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4684d79 | 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 | # Copyright 2026 Krea AI 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.
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.modular_pipelines.modular_pipeline import ModularPipeline
# Copied from diffusers.modular_pipelines.qwenimage.modular_pipeline.QwenImagePachifier with QwenImage->Krea2
class Krea2Pachifier(ConfigMixin):
"""
A class to pack and unpack latents for Krea2.
"""
config_name = "config.json"
@register_to_config
def __init__(self, patch_size: int = 2):
super().__init__()
def pack_latents(self, latents):
if latents.ndim != 4 and latents.ndim != 5:
raise ValueError(f"Latents must have 4 or 5 dimensions, but got {latents.ndim}")
if latents.ndim == 4:
latents = latents.unsqueeze(2)
batch_size, num_channels_latents, num_latent_frames, latent_height, latent_width = latents.shape
patch_size = self.config.patch_size
if latent_height % patch_size != 0 or latent_width % patch_size != 0:
raise ValueError(
f"Latent height and width must be divisible by {patch_size}, but got {latent_height} and {latent_width}"
)
latents = latents.view(
batch_size,
num_channels_latents,
latent_height // patch_size,
patch_size,
latent_width // patch_size,
patch_size,
)
latents = latents.permute(
0, 2, 4, 1, 3, 5
) # Batch_size, num_patches_height, num_patches_width, num_channels_latents, patch_size, patch_size
latents = latents.reshape(
batch_size,
(latent_height // patch_size) * (latent_width // patch_size),
num_channels_latents * patch_size * patch_size,
)
return latents
def unpack_latents(self, latents, height, width, vae_scale_factor=8):
if latents.ndim != 3:
raise ValueError(f"Latents must have 3 dimensions, but got {latents.ndim}")
batch_size, num_patches, channels = latents.shape
patch_size = self.config.patch_size
# VAE applies 8x compression on images but we must also account for packing which requires
# latent height and width to be divisible by 2.
height = patch_size * (int(height) // (vae_scale_factor * patch_size))
width = patch_size * (int(width) // (vae_scale_factor * patch_size))
latents = latents.view(
batch_size,
height // patch_size,
width // patch_size,
channels // (patch_size * patch_size),
patch_size,
patch_size,
)
latents = latents.permute(0, 3, 1, 4, 2, 5)
latents = latents.reshape(batch_size, channels // (patch_size * patch_size), 1, height, width)
return latents
class Krea2ModularPipeline(ModularPipeline):
"""
A ModularPipeline for Krea 2.
> [!WARNING] > This is an experimental feature and is likely to change in the future.
"""
default_blocks_name = "Krea2AutoBlocks"
@property
def default_height(self):
return self.default_sample_size * self.vae_scale_factor
@property
def default_width(self):
return self.default_sample_size * self.vae_scale_factor
@property
def default_sample_size(self):
return 128
@property
def vae_scale_factor(self):
vae_scale_factor = 8
if hasattr(self, "vae") and self.vae is not None:
vae_scale_factor = 2 ** len(self.vae.temperal_downsample)
return vae_scale_factor
@property
def num_channels_latents(self):
num_channels_latents = 16
if hasattr(self, "transformer") and self.transformer is not None:
num_channels_latents = self.transformer.config.in_channels // 4
return num_channels_latents
@property
def requires_unconditional_embeds(self):
requires_unconditional_embeds = False
if hasattr(self, "guider") and self.guider is not None:
requires_unconditional_embeds = self.guider._enabled and self.guider.num_conditions > 1
return requires_unconditional_embeds
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