InvokeAI/invokeai/backend/stable_diffusion/diffusers_pipeline.py

from __future__ import annotations

import math
from contextlib import nullcontext
from dataclasses import dataclass
from typing import Any, Callable, List, Optional, Union

import einops
import PIL.Image
import psutil
import torch
import torchvision.transforms as T
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.models.controlnet import ControlNetModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.schedulers.scheduling_utils import SchedulerMixin, SchedulerOutput
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.outputs import BaseOutput
from pydantic import Field
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer

from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
from invokeai.backend.ip_adapter.unet_patcher import UNetPatcher
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningData

from ..util import auto_detect_slice_size, normalize_device
from .diffusion import AttentionMapSaver, InvokeAIDiffuserComponent


@dataclass
class PipelineIntermediateState:
    step: int
    order: int
    total_steps: int
    timestep: int
    latents: torch.Tensor
    predicted_original: Optional[torch.Tensor] = None
    attention_map_saver: Optional[AttentionMapSaver] = None


@dataclass
class AddsMaskLatents:
    """Add the channels required for inpainting model input.

    The inpainting model takes the normal latent channels as input, _plus_ a one-channel mask
    and the latent encoding of the base image.

    This class assumes the same mask and base image should apply to all items in the batch.
    """

    forward: Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor]
    mask: torch.Tensor
    initial_image_latents: torch.Tensor

    def __call__(
        self,
        latents: torch.Tensor,
        t: torch.Tensor,
        text_embeddings: torch.Tensor,
        **kwargs,
    ) -> torch.Tensor:
        model_input = self.add_mask_channels(latents)
        return self.forward(model_input, t, text_embeddings, **kwargs)

    def add_mask_channels(self, latents):
        batch_size = latents.size(0)
        # duplicate mask and latents for each batch
        mask = einops.repeat(self.mask, "b c h w -> (repeat b) c h w", repeat=batch_size)
        image_latents = einops.repeat(self.initial_image_latents, "b c h w -> (repeat b) c h w", repeat=batch_size)
        # add mask and image as additional channels
        model_input, _ = einops.pack([latents, mask, image_latents], "b * h w")
        return model_input


def are_like_tensors(a: torch.Tensor, b: object) -> bool:
    return isinstance(b, torch.Tensor) and (a.size() == b.size())


@dataclass
class AddsMaskGuidance:
    mask: torch.FloatTensor
    mask_latents: torch.FloatTensor
    scheduler: SchedulerMixin
    noise: torch.Tensor

    def __call__(self, step_output: Union[BaseOutput, SchedulerOutput], t: torch.Tensor, conditioning) -> BaseOutput:
        output_class = step_output.__class__  # We'll create a new one with masked data.

        # The problem with taking SchedulerOutput instead of the model output is that we're less certain what's in it.
        # It's reasonable to assume the first thing is prev_sample, but then does it have other things
        # like pred_original_sample? Should we apply the mask to them too?
        # But what if there's just some other random field?
        prev_sample = step_output[0]
        # Mask anything that has the same shape as prev_sample, return others as-is.
        return output_class(
            {
                k: self.apply_mask(v, self._t_for_field(k, t)) if are_like_tensors(prev_sample, v) else v
                for k, v in step_output.items()
            }
        )

    def _t_for_field(self, field_name: str, t):
        if field_name == "pred_original_sample":
            return self.scheduler.timesteps[-1]
        return t

    def apply_mask(self, latents: torch.Tensor, t) -> torch.Tensor:
        batch_size = latents.size(0)
        mask = einops.repeat(self.mask, "b c h w -> (repeat b) c h w", repeat=batch_size)
        if t.dim() == 0:
            # some schedulers expect t to be one-dimensional.
            # TODO: file diffusers bug about inconsistency?
            t = einops.repeat(t, "-> batch", batch=batch_size)
        # Noise shouldn't be re-randomized between steps here. The multistep schedulers
        # get very confused about what is happening from step to step when we do that.
        mask_latents = self.scheduler.add_noise(self.mask_latents, self.noise, t)
        # TODO: Do we need to also apply scheduler.scale_model_input? Or is add_noise appropriately scaled already?
        # mask_latents = self.scheduler.scale_model_input(mask_latents, t)
        mask_latents = einops.repeat(mask_latents, "b c h w -> (repeat b) c h w", repeat=batch_size)
        masked_input = torch.lerp(mask_latents.to(dtype=latents.dtype), latents, mask.to(dtype=latents.dtype))
        return masked_input


def trim_to_multiple_of(*args, multiple_of=8):
    return tuple((x - x % multiple_of) for x in args)


def image_resized_to_grid_as_tensor(image: PIL.Image.Image, normalize: bool = True, multiple_of=8) -> torch.FloatTensor:
    """

