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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 | # Copyright 2026 Ideogram 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 Qwen2Tokenizer, Qwen3VLModel
from transformers.masking_utils import create_causal_mask
from ...pipelines.ideogram4.prompt_enhancer import (
PROMPT_UPSAMPLE_TEMPERATURE,
Ideogram4PromptEnhancerHead,
build_caption_logits_processor,
build_prompt_enhancer,
generate_captions,
)
from ...utils import is_outlines_available, logging
from ..modular_pipeline import ModularPipelineBlocks, PipelineState
from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
from .modular_pipeline import Ideogram4ModularPipeline
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# Hidden states of these Qwen3-VL decoder layers are concatenated to form the per-token
# text conditioning consumed by the Ideogram4 transformer.
QWEN3_VL_ACTIVATION_LAYERS = (0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 35)
# auto_docstring
class Ideogram4PromptUpsampleStep(ModularPipelineBlocks):
"""
Optional step that rewrites the prompt(s) into Ideogram4's native structured JSON caption when
`prompt_upsampling=True` (the format the model is trained on). Requires a generative `text_encoder` (a
`Qwen3VLForConditionalGeneration`); install `outlines` for schema-constrained captions.
Components:
text_encoder (`Qwen3VLModel`): The Qwen3-VL text encoder. tokenizer (`Qwen2Tokenizer`): The tokenizer paired
with the text encoder. prompt_enhancer_head (`Ideogram4PromptEnhancerHead`): LM head grafted onto the text
encoder for prompt upsampling.
Inputs:
prompt (`str`):
The prompt or prompts to guide image generation.
prompt_upsampling (`bool`, *optional*, defaults to False):
If True, rewrite the prompt into Ideogram4's native JSON caption before encoding.
prompt_upsampling_temperature (`float`, *optional*, defaults to 1.0):
Sampling temperature for prompt upsampling.
height (`int`, *optional*):
The height in pixels of the generated image.
width (`int`, *optional*):
The width in pixels of the generated image.
max_sequence_length (`int`, *optional*, defaults to 2048):
Maximum sequence length for prompt encoding.
generator (`Generator`, *optional*):
Torch generator for deterministic generation.
Outputs:
prompt (`list`):
The (possibly upsampled) prompt forwarded to the text encoder.
"""
model_name = "ideogram4"
def __init__(self):
# Built lazily on first upsample: the head-less encoder body + `prompt_enhancer_head`, combined.
self._prompt_enhancer = None
# Outlines logits processor for schema-constrained captions; built lazily on first upsample.
self._caption_logits_processor = None
super().__init__()
@property
def description(self) -> str:
return (
"Optional step that rewrites the prompt(s) into Ideogram4's native structured JSON caption when "
"`prompt_upsampling=True` (the format the model is trained on). Requires a generative `text_encoder` "
"(a `Qwen3VLForConditionalGeneration`); install `outlines` for schema-constrained captions."
)
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("text_encoder", Qwen3VLModel, description="The Qwen3-VL text encoder."),
ComponentSpec("tokenizer", Qwen2Tokenizer, description="The tokenizer paired with the text encoder."),
ComponentSpec(
"prompt_enhancer_head",
Ideogram4PromptEnhancerHead,
description="LM head grafted onto the text encoder for prompt upsampling.",
),
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam.template("prompt", required=True),
InputParam(
name="prompt_upsampling",
type_hint=bool,
default=False,
description="If True, rewrite the prompt into Ideogram4's native JSON caption before encoding.",
),
InputParam(
name="prompt_upsampling_temperature",
type_hint=float,
default=PROMPT_UPSAMPLE_TEMPERATURE,
description="Sampling temperature for prompt upsampling.",
),
InputParam.template("height"),
InputParam.template("width"),
InputParam.template("max_sequence_length", default=2048),
InputParam.template("generator"),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
name="prompt",
type_hint=list,
description="The (possibly upsampled) prompt forwarded to the text encoder.",
),
]
@torch.no_grad()
def __call__(self, components: Ideogram4ModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
if block_state.prompt_upsampling:
if components.prompt_enhancer_head is None:
raise ValueError(
"Prompt upsampling requires the `prompt_enhancer_head` component, which is not loaded. Load an "
"`Ideogram4PromptEnhancerHead` and add it to the pipeline."
)
if self._prompt_enhancer is None:
self._prompt_enhancer = build_prompt_enhancer(components.text_encoder, components.prompt_enhancer_head)
if self._caption_logits_processor is None and is_outlines_available():
self._caption_logits_processor = build_caption_logits_processor(
self._prompt_enhancer, components.tokenizer
)
if self._caption_logits_processor is None:
logger.warning_once(
"`outlines` is not installed; prompt upsampling runs unconstrained and may not return "
"schema-valid JSON. Install with `pip install outlines` for structured captions."
)
height = block_state.height or components.default_height
width = block_state.width or components.default_width
block_state.prompt = generate_captions(
self._prompt_enhancer,
components.tokenizer,
self._caption_logits_processor,
block_state.prompt,
height,
width,
temperature=block_state.prompt_upsampling_temperature,
max_new_tokens=block_state.max_sequence_length,
generator=block_state.generator,
device=components._execution_device,
)
self.set_block_state(state, block_state)
return components, state
# auto_docstring
class Ideogram4TextEncoderStep(ModularPipelineBlocks):
"""
Text encoder step that tokenizes the prompt(s) and runs the Qwen3-VL text encoder, returning the per-token text
features (concatenated from a fixed set of activation layers). Only the text tokens are encoded; the packed image
tokens are appended later (the encoder is causal with image after text, so they never affect the text features).
