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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.
from typing import Any
import torch
from ...models import FluxTransformer2DModel
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 FluxModularPipeline
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class FluxLoopDenoiser(ModularPipelineBlocks):
model_name = "flux"
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("transformer", FluxTransformer2DModel)]
@property
def description(self) -> str:
return (
"Step within the denoising loop that denoise the latents. "
"This block should be used to compose the `sub_blocks` attribute of a `LoopSequentialPipelineBlocks` "
"object (e.g. `FluxDenoiseLoopWrapper`)"
)
@property
def inputs(self) -> list[tuple[str, Any]]:
return [
InputParam("joint_attention_kwargs"),
InputParam(
"latents",
required=True,
type_hint=torch.Tensor,
description="The initial latents to use for the denoising process. Can be generated in prepare_latent step.",
),
InputParam(
"guidance",
required=False,
type_hint=torch.Tensor,
description="Guidance scale as a tensor",
),
InputParam(
"prompt_embeds",
required=True,
type_hint=torch.Tensor,
description="Prompt embeddings",
),
InputParam(
"pooled_prompt_embeds",
required=True,
type_hint=torch.Tensor,
description="Pooled prompt embeddings",
),
InputParam(
"txt_ids",
required=True,
type_hint=torch.Tensor,
description="IDs computed from text sequence needed for RoPE",
),
InputParam(
"img_ids",
required=True,
type_hint=torch.Tensor,
description="IDs computed from image sequence needed for RoPE",
),
]
@torch.no_grad()
def __call__(
self, components: FluxModularPipeline, block_state: BlockState, i: int, t: torch.Tensor
) -> PipelineState:
noise_pred = components.transformer(
hidden_states=block_state.latents,
timestep=t.flatten() / 1000,
guidance=block_state.guidance,
encoder_hidden_states=block_state.prompt_embeds,
pooled_projections=block_state.pooled_prompt_embeds,
joint_attention_kwargs=block_state.joint_attention_kwargs,
txt_ids=block_state.txt_ids,
img_ids=block_state.img_ids,
return_dict=False,
)[0]
block_state.noise_pred = noise_pred
return components, block_state
class FluxKontextLoopDenoiser(ModularPipelineBlocks):
model_name = "flux-kontext"
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("transformer", FluxTransformer2DModel)]
@property
def description(self) -> str:
return (
"Step within the denoising loop that denoise the latents for Flux Kontext. "
"This block should be used to compose the `sub_blocks` attribute of a `LoopSequentialPipelineBlocks` "
"object (e.g. `FluxDenoiseLoopWrapper`)"
)
@property
def inputs(self) -> list[tuple[str, Any]]:
return [
InputParam("joint_attention_kwargs"),
InputParam(
"latents",
required=True,
type_hint=torch.Tensor,
description="The initial latents to use for the denoising process. Can be generated in prepare_latent step.",
),
InputParam(
"image_latents",
type_hint=torch.Tensor,
description="Image latents to use for the denoising process. Can be generated in prepare_latent step.",
),
InputParam(
"guidance",
required=False,
type_hint=torch.Tensor,
description="Guidance scale as a tensor",
),
InputParam(
"prompt_embeds",
required=True,
type_hint=torch.Tensor,
description="Prompt embeddings",
),
InputParam(
"pooled_prompt_embeds",
required=True,
type_hint=torch.Tensor,
description="Pooled prompt embeddings",
),
InputParam(
"txt_ids",
required=True,
type_hint=torch.Tensor,
description="IDs computed from text sequence needed for RoPE",
),
InputParam(
"img_ids",
required=True,
type_hint=torch.Tensor,
description="IDs computed from latent sequence needed for RoPE",
),
]
@torch.no_grad()
def __call__(
self, components: FluxModularPipeline, block_state: BlockState, i: int, t: torch.Tensor
) -> PipelineState:
latents = block_state.latents
latent_model_input = latents
image_latents = block_state.image_latents
if image_latents is not None:
latent_model_input = torch.cat([latent_model_input, image_latents], dim=1)
timestep = t.expand(latents.shape[0]).to(latents.dtype)
noise_pred = components.transformer(
hidden_states=latent_model_input,
timestep=timestep / 1000,
guidance=block_state.guidance,
