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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 | # 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 numpy as np
import PIL
import torch
from ...configuration_utils import FrozenDict
from ...image_processor import VaeImageProcessor
from ...models import AutoencoderKLQwenImage
from ..modular_pipeline import ModularPipelineBlocks, PipelineState
from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
from .modular_pipeline import AnimaModularPipeline
class AnimaVaeDecoderStep(ModularPipelineBlocks):
model_name = "anima"
@property
def description(self) -> str:
return "Step that decodes Anima latents into image tensors."
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("vae", AutoencoderKLQwenImage)]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam("latents", required=True, type_hint=torch.Tensor, description="Denoised Anima latents."),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [OutputParam.template("images", note="tensor output of the VAE decoder")]
@torch.no_grad()
def __call__(self, components: AnimaModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
latents = block_state.latents.to(components.vae.dtype)
latents_mean = (
torch.tensor(components.vae.config.latents_mean)
.view(1, components.vae.config.z_dim, 1, 1, 1)
.to(latents.device, latents.dtype)
)
latents_std = 1.0 / torch.tensor(components.vae.config.latents_std).view(
1, components.vae.config.z_dim, 1, 1, 1
).to(latents.device, latents.dtype)
latents = latents / latents_std + latents_mean
block_state.images = components.vae.decode(latents, return_dict=False)[0][:, :, 0]
self.set_block_state(state, block_state)
return components, state
class AnimaProcessImagesOutputStep(ModularPipelineBlocks):
model_name = "anima"
@property
def description(self) -> str:
return "Postprocess decoded Anima image tensors."
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec(
"image_processor",
VaeImageProcessor,
config=FrozenDict({"vae_scale_factor": 8}),
default_creation_method="from_config",
),
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam("images", required=True, type_hint=torch.Tensor, description="Decoded Anima image tensors."),
InputParam.template("output_type"),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
"images",
type_hint=list[PIL.Image.Image] | np.ndarray | torch.Tensor,
description="Generated images.",
)
]
@staticmethod
def check_inputs(output_type):
if output_type not in ["pil", "np", "pt"]:
raise ValueError(f"Invalid output_type: {output_type}")
@torch.no_grad()
def __call__(self, components: AnimaModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
self.check_inputs(block_state.output_type)
block_state.images = components.image_processor.postprocess(
image=block_state.images,
output_type=block_state.output_type,
)
self.set_block_state(state, block_state)
return components, state
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