    :param image: input image
    :param normalize: scale the range to [-1, 1] instead of [0, 1]
    :param multiple_of: resize the input so both dimensions are a multiple of this
    """
    w, h = trim_to_multiple_of(*image.size, multiple_of=multiple_of)
    transformation = T.Compose(
        [
            T.Resize((h, w), T.InterpolationMode.LANCZOS, antialias=True),
            T.ToTensor(),
        ]
    )
    tensor = transformation(image)
    if normalize:
        tensor = tensor * 2.0 - 1.0
    return tensor


def is_inpainting_model(unet: UNet2DConditionModel):
    return unet.conv_in.in_channels == 9


@dataclass
class ControlNetData:
    model: ControlNetModel = Field(default=None)
    image_tensor: torch.Tensor = Field(default=None)
    weight: Union[float, List[float]] = Field(default=1.0)
    begin_step_percent: float = Field(default=0.0)
    end_step_percent: float = Field(default=1.0)
    control_mode: str = Field(default="balanced")
    resize_mode: str = Field(default="just_resize")


@dataclass
class IPAdapterData:
    ip_adapter_model: IPAdapter = Field(default=None)
    # TODO: change to polymorphic so can do different weights per step (once implemented...)
    weight: Union[float, List[float]] = Field(default=1.0)
    # weight: float = Field(default=1.0)
    begin_step_percent: float = Field(default=0.0)
    end_step_percent: float = Field(default=1.0)


@dataclass
class T2IAdapterData:
    """A structure containing the information required to apply conditioning from a single T2I-Adapter model."""

    adapter_state: dict[torch.Tensor] = Field()
    weight: Union[float, list[float]] = Field(default=1.0)
    begin_step_percent: float = Field(default=0.0)
    end_step_percent: float = Field(default=1.0)


@dataclass
class InvokeAIStableDiffusionPipelineOutput(StableDiffusionPipelineOutput):
    r"""
    Output class for InvokeAI's Stable Diffusion pipeline.

    Args:
        attention_map_saver (`AttentionMapSaver`): Object containing attention maps that can be displayed to the user
         after generation completes. Optional.
    """

    attention_map_saver: Optional[AttentionMapSaver]


class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
    r"""
    Pipeline for text-to-image generation using Stable Diffusion.

    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

    Implementation note: This class started as a refactored copy of diffusers.StableDiffusionPipeline.
    Hopefully future versions of diffusers provide access to more of these functions so that we don't
    need to duplicate them here: https://github.com/huggingface/diffusers/issues/551#issuecomment-1281508384

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
        text_encoder ([`CLIPTextModel`]):
            Frozen text-encoder. Stable Diffusion uses the text portion of
            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
        tokenizer (`CLIPTokenizer`):
            Tokenizer of class
            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
        feature_extractor ([`CLIPFeatureExtractor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        unet: UNet2DConditionModel,
        scheduler: KarrasDiffusionSchedulers,
        safety_checker: Optional[StableDiffusionSafetyChecker],
        feature_extractor: Optional[CLIPFeatureExtractor],
        requires_safety_checker: bool = False,
        control_model: ControlNetModel = None,
    ):
        super().__init__(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
            requires_safety_checker=requires_safety_checker,
        )

        self.invokeai_diffuser = InvokeAIDiffuserComponent(self.unet, self._unet_forward)
        self.control_model = control_model
        self.use_ip_adapter = False