encoder_hidden_states=block_state.prompt_embeds,
pooled_projections=block_state.pooled_prompt_embeds,
joint_attention_kwargs=block_state.joint_attention_kwargs,
txt_ids=block_state.txt_ids,
img_ids=block_state.img_ids,
return_dict=False,
)[0]
noise_pred = noise_pred[:, : latents.size(1)]
block_state.noise_pred = noise_pred
return components, block_state
class FluxLoopAfterDenoiser(ModularPipelineBlocks):
model_name = "flux"
@property
def expected_components(self) -> list[ComponentSpec]:
return [ComponentSpec("scheduler", FlowMatchEulerDiscreteScheduler)]
@property
def description(self) -> str:
return (
"step within the denoising loop that update the latents. "
"This block should be used to compose the `sub_blocks` attribute of a `LoopSequentialPipelineBlocks` "
"object (e.g. `FluxDenoiseLoopWrapper`)"
)
@property
def inputs(self) -> list[tuple[str, Any]]:
return []
@property
def intermediate_inputs(self) -> list[str]:
return [InputParam("generator")]
@property
def intermediate_outputs(self) -> list[OutputParam]:
return [OutputParam("latents", type_hint=torch.Tensor, description="The denoised latents")]
@torch.no_grad()
def __call__(self, components: FluxModularPipeline, block_state: BlockState, i: int, t: torch.Tensor):
# Perform scheduler step using the predicted output
latents_dtype = block_state.latents.dtype
block_state.latents = components.scheduler.step(
block_state.noise_pred,
t,
block_state.latents,
return_dict=False,
)[0]
if block_state.latents.dtype != latents_dtype:
block_state.latents = block_state.latents.to(latents_dtype)
return components, block_state
class FluxDenoiseLoopWrapper(LoopSequentialPipelineBlocks):
model_name = "flux"
@property
def description(self) -> str:
return (
"Pipeline block that iteratively denoise the latents over `timesteps`. "
"The specific steps with each iteration can be customized with `sub_blocks` attributes"
)
@property
def loop_expected_components(self) -> list[ComponentSpec]:
return [
ComponentSpec("scheduler", FlowMatchEulerDiscreteScheduler),
ComponentSpec("transformer", FluxTransformer2DModel),
]
@property
def loop_inputs(self) -> list[InputParam]:
return [
InputParam(
"timesteps",
required=True,
type_hint=torch.Tensor,
description="The timesteps to use for the denoising process. Can be generated in set_timesteps step.",
),
InputParam(
"num_inference_steps",
required=True,
type_hint=int,
description="The number of inference steps to use for the denoising process. Can be generated in set_timesteps step.",
),
]
@torch.no_grad()
def __call__(self, components: FluxModularPipeline, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
block_state.num_warmup_steps = max(
len(block_state.timesteps) - block_state.num_inference_steps * components.scheduler.order, 0
)
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)
if i == len(block_state.timesteps) - 1 or (
(i + 1) > block_state.num_warmup_steps and (i + 1) % components.scheduler.order == 0
):
progress_bar.update()
self.set_block_state(state, block_state)
return components, state
class FluxDenoiseStep(FluxDenoiseLoopWrapper):
block_classes = [FluxLoopDenoiser, FluxLoopAfterDenoiser]
block_names = ["denoiser", "after_denoiser"]
@property
def description(self) -> str:
return (
"Denoise step that iteratively denoise the latents. \n"
"Its loop logic is defined in `FluxDenoiseLoopWrapper.__call__` method \n"
"At each iteration, it runs blocks defined in `sub_blocks` sequentially:\n"
" - `FluxLoopDenoiser`\n"
" - `FluxLoopAfterDenoiser`\n"
"This block supports both text2image and img2img tasks."
)
class FluxKontextDenoiseStep(FluxDenoiseLoopWrapper):
model_name = "flux-kontext"
block_classes = [FluxKontextLoopDenoiser, FluxLoopAfterDenoiser]
block_names = ["denoiser", "after_denoiser"]
@property
def description(self) -> str:
return (
"Denoise step that iteratively denoise the latents. \n"
"Its loop logic is defined in `FluxDenoiseLoopWrapper.__call__` method \n"
"At each iteration, it runs blocks defined in `sub_blocks` sequentially:\n"
" - `FluxKontextLoopDenoiser`\n"
" - `FluxLoopAfterDenoiser`\n"
"This block supports both text2image and img2img tasks."
)
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