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#
# 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 ...models.transformers.transformer_ideogram4 import Ideogram4Transformer2DModel
from ...schedulers import FlowMatchEulerDiscreteScheduler
from ...utils import logging
from ..modular_pipeline import (
BlockState,
LoopSequentialPipelineBlocks,
ModularPipelineBlocks,
PipelineState,
)
from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
from .modular_pipeline import Ideogram4ModularPipeline
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class Ideogram4LoopBeforeDenoiser(ModularPipelineBlocks):
model_name = "ideogram4"
@property
def description(self) -> str:
return (
"Within the denoising loop: build the conditional packed input `[text-padding][image latents]` and the "
"model timestep. Compose into the `sub_blocks` of `Ideogram4DenoiseLoopWrapper`."
)
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("scheduler", FlowMatchEulerDiscreteScheduler)]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(name="latents", required=True, type_hint=torch.Tensor, description="Packed image latents."),
InputParam(
name="position_ids", required=True, type_hint=torch.Tensor, description="Conditional position ids."
),
InputParam(name="batch_size", required=True, type_hint=int, description="Effective batch size."),
]
@torch.no_grad()
def __call__(self, components: Ideogram4ModularPipeline, block_state: BlockState, i: int, t: torch.Tensor):
# Conditional packed sequence is [text-padding][image latents]; text region length = total - image tokens.
max_text_tokens = block_state.position_ids.shape[1] - block_state.latents.shape[1]
text_z_padding = torch.zeros(
block_state.latents.shape[0],
max_text_tokens,
block_state.latents.shape[-1],
dtype=block_state.latents.dtype,
device=block_state.latents.device,
)
block_state.pos_z = torch.cat([text_z_padding, block_state.latents], dim=1)
block_state.max_text_tokens = max_text_tokens
# Map sigma-domain timestep to model time t in [0, 1] (0 = noise, 1 = clean data).
num_train_timesteps = components.scheduler.config.num_train_timesteps
t_model = 1.0 - (t.float() / num_train_timesteps)
block_state.t_model = t_model.expand(block_state.batch_size)
return components, block_state
class Ideogram4LoopDenoiser(ModularPipelineBlocks):
model_name = "ideogram4"
@property
def description(self) -> str:
return (
"Within the denoising loop: run the conditional `transformer` on the full packed sequence and the "
"`unconditional_transformer` on the image-only sequence, then blend with the per-step guidance weight "
"(asymmetric CFG, no guider). Compose into `Ideogram4DenoiseLoopWrapper`."
)
@property
def expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("transformer", Ideogram4Transformer2DModel),
ComponentSpec("unconditional_transformer", Ideogram4Transformer2DModel),
]
@property
def inputs(self) -> list[InputParam]:
return [
InputParam(
name="prompt_embeds",
required=True,
type_hint=torch.Tensor,
description="Packed conditional encoder_hidden_states.",
),
InputParam(
name="position_ids",
required=True,
type_hint=torch.Tensor,
description="Conditional 3-axis MRoPE position ids.",
),
InputParam(
name="segment_ids",
required=True,
type_hint=torch.Tensor,
description="Conditional block-diagonal segment ids.",
),
InputParam(
name="indicator",
required=True,
type_hint=torch.Tensor,
description="Conditional per-token text/image/pad role.",
),
InputParam(
name="negative_prompt_embeds",
required=True,
type_hint=torch.Tensor,
description="Unconditional (zeroed) text features.",
),
InputParam(
name="negative_position_ids",
required=True,
type_hint=torch.Tensor,
description="Unconditional position ids (image region).",
),
InputParam(
name="negative_segment_ids",
required=True,
type_hint=torch.Tensor,
description="Unconditional segment ids (image region).",
),
InputParam(
name="negative_indicator",
required=True,
type_hint=torch.Tensor,
description="Unconditional indicator (image region).",
),
InputParam(name="gw", required=True, type_hint=torch.Tensor, description="Per-step guidance weights."),
InputParam(name="latents", required=True, type_hint=torch.Tensor, description="Packed image latents."),
]
@torch.no_grad()
def __call__(self, components: Ideogram4ModularPipeline, block_state: BlockState, i: int, t: torch.Tensor):
transformer = components.transformer
unconditional_transformer = components.unconditional_transformer
# Conditional pass operates on the full packed sequence; the velocity is the image-token region.
pos_out = transformer(
hidden_states=block_state.pos_z.to(transformer.dtype),
timestep=block_state.t_model.to(transformer.dtype),
encoder_hidden_states=block_state.prompt_embeds.to(transformer.dtype),
position_ids=block_state.position_ids,
segment_ids=block_state.segment_ids,
indicator=block_state.indicator,
return_dict=False,
)[0]
pos_v = pos_out[:, block_state.max_text_tokens :].to(torch.float32)
# Unconditional pass uses the image-only positions with zeroed text features.
neg_v = unconditional_transformer(
hidden_states=block_state.latents.to(unconditional_transformer.dtype),
timestep=block_state.t_model.to(unconditional_transformer.dtype),
encoder_hidden_states=block_state.negative_prompt_embeds.to(unconditional_transformer.dtype),
position_ids=block_state.negative_position_ids,
segment_ids=block_state.negative_segment_ids,
indicator=block_state.negative_indicator,
return_dict=False,
)[0].to(torch.float32)
gw_i = block_state.gw[i]
v = gw_i * pos_v + (1.0 - gw_i) * neg_v
# The scheduler integrates `-v` (Ideogram predicts velocity v = x0 - noise).
block_state.noise_pred = -v
return components, block_state
class Ideogram4LoopAfterDenoiser(ModularPipelineBlocks):
model_name = "ideogram4"
@property
def description(self) -> str:
return "Within the denoising loop: scheduler step. Compose into `Ideogram4DenoiseLoopWrapper`."
