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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 | # Copyright 2025 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 __future__ import annotations
from typing import Any, Union
import numpy as np
import PIL
import torch
from ...configuration_utils import FrozenDict
from ...models import AutoencoderKLFlux2
from ...pipelines.flux2.image_processor import Flux2ImageProcessor
from ...utils import logging
from ..modular_pipeline import ModularPipelineBlocks, PipelineState
from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class Flux2UnpackLatentsStep(ModularPipelineBlocks):
model_name = "flux2"
@property
def description(self) -> str:
return "Step that unpacks the latents from the denoising step"
@property
def inputs(self) -> list[tuple[str, Any]]:
return [
InputParam(
"latents",
required=True,
type_hint=torch.Tensor,
description="The denoised latents from the denoising step",
),
InputParam(
"latent_ids",
required=True,
type_hint=torch.Tensor,
description="Position IDs for the latents, used for unpacking",
),
]
@property
def intermediate_outputs(self) -> list[str]:
return [
OutputParam(
"latents",
type_hint=torch.Tensor,
description="The denoise latents from denoising step, unpacked with position IDs.",
)
]
@staticmethod
def _unpack_latents_with_ids(x: torch.Tensor, x_ids: torch.Tensor) -> torch.Tensor:
"""
Unpack latents using position IDs to scatter tokens into place.
Args:
x: Packed latents tensor of shape (B, seq_len, C)
x_ids: Position IDs tensor of shape (B, seq_len, 4) with (T, H, W, L) coordinates
Returns:
Unpacked latents tensor of shape (B, C, H, W)
"""
x_list = []
for data, pos in zip(x, x_ids):
_, ch = data.shape # noqa: F841
h_ids = pos[:, 1].to(torch.int64)
w_ids = pos[:, 2].to(torch.int64)
h = torch.max(h_ids) + 1
w = torch.max(w_ids) + 1
flat_ids = h_ids * w + w_ids
out = torch.zeros((h * w, ch), device=data.device, dtype=data.dtype)
out.scatter_(0, flat_ids.unsqueeze(1).expand(-1, ch), data)
out = out.view(h, w, ch).permute(2, 0, 1)
x_list.append(out)
return torch.stack(x_list, dim=0)
@torch.no_grad()
def __call__(self, components, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
latents = block_state.latents
latent_ids = block_state.latent_ids
latents = self._unpack_latents_with_ids(latents, latent_ids)
block_state.latents = latents
self.set_block_state(state, block_state)
return components, state
class Flux2DecodeStep(ModularPipelineBlocks):
model_name = "flux2"
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("vae", AutoencoderKLFlux2),
ComponentSpec(
"image_processor",
Flux2ImageProcessor,
config=FrozenDict({"vae_scale_factor": 16, "vae_latent_channels": 32}),
default_creation_method="from_config",
),
]
@property
def description(self) -> str:
return "Step that decodes the denoised latents into images using Flux2 VAE with batch norm denormalization"
@property
def inputs(self) -> list[tuple[str, Any]]:
return [
InputParam("output_type", default="pil"),
InputParam(
"latents",
required=True,
type_hint=torch.Tensor,
description="The denoised latents from the denoising step",
),
]
@property
def intermediate_outputs(self) -> list[str]:
return [
OutputParam(
"images",
type_hint=Union[list[PIL.Image.Image], torch.Tensor, np.ndarray],
description="The generated images, can be a list of PIL.Image.Image, torch.Tensor or a numpy array",
)
]
@staticmethod
def _unpatchify_latents(latents):
"""Convert patchified latents back to regular format."""
batch_size, num_channels_latents, height, width = latents.shape
latents = latents.reshape(batch_size, num_channels_latents // (2 * 2), 2, 2, height, width)
latents = latents.permute(0, 1, 4, 2, 5, 3)
latents = latents.reshape(batch_size, num_channels_latents // (2 * 2), height * 2, width * 2)
return latents
@torch.no_grad()
def __call__(self, components, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
vae = components.vae
latents = block_state.latents
latents_bn_mean = vae.bn.running_mean.view(1, -1, 1, 1).to(latents.device, latents.dtype)
latents_bn_std = torch.sqrt(vae.bn.running_var.view(1, -1, 1, 1) + vae.config.batch_norm_eps).to(
latents.device, latents.dtype
)
latents = latents * latents_bn_std + latents_bn_mean
latents = self._unpatchify_latents(latents)
block_state.images = vae.decode(latents, return_dict=False)[0]
block_state.images = components.image_processor.postprocess(
block_state.images, output_type=block_state.output_type
)
self.set_block_state(state, block_state)
return components, state
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