    def _adjust_memory_efficient_attention(self, latents: torch.Tensor):
        """
        if xformers is available, use it, otherwise use sliced attention.
        """
        config = InvokeAIAppConfig.get_config()
        if config.attention_type == "xformers":
            self.enable_xformers_memory_efficient_attention()
            return
        elif config.attention_type == "sliced":
            slice_size = config.attention_slice_size
            if slice_size == "auto":
                slice_size = auto_detect_slice_size(latents)
            elif slice_size == "balanced":
                slice_size = "auto"
            self.enable_attention_slicing(slice_size=slice_size)
            return
        elif config.attention_type == "normal":
            self.disable_attention_slicing()
            return
        elif config.attention_type == "torch-sdp":
            raise Exception("torch-sdp attention slicing not yet implemented")

        # the remainder if this code is called when attention_type=='auto'
        if self.unet.device.type == "cuda":
            if is_xformers_available() and not config.disable_xformers:
                self.enable_xformers_memory_efficient_attention()
                return
            elif hasattr(torch.nn.functional, "scaled_dot_product_attention"):
                # diffusers enable sdp automatically
                return

        if self.unet.device.type == "cpu" or self.unet.device.type == "mps":
            mem_free = psutil.virtual_memory().free
        elif self.unet.device.type == "cuda":
            mem_free, _ = torch.cuda.mem_get_info(normalize_device(self.unet.device))
        else:
            raise ValueError(f"unrecognized device {self.unet.device}")
        # input tensor of [1, 4, h/8, w/8]
        # output tensor of [16, (h/8 * w/8), (h/8 * w/8)]
        bytes_per_element_needed_for_baddbmm_duplication = latents.element_size() + 4
        max_size_required_for_baddbmm = (
            16
            * latents.size(dim=2)
            * latents.size(dim=3)
            * latents.size(dim=2)
            * latents.size(dim=3)
            * bytes_per_element_needed_for_baddbmm_duplication
        )
        if max_size_required_for_baddbmm > (mem_free * 3.0 / 4.0):  # 3.3 / 4.0 is from old Invoke code
            self.enable_attention_slicing(slice_size="max")
        elif torch.backends.mps.is_available():
            # diffusers recommends always enabling for mps
            self.enable_attention_slicing(slice_size="max")
        else:
            self.disable_attention_slicing()

    def to(self, torch_device: Optional[Union[str, torch.device]] = None, silence_dtype_warnings=False):
        raise Exception("Should not be called")

    def latents_from_embeddings(
        self,
        latents: torch.Tensor,
        num_inference_steps: int,
        conditioning_data: ConditioningData,
        *,
        noise: Optional[torch.Tensor],
        timesteps: torch.Tensor,
        init_timestep: torch.Tensor,
        additional_guidance: List[Callable] = None,
        callback: Callable[[PipelineIntermediateState], None] = None,
        control_data: List[ControlNetData] = None,
        ip_adapter_data: Optional[list[IPAdapterData]] = None,
        t2i_adapter_data: Optional[list[T2IAdapterData]] = None,
        mask: Optional[torch.Tensor] = None,
        masked_latents: Optional[torch.Tensor] = None,
        seed: Optional[int] = None,
    ) -> tuple[torch.Tensor, Optional[AttentionMapSaver]]:
        if init_timestep.shape[0] == 0:
            return latents, None

        if additional_guidance is None:
            additional_guidance = []

        orig_latents = latents.clone()

        batch_size = latents.shape[0]
        batched_t = init_timestep.expand(batch_size)

        if noise is not None:
            # latents = noise * self.scheduler.init_noise_sigma # it's like in t2l according to diffusers
            latents = self.scheduler.add_noise(latents, noise, batched_t)

        if mask is not None:
            # if no noise provided, noisify unmasked area based on seed(or 0 as fallback)
            if noise is None:
                noise = torch.randn(
                    orig_latents.shape,
                    dtype=torch.float32,
                    device="cpu",
                    generator=torch.Generator(device="cpu").manual_seed(seed or 0),
                ).to(device=orig_latents.device, dtype=orig_latents.dtype)

                latents = self.scheduler.add_noise(latents, noise, batched_t)
                latents = torch.lerp(
                    orig_latents, latents.to(dtype=orig_latents.dtype), mask.to(dtype=orig_latents.dtype)
                )

            if is_inpainting_model(self.unet):
                if masked_latents is None:
                    raise Exception("Source image required for inpaint mask when inpaint model used!")