Components:
text_encoder (`Qwen3VLModel`): The Qwen3-VL text encoder. tokenizer (`Qwen2Tokenizer`): 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 2048):
Maximum sequence length for prompt encoding.
Outputs:
text_features (`Tensor`):
Per-prompt text features (B, max_sequence_length, llm_features_dim), padding zeroed.
text_lengths (`list`):
Per-prompt real text-token counts, used to lay out the packed sequence.
"""
model_name = "ideogram4"
@property
def description(self) -> str:
return (
"Text encoder step that tokenizes the prompt(s) and runs the Qwen3-VL text encoder, returning the "
"per-token text features (concatenated from a fixed set of activation layers). Only the text tokens are "
"encoded; the packed image tokens are appended later (the encoder is causal with image after text, so "
"they never affect the text features)."
)
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("text_encoder", Qwen3VLModel, description="The Qwen3-VL text encoder."),
ComponentSpec("tokenizer", Qwen2Tokenizer, 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=2048),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
name="text_features",
type_hint=torch.Tensor,
description="Per-prompt text features (B, max_sequence_length, llm_features_dim), padding zeroed.",
),
OutputParam(
name="text_lengths",
type_hint=list,
description="Per-prompt real text-token counts, used to lay out the packed sequence.",
),
]
@staticmethod
# Copied from diffusers.pipelines.ideogram4.pipeline_ideogram4.Ideogram4Pipeline._get_text_encoder_hidden_states
def _get_text_encoder_hidden_states(
text_encoder,
token_ids: torch.Tensor,
attention_mask: torch.Tensor,
pos_2d: torch.Tensor,
) -> list[torch.Tensor]:
"""Run the text encoder's decoder layers, returning the hidden states tapped at each activation layer."""
language_model = text_encoder.language_model
inputs_embeds = language_model.embed_tokens(token_ids)
position_ids_4d = pos_2d[None, ...].expand(4, pos_2d.shape[0], -1)
text_position_ids = position_ids_4d[0]
mrope_position_ids = position_ids_4d[1:]
causal_mask = create_causal_mask(
config=language_model.config,
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
past_key_values=None,
position_ids=text_position_ids,
)
position_embeddings = language_model.rotary_emb(inputs_embeds, mrope_position_ids)
tap_set = set(QWEN3_VL_ACTIVATION_LAYERS)
captured: dict[int, torch.Tensor] = {}
hidden_states = inputs_embeds
for layer_idx, decoder_layer in enumerate(language_model.layers):
hidden_states = decoder_layer(
hidden_states,
attention_mask=causal_mask,
position_ids=text_position_ids,
past_key_values=None,
position_embeddings=position_embeddings,
)
if layer_idx in tap_set:
captured[layer_idx] = hidden_states
return [captured[i] for i in QWEN3_VL_ACTIVATION_LAYERS]
@torch.no_grad()
def __call__(self, components: Ideogram4ModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
device = components._execution_device
tokenizer = components.tokenizer
max_text_tokens = block_state.max_sequence_length
prompts = [block_state.prompt] if isinstance(block_state.prompt, str) else list(block_state.prompt)
batch_size = len(prompts)
# Tokenize each chat-formatted prompt and left-pad to `max_sequence_length`.
token_ids = torch.zeros(batch_size, max_text_tokens, dtype=torch.long)
attention_mask = torch.zeros(batch_size, max_text_tokens, dtype=torch.long)
text_position_ids = torch.zeros(batch_size, max_text_tokens, dtype=torch.long)
text_lengths = []
for b, text_prompt in enumerate(prompts):
messages = [{"role": "user", "content": [{"type": "text", "text": text_prompt}]}]
text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
toks = tokenizer(text, return_tensors="pt", add_special_tokens=False)["input_ids"][0]
n = int(toks.shape[0])
if n > max_text_tokens:
raise ValueError(f"prompt has {n} tokens, exceeds max_sequence_length={max_text_tokens}")
text_lengths.append(n)
offset = max_text_tokens - n
token_ids[b, offset:] = toks
attention_mask[b, offset:] = 1
text_position_ids[b, offset:] = torch.arange(n)
token_ids = token_ids.to(device)
attention_mask = attention_mask.to(device)
text_position_ids = text_position_ids.to(device)
# Run the text encoder, tapping the activation-layer hidden states, then concatenate them into per-token
# text features (padding zeroed).
selected = self._get_text_encoder_hidden_states(
components.text_encoder, token_ids, attention_mask, text_position_ids
)
text_features = torch.stack(selected, dim=0).permute(1, 2, 3, 0).reshape(batch_size, max_text_tokens, -1)
text_features = (text_features * attention_mask.to(text_features.dtype).unsqueeze(-1)).to(torch.float32)
block_state.text_features = text_features
block_state.text_lengths = text_lengths
self.set_block_state(state, block_state)
return components, state
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