t: torch.Tensor
) -> PipelineState:
components.guider.set_state(step=i, num_inference_steps=block_state.num_inference_steps, timestep=t)
guider_state = components.guider.prepare_inputs_from_block_state(block_state, self._guider_input_fields)
for guider_state_batch in guider_state:
components.guider.prepare_models(components.transformer)
cond_kwargs = {
key: getattr(guider_state_batch, key).to(block_state.dtype) for key in self._guider_input_fields.keys()
}
guider_state_batch.noise_pred = components.transformer(
hidden_states=block_state.latent_model_input,
timestep=block_state.timestep,
padding_mask=block_state.padding_mask,
return_dict=False,
**cond_kwargs,
)[0]
components.guider.cleanup_models(components.transformer)
block_state.noise_pred = components.guider(guider_state)[0]
return components, block_state
class AnimaLoopAfterDenoiser(ModularPipelineBlocks):
model_name = "anima"
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("scheduler", FlowMatchEulerDiscreteScheduler)]
@property
def description(self) -> str:
return "Step within the denoising loop that updates Anima latents."
@torch.no_grad()
def __call__(self, components: AnimaModularPipeline, 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 AnimaDenoiseLoopWrapper(LoopSequentialPipelineBlocks):
model_name = "anima"
@property
def description(self) -> str:
return "Pipeline block that iteratively denoises Anima latents over scheduler timesteps."
@property
def loop_expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("scheduler", FlowMatchEulerDiscreteScheduler)]
@property
def loop_inputs(self) -> list[InputParam]:
return [
InputParam("timesteps", required=True, type_hint=torch.Tensor, description="Timesteps to denoise over."),
InputParam("num_inference_steps", required=True, type_hint=int, description="Number of denoising steps."),
]
@torch.no_grad()
def __call__(self, components: AnimaModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
num_warmup_steps = len(block_state.timesteps) - block_state.num_inference_steps * components.scheduler.order
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) > num_warmup_steps and (i + 1) % components.scheduler.order == 0
):
progress_bar.update()
self.set_block_state(state, block_state)
return components, state
class AnimaDenoiseStep(AnimaDenoiseLoopWrapper):
block_classes = [
AnimaLoopBeforeDenoiser,
AnimaLoopDenoiser(guider_input_fields={"encoder_hidden_states": ("prompt_embeds", "negative_prompt_embeds")}),
AnimaLoopAfterDenoiser,
]
block_names = ["before_denoiser", "denoiser", "after_denoiser"]
@property
def description(self) -> str:
return "Denoise step that iteratively denoises image latents for Anima."
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