                self.invokeai_diffuser.model_forward_callback = AddsMaskLatents(
                    self._unet_forward, mask, masked_latents
                )
            else:
                additional_guidance.append(AddsMaskGuidance(mask, orig_latents, self.scheduler, noise))

        try:
            latents, attention_map_saver = self.generate_latents_from_embeddings(
                latents,
                timesteps,
                conditioning_data,
                additional_guidance=additional_guidance,
                control_data=control_data,
                ip_adapter_data=ip_adapter_data,
                t2i_adapter_data=t2i_adapter_data,
                callback=callback,
            )
        finally:
            self.invokeai_diffuser.model_forward_callback = self._unet_forward

        # restore unmasked part
        if mask is not None:
            latents = torch.lerp(orig_latents, latents.to(dtype=orig_latents.dtype), mask.to(dtype=orig_latents.dtype))

        return latents, attention_map_saver

    def generate_latents_from_embeddings(
        self,
        latents: torch.Tensor,
        timesteps,
        conditioning_data: ConditioningData,
        *,
        additional_guidance: List[Callable] = None,
        control_data: List[ControlNetData] = None,
        ip_adapter_data: Optional[list[IPAdapterData]] = None,
        t2i_adapter_data: Optional[list[T2IAdapterData]] = None,
        callback: Callable[[PipelineIntermediateState], None] = None,
    ):
        self._adjust_memory_efficient_attention(latents)
        if additional_guidance is None:
            additional_guidance = []

        batch_size = latents.shape[0]
        attention_map_saver: Optional[AttentionMapSaver] = None

        if timesteps.shape[0] == 0:
            return latents, attention_map_saver

        ip_adapter_unet_patcher = None
        if conditioning_data.extra is not None and conditioning_data.extra.wants_cross_attention_control:
            attn_ctx = self.invokeai_diffuser.custom_attention_context(
                self.invokeai_diffuser.model,
                extra_conditioning_info=conditioning_data.extra,
                step_count=len(self.scheduler.timesteps),
            )
            self.use_ip_adapter = False
        elif ip_adapter_data is not None:
            # TODO(ryand): Should we raise an exception if both custom attention and IP-Adapter attention are active?
            # As it is now, the IP-Adapter will silently be skipped.
            ip_adapter_unet_patcher = UNetPatcher([ipa.ip_adapter_model for ipa in ip_adapter_data])
            attn_ctx = ip_adapter_unet_patcher.apply_ip_adapter_attention(self.invokeai_diffuser.model)
            self.use_ip_adapter = True
        else:
            attn_ctx = nullcontext()

        with attn_ctx:
            if callback is not None:
                callback(
                    PipelineIntermediateState(
                        step=-1,
                        order=self.scheduler.order,
                        total_steps=len(timesteps),
                        timestep=self.scheduler.config.num_train_timesteps,
                        latents=latents,
                    )
                )

            # print("timesteps:", timesteps)
            for i, t in enumerate(self.progress_bar(timesteps)):
                batched_t = t.expand(batch_size)
                step_output = self.step(
                    batched_t,
                    latents,
                    conditioning_data,
                    step_index=i,
                    total_step_count=len(timesteps),
                    additional_guidance=additional_guidance,
                    control_data=control_data,
                    ip_adapter_data=ip_adapter_data,
                    t2i_adapter_data=t2i_adapter_data,
                    ip_adapter_unet_patcher=ip_adapter_unet_patcher,
                )
                latents = step_output.prev_sample

                latents = self.invokeai_diffuser.do_latent_postprocessing(
                    postprocessing_settings=conditioning_data.postprocessing_settings,
                    latents=latents,
                    sigma=batched_t,
                    step_index=i,
                    total_step_count=len(timesteps),
                )

                predicted_original = getattr(step_output, "pred_original_sample", None)