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("scheduler", FlowMatchEulerDiscreteScheduler)]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [OutputParam(name="latents", type_hint=torch.Tensor, description="The denoised latents.")]
@torch.no_grad()
def __call__(self, components: Ideogram4ModularPipeline, block_state: BlockState, i: int, t: torch.Tensor):
block_state.latents = components.scheduler.step(
block_state.noise_pred, t, block_state.latents, return_dict=False
)[0]
return components, block_state
# auto_docstring
class Ideogram4DenoiseStep(LoopSequentialPipelineBlocks):
"""
Denoising loop that iteratively denoises the packed image latents over `timesteps`, running both the conditional
and unconditional transformers and blending with the per-step guidance schedule.
Components:
scheduler (`FlowMatchEulerDiscreteScheduler`) transformer (`Ideogram4Transformer2DModel`)
unconditional_transformer (`Ideogram4Transformer2DModel`)
Inputs:
timesteps (`Tensor`):
Denoising timesteps from set_timesteps.
num_inference_steps (`int`, *optional*, defaults to 48):
The number of denoising steps.
latents (`Tensor`):
Packed image latents.
position_ids (`Tensor`):
Conditional position ids.
batch_size (`int`):
Effective batch size.
prompt_embeds (`Tensor`):
Packed conditional encoder_hidden_states.
position_ids (`Tensor`):
Conditional 3-axis MRoPE position ids.
segment_ids (`Tensor`):
Conditional block-diagonal segment ids.
indicator (`Tensor`):
Conditional per-token text/image/pad role.
negative_prompt_embeds (`Tensor`):
Unconditional (zeroed) text features.
negative_position_ids (`Tensor`):
Unconditional position ids (image region).
negative_segment_ids (`Tensor`):
Unconditional segment ids (image region).
negative_indicator (`Tensor`):
Unconditional indicator (image region).
gw (`Tensor`):
Per-step guidance weights.
Outputs:
latents (`Tensor`):
The denoised latents.
"""
model_name = "ideogram4"
block_classes = [Ideogram4LoopBeforeDenoiser, Ideogram4LoopDenoiser, Ideogram4LoopAfterDenoiser]
block_names = ["before_denoiser", "denoiser", "after_denoiser"]
@property
def description(self) -> str:
return (
"Denoising loop that iteratively denoises the packed image latents over `timesteps`, running both the "
"conditional and unconditional transformers and blending with the per-step guidance schedule."
)
@property
def loop_expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("scheduler", FlowMatchEulerDiscreteScheduler)]
@property
def loop_inputs(self) -> list[InputParam]:
return [
InputParam(
name="timesteps",
required=True,
type_hint=torch.Tensor,
description="Denoising timesteps from set_timesteps.",
),
InputParam.template("num_inference_steps", default=48),
]
@torch.no_grad()
def __call__(self, components: Ideogram4ModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
with self.progress_bar(total=block_state.num_inference_steps) as progress_bar:
for i, t in enumerate(block_state.timesteps):
components, block_state = self.loop_step(components, block_state, i=i, t=t)
progress_bar.update()
self.set_block_state(state, block_state)
return components, state
# auto_docstring
class Ideogram4AfterDenoiseStep(ModularPipelineBlocks):
"""
Step that runs after the denoising loop: unpatchifies the packed image latents (B, num_image_tokens, ae_channels *
patch ** 2) into a (B, ae_channels, H, W) latent for the decoder.
Inputs:
height (`int`):
The height in pixels of the generated image.
width (`int`):
The width in pixels of the generated image.
latents (`Tensor`):
The denoised packed image latents (B, num_image_tokens, latent_dim).
Outputs:
latents (`Tensor`):
Unpatchified latents (B, ae_channels, H, W) ready for the VAE decoder.
"""
model_name = "ideogram4"
@property
def description(self) -> str:
return (
"Step that runs after the denoising loop: unpatchifies the packed image latents "
"(B, num_image_tokens, ae_channels * patch ** 2) into a (B, ae_channels, H, W) latent for the decoder."
)
@property
def inputs(self) -> list[InputParam]:
return [
InputParam.template("height", required=True),
InputParam.template("width", required=True),
InputParam(
name="latents",
required=True,
type_hint=torch.Tensor,
description="The denoised packed image latents (B, num_image_tokens, latent_dim).",
),
]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [
OutputParam(
name="latents",
type_hint=torch.Tensor,
description="Unpatchified latents (B, ae_channels, H, W) ready for the VAE decoder.",
)
]
@torch.no_grad()
def __call__(self, components: Ideogram4ModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
z = block_state.latents
patch = components.patch_size
grid_h = block_state.height // (components.vae_scale_factor * patch)
grid_w = block_state.width // (components.vae_scale_factor * patch)
ae_channels = z.shape[-1] // (patch * patch)
z = z.view(z.shape[0], grid_h, grid_w, patch, patch, ae_channels)
z = z.permute(0, 5, 1, 3, 2, 4).contiguous()
z = z.view(z.shape[0], ae_channels, grid_h * patch, grid_w * patch)
block_state.latents = z
self.set_block_state(state, block_state)
return components, state
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