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9e3b8ca | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 | # 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.
import torch
from transformers import AutoTokenizer, Qwen3VLModel
from ...configuration_utils import FrozenDict
from ...guiders import ClassifierFreeGuidance
from ...utils import logging
from ..modular_pipeline import ModularPipelineBlocks, PipelineState
from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
from .modular_pipeline import Krea2ModularPipeline
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# Indices into the Qwen3-VL `hidden_states` tuple (0 is the embedding output) whose states are stacked per token as the
# transformer's text conditioning. Must have `transformer.config.num_text_layers` entries.
KREA2_TEXT_ENCODER_SELECT_LAYERS = (2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35)
# Krea 2 wraps the prompt in this Qwen-Image chat template before encoding. The prompt is padded to a fixed length
# first and the assistant suffix is appended *after* the padding (matching how the model was sampled at training time);
# the first `_PROMPT_TEMPLATE_ENCODE_START_IDX` (system prefix) tokens are dropped from the encoder outputs.
_PROMPT_TEMPLATE_ENCODE_PREFIX = (
"<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, "
"spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n"
)
_PROMPT_TEMPLATE_ENCODE_SUFFIX = "<|im_end|>\n<|im_start|>assistant\n"
_PROMPT_TEMPLATE_ENCODE_START_IDX = 34
_PROMPT_TEMPLATE_ENCODE_NUM_SUFFIX_TOKENS = 5
# auto_docstring
class Krea2TextEncoderStep(ModularPipelineBlocks):
"""
Text encoder step that tokenizes the prompt(s) with the Krea 2 chat template, runs the Qwen3-VL text encoder, and
stacks a fixed set of decoder-layer hidden states per token as the transformer's text conditioning. The negative
prompt is encoded the same way when the guider enables CFG.
Components:
text_encoder (`Qwen3VLModel`): The Qwen3-VL text encoder. tokenizer (`AutoTokenizer`): The tokenizer paired
with the text encoder. guider (`ClassifierFreeGuidance`)
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.
Outputs:
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`):
Per-prompt negative text features (only when guidance is enabled).
negative_prompt_embeds_mask (`Tensor`):
Per-prompt negative text mask (only when guidance is enabled).
"""
model_name = "krea2"
@property
def description(self) -> str:
return (
"Text encoder step that tokenizes the prompt(s) with the Krea 2 chat template, runs the Qwen3-VL text "
"encoder, and stacks a fixed set of decoder-layer hidden states per token as the transformer's text "
"conditioning. The negative prompt is encoded the same way when the guider enables CFG."
)
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("text_encoder", Qwen3VLModel, description="The Qwen3-VL text encoder."),
ComponentSpec("tokenizer", AutoTokenizer, description="The tokenizer paired with the text encoder."),
ComponentSpec(
"guider",
ClassifierFreeGuidance,
config=FrozenDict({"guidance_scale": 4.5, "use_original_formulation": True}),
default_creation_method="from_config",
),
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam.template("prompt", required=True),
InputParam(name="negative_prompt", type_hint=str, description="The negative prompt(s) for CFG."),
InputParam.template("max_sequence_length", default=512),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
name="prompt_embeds",
type_hint=torch.Tensor,
description="Per-prompt stacked text features (B, text_seq_len, num_text_layers, text_hidden_dim).",
),
OutputParam(
name="prompt_embeds_mask",
type_hint=torch.Tensor,
description="Per-prompt boolean text mask (B, text_seq_len).",
),
OutputParam(
name="negative_prompt_embeds",
type_hint=torch.Tensor,
description="Per-prompt negative text features (only when guidance is enabled).",
),
OutputParam(
name="negative_prompt_embeds_mask",
type_hint=torch.Tensor,
description="Per-prompt negative text mask (only when guidance is enabled).",
),
]
def _encode_prompt(self, components, prompt, max_sequence_length, device):
"""Tokenize `prompt` into the fixed-length Krea 2 layout and tap the selected encoder hidden states.
Mirrors `Krea2Pipeline.get_text_hidden_states`. Returns a `(hidden_states, attention_mask)` tuple of shapes
`(batch_size, text_seq_len, num_text_layers, text_hidden_dim)` and `(batch_size, text_seq_len)` (bool).