                # TODO resuscitate attention map saving
                # if i == len(timesteps)-1 and extra_conditioning_info is not None:
                #    eos_token_index = extra_conditioning_info.tokens_count_including_eos_bos - 1
                #    attention_map_token_ids = range(1, eos_token_index)
                #    attention_map_saver = AttentionMapSaver(token_ids=attention_map_token_ids, latents_shape=latents.shape[-2:])
                #    self.invokeai_diffuser.setup_attention_map_saving(attention_map_saver)

                if callback is not None:
                    callback(
                        PipelineIntermediateState(
                            step=i,
                            order=self.scheduler.order,
                            total_steps=len(timesteps),
                            timestep=int(t),
                            latents=latents,
                            predicted_original=predicted_original,
                            attention_map_saver=attention_map_saver,
                        )
                    )

            return latents, attention_map_saver

    @torch.inference_mode()
    def step(
        self,
        t: torch.Tensor,
        latents: torch.Tensor,
        conditioning_data: ConditioningData,
        step_index: int,
        total_step_count: int,
        additional_guidance: List[Callable] = None,
        control_data: List[ControlNetData] = None,
        ip_adapter_data: Optional[list[IPAdapterData]] = None,
        t2i_adapter_data: Optional[list[T2IAdapterData]] = None,
        ip_adapter_unet_patcher: Optional[UNetPatcher] = None,
    ):
        # invokeai_diffuser has batched timesteps, but diffusers schedulers expect a single value
        timestep = t[0]
        if additional_guidance is None:
            additional_guidance = []

        # TODO: should this scaling happen here or inside self._unet_forward?
        #     i.e. before or after passing it to InvokeAIDiffuserComponent
        latent_model_input = self.scheduler.scale_model_input(latents, timestep)

        # handle IP-Adapter
        if self.use_ip_adapter and ip_adapter_data is not None:  # somewhat redundant but logic is clearer
            for i, single_ip_adapter_data in enumerate(ip_adapter_data):
                first_adapter_step = math.floor(single_ip_adapter_data.begin_step_percent * total_step_count)
                last_adapter_step = math.ceil(single_ip_adapter_data.end_step_percent * total_step_count)
                weight = (
                    single_ip_adapter_data.weight[step_index]
                    if isinstance(single_ip_adapter_data.weight, List)
                    else single_ip_adapter_data.weight
                )
                if step_index >= first_adapter_step and step_index <= last_adapter_step:
                    # Only apply this IP-Adapter if the current step is within the IP-Adapter's begin/end step range.
                    ip_adapter_unet_patcher.set_scale(i, weight)
                else:
                    # Otherwise, set the IP-Adapter's scale to 0, so it has no effect.
                    ip_adapter_unet_patcher.set_scale(i, 0.0)

        # Handle ControlNet(s) and T2I-Adapter(s)
        down_block_additional_residuals = None
        mid_block_additional_residual = None
        down_intrablock_additional_residuals = None
        # if control_data is not None and t2i_adapter_data is not None:
        # TODO(ryand): This is a limitation of the UNet2DConditionModel API, not a fundamental incompatibility
        # between ControlNets and T2I-Adapters. We will try to fix this upstream in diffusers.
        #    raise Exception("ControlNet(s) and T2I-Adapter(s) cannot be used simultaneously (yet).")
        # elif control_data is not None:
        if control_data is not None:
            down_block_additional_residuals, mid_block_additional_residual = self.invokeai_diffuser.do_controlnet_step(
                control_data=control_data,
                sample=latent_model_input,
                timestep=timestep,
                step_index=step_index,
                total_step_count=total_step_count,
                conditioning_data=conditioning_data,
            )
        # elif t2i_adapter_data is not None:
        if t2i_adapter_data is not None:
            accum_adapter_state = None
            for single_t2i_adapter_data in t2i_adapter_data:
                # Determine the T2I-Adapter weights for the current denoising step.
                first_t2i_adapter_step = math.floor(single_t2i_adapter_data.begin_step_percent * total_step_count)
                last_t2i_adapter_step = math.ceil(single_t2i_adapter_data.end_step_percent * total_step_count)
                t2i_adapter_weight = (
                    single_t2i_adapter_data.weight[step_index]
                    if isinstance(single_t2i_adapter_data.weight, list)
                    else single_t2i_adapter_data.weight
                )
                if step_index < first_t2i_adapter_step or step_index > last_t2i_adapter_step:
                    # If the current step is outside of the T2I-Adapter's begin/end step range, then set its weight to 0
                    # so it has no effect.
                    t2i_adapter_weight = 0.0

                # Apply the t2i_adapter_weight, and accumulate.
                if accum_adapter_state is None:
                    # Handle the first T2I-Adapter.
                    accum_adapter_state = [val * t2i_adapter_weight for val in single_t2i_adapter_data.adapter_state]
                else:
                    # Add to the previous adapter states.
                    for idx, value in enumerate(single_t2i_adapter_data.adapter_state):
                        accum_adapter_state[idx] += value * t2i_adapter_weight

            # down_block_additional_residuals = accum_adapter_state
            down_intrablock_additional_residuals = accum_adapter_state

        uc_noise_pred, c_noise_pred = self.invokeai_diffuser.do_unet_step(
            sample=latent_model_input,
            timestep=t,  # TODO: debug how handled batched and non batched timesteps
            step_index=step_index,
            total_step_count=total_step_count,
            conditioning_data=conditioning_data,
            # extra:
            down_block_additional_residuals=down_block_additional_residuals,  # for ControlNet
            mid_block_additional_residual=mid_block_additional_residual,  # for ControlNet
            down_intrablock_additional_residuals=down_intrablock_additional_residuals,  # for T2I-Adapter
        )

        guidance_scale = conditioning_data.guidance_scale
        if isinstance(guidance_scale, list):
            guidance_scale = guidance_scale[step_index]

        noise_pred = self.invokeai_diffuser._combine(uc_noise_pred, c_noise_pred, guidance_scale)
        guidance_rescale_multiplier = conditioning_data.guidance_rescale_multiplier
        if guidance_rescale_multiplier > 0:
            noise_pred = self._rescale_cfg(
                noise_pred,
                c_noise_pred,
                guidance_rescale_multiplier,
            )

        # compute the previous noisy sample x_t -> x_t-1
        step_output = self.scheduler.step(noise_pred, timestep, latents, **conditioning_data.scheduler_args)

        # TODO: issue to diffusers?
        # undo internal counter increment done by scheduler.step, so timestep can be resolved as before call
        # this needed to be able call scheduler.add_noise with current timestep
        if self.scheduler.order == 2:
            self.scheduler._index_counter[timestep.item()] -= 1

        # TODO: this additional_guidance extension point feels redundant with InvokeAIDiffusionComponent.
        #    But the way things are now, scheduler runs _after_ that, so there was
        #    no way to use it to apply an operation that happens after the last scheduler.step.
        for guidance in additional_guidance:
            step_output = guidance(step_output, timestep, conditioning_data)

        # restore internal counter
        if self.scheduler.order == 2:
            self.scheduler._index_counter[timestep.item()] += 1

        return step_output

    @staticmethod
    def _rescale_cfg(total_noise_pred, pos_noise_pred, multiplier=0.7):
        """Implementation of Algorithm 2 from https://arxiv.org/pdf/2305.08891.pdf."""
        ro_pos = torch.std(pos_noise_pred, dim=(1, 2, 3), keepdim=True)
        ro_cfg = torch.std(total_noise_pred, dim=(1, 2, 3), keepdim=True)

        x_rescaled = total_noise_pred * (ro_pos / ro_cfg)
        x_final = multiplier * x_rescaled + (1.0 - multiplier) * total_noise_pred
        return x_final

    def _unet_forward(
        self,
        latents,
        t,
        text_embeddings,
        cross_attention_kwargs: Optional[dict[str, Any]] = None,
        **kwargs,
    ):
        """predict the noise residual"""
        if is_inpainting_model(self.unet) and latents.size(1) == 4:
            # Pad out normal non-inpainting inputs for an inpainting model.
            # FIXME: There are too many layers of functions and we have too many different ways of
            #     overriding things! This should get handled in a way more consistent with the other
            #     use of AddsMaskLatents.
            latents = AddsMaskLatents(
                self._unet_forward,
                mask=torch.ones_like(latents[:1, :1], device=latents.device, dtype=latents.dtype),
                initial_image_latents=torch.zeros