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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 | # 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 ...utils import logging
from ..modular_pipeline import SequentialPipelineBlocks
from ..modular_pipeline_utils import InsertableDict, OutputParam
from .before_denoise import (
Krea2PrepareLatentsStep,
Krea2PreparePositionIdsStep,
Krea2SetTimestepsStep,
Krea2TextInputsStep,
)
from .decoders import Krea2DecodeStep
from .denoise import Krea2DenoiseStep
from .encoders import Krea2TextEncoderStep
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
CORE_DENOISE_BLOCKS = InsertableDict(
[
("input", Krea2TextInputsStep()),
("prepare_latents", Krea2PrepareLatentsStep()),
("set_timesteps", Krea2SetTimestepsStep()),
("prepare_position_ids", Krea2PreparePositionIdsStep()),
("denoise", Krea2DenoiseStep()),
]
)
# auto_docstring
class Krea2CoreDenoiseStep(SequentialPipelineBlocks):
"""
Core denoising workflow for Krea 2 text-to-image: prepares the batch/latents/timesteps and the shared position ids,
then runs the symmetric-CFG denoising loop, producing the denoised packed latents for the decoder.
Components:
transformer (`Krea2Transformer2DModel`) scheduler (`FlowMatchEulerDiscreteScheduler`) guider
(`ClassifierFreeGuidance`)
Inputs:
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
prompt_embeds (`Tensor`):
Per-prompt stacked text features (B, text_seq_len, num_text_layers, text_hidden_dim).
prompt_embeds_mask (`Tensor`):
Per-prompt boolean text mask (B, text_seq_len).
negative_prompt_embeds (`Tensor`, *optional*):
Per-prompt negative text features.
negative_prompt_embeds_mask (`Tensor`, *optional*):
Per-prompt negative text mask.
latents (`Tensor`, *optional*):
Pre-generated noisy latents for image generation.
height (`int`, *optional*, defaults to 1024):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 1024):
The width in pixels of the generated image.
generator (`Generator`, *optional*):
Torch generator for deterministic generation.
num_inference_steps (`int`, *optional*, defaults to 28):
The number of denoising steps.
sigmas (`list`, *optional*):
Custom sigma schedule (defaults to a linear ramp).
attention_kwargs (`dict`, *optional*):
Additional kwargs for attention processors.
Outputs:
latents (`Tensor`):
The denoised packed latents (B, image_seq_len, in_channels).
"""
model_name = "krea2"
block_classes = list(CORE_DENOISE_BLOCKS.values())
block_names = list(CORE_DENOISE_BLOCKS.keys())
@property
def description(self) -> str:
return (
"Core denoising workflow for Krea 2 text-to-image: prepares the batch/latents/timesteps and the shared "
"position ids, then runs the symmetric-CFG denoising loop, producing the denoised packed latents for the "
"decoder."
)
@property
def outputs(self) -> list[OutputParam]:
return [
OutputParam.template("latents", description="The denoised packed latents (B, image_seq_len, in_channels).")
]
# auto_docstring
class Krea2AutoBlocks(SequentialPipelineBlocks):
"""
Auto Modular pipeline for text-to-image generation using Krea 2: encode text -> core denoise (symmetric CFG) ->
decode.
Supported workflows:
- `text2image`: requires `prompt`
Components:
text_encoder (`Qwen3VLModel`): The Qwen3-VL text encoder. tokenizer (`AutoTokenizer`): The tokenizer paired
with the text encoder. guider (`ClassifierFreeGuidance`) transformer (`Krea2Transformer2DModel`) scheduler
(`FlowMatchEulerDiscreteScheduler`) vae (`AutoencoderKLQwenImage`) image_processor (`VaeImageProcessor`)
Inputs:
prompt (`str`):
The prompt or prompts to guide image generation.
negative_prompt (`str`, *optional*):
The negative prompt(s) for CFG.
max_sequence_length (`int`, *optional*, defaults to 512):
Maximum sequence length for prompt encoding.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
latents (`Tensor`, *optional*):
Pre-generated noisy latents for image generation.
height (`int`, *optional*, defaults to 1024):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 1024):
The width in pixels of the generated image.
generator (`Generator`, *optional*):
Torch generator for deterministic generation.
num_inference_steps (`int`, *optional*, defaults to 28):
The number of denoising steps.
sigmas (`list`, *optional*):
Custom sigma schedule (defaults to a linear ramp).
attention_kwargs (`dict`, *optional*):
Additional kwargs for attention processors.
output_type (`str`, *optional*, defaults to pil):
Output format: 'pil', 'np', 'pt'.
Outputs:
images (`list`):
Generated images.
"""
model_name = "krea2"
block_classes = [
Krea2TextEncoderStep,
Krea2CoreDenoiseStep,
Krea2DecodeStep,
]
block_names = ["text_encoder", "denoise", "decode"]
_workflow_map = {
"text2image": {"prompt": True},
}
@property
def description(self) -> str:
return (
"Auto Modular pipeline for text-to-image generation using Krea 2: encode text -> core denoise "
"(symmetric CFG) -> decode."
)
@property
def outputs(self) -> list[OutputParam]:
return [OutputParam.template("images")]
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