"""
tokenizer = components.tokenizer
prompt = [prompt] if isinstance(prompt, str) else prompt
prefix_idx = _PROMPT_TEMPLATE_ENCODE_START_IDX
text = [_PROMPT_TEMPLATE_ENCODE_PREFIX + e for e in prompt]
text_tokens = tokenizer(
text,
truncation=True,
padding="max_length",
max_length=max_sequence_length + prefix_idx - _PROMPT_TEMPLATE_ENCODE_NUM_SUFFIX_TOKENS,
return_tensors="pt",
).to(device)
suffix_tokens = tokenizer([_PROMPT_TEMPLATE_ENCODE_SUFFIX] * len(text), return_tensors="pt").to(device)
input_ids = torch.cat([text_tokens.input_ids, suffix_tokens.input_ids], dim=1)
attention_mask = torch.cat([text_tokens.attention_mask, suffix_tokens.attention_mask], dim=1).bool()
# Krea 2 pads in the middle of the template (`[prefix | prompt | PAD | suffix]`), so the suffix tokens sit
# downstream of the padding. The text features must use positions that count only real tokens (padding does
# not consume a position) to match how the model was trained; otherwise the suffix gets a shifted mRoPE phase.
position_ids = (attention_mask.long().cumsum(dim=-1) - 1).clamp(min=0)
position_ids = position_ids.unsqueeze(0).expand(3, -1, -1)
outputs = components.text_encoder(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_hidden_states=True,
)
hidden_states = torch.stack([outputs.hidden_states[i] for i in KREA2_TEXT_ENCODER_SELECT_LAYERS], dim=2)
hidden_states = hidden_states[:, prefix_idx:]
attention_mask = attention_mask[:, prefix_idx:]
return hidden_states, attention_mask
@torch.no_grad()
def __call__(self, components: Krea2ModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
device = components._execution_device
prompts = [block_state.prompt] if isinstance(block_state.prompt, str) else list(block_state.prompt)
block_state.prompt_embeds, block_state.prompt_embeds_mask = self._encode_prompt(
components, prompts, block_state.max_sequence_length, device
)
block_state.negative_prompt_embeds = None
block_state.negative_prompt_embeds_mask = None
if components.requires_unconditional_embeds:
negative_prompt = block_state.negative_prompt
if negative_prompt is None:
negative_prompt = ""
if isinstance(negative_prompt, str):
negative_prompt = [negative_prompt] * len(prompts)
block_state.negative_prompt_embeds, block_state.negative_prompt_embeds_mask = self._encode_prompt(
components, negative_prompt, block_state.max_sequence_length, device
)
self.set_block_state(state, block_state)
return components, state
# auto_docstring
class Krea2TurboTextEncoderStep(Krea2TextEncoderStep):
"""
Text encoder step for the distilled Krea 2 turbo checkpoint that tokenizes the prompt(s) with the Krea 2 chat
template, runs the Qwen3-VL text encoder, and stacks a fixed set of decoder-layer hidden states per token as the
transformer's text conditioning. The distilled checkpoint runs without classifier-free guidance, so it takes no
negative prompt and has no guider.
Components:
text_encoder (`Qwen3VLModel`): The Qwen3-VL text encoder. tokenizer (`AutoTokenizer`): The tokenizer paired
with the text encoder.
Inputs:
prompt (`str`):
The prompt or prompts to guide image generation.
max_sequence_length (`int`, *optional*, defaults to 512):
Maximum sequence length for prompt encoding.
Outputs:
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).
"""
model_name = "krea2"
@property
def description(self) -> str:
return (
"Text encoder step for the distilled Krea 2 turbo checkpoint that tokenizes the prompt(s) with the Krea 2 "
"chat template, runs the Qwen3-VL text encoder, and stacks a fixed set of decoder-layer hidden states per "
"token as the transformer's text conditioning. The distilled checkpoint runs without classifier-free "
"guidance, so it takes no negative prompt and has no guider."
)
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("text_encoder", Qwen3VLModel, description="The Qwen3-VL text encoder."),
ComponentSpec("tokenizer", AutoTokenizer, description="The tokenizer paired with the text encoder."),
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam.template("prompt", required=True),
InputParam.template("max_sequence_length", default=512),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
name="prompt_embeds",
type_hint=torch.Tensor,
description="Per-prompt stacked text features (B, text_seq_len, num_text_layers, text_hidden_dim).",
),
OutputParam(
name="prompt_embeds_mask",
type_hint=torch.Tensor,
description="Per-prompt boolean text mask (B, text_seq_len).",
),
]
@torch.no_grad()
def __call__(self, components: Krea2ModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
device = components._execution_device
prompts = [block_state.prompt] if isinstance(block_state.prompt, str) else list(block_state.prompt)
block_state.prompt_embeds, block_state.prompt_embeds_mask = self._encode_prompt(
components, prompts, block_state.max_sequence_length, device
)
self.set_block_state(state, block_state)
return components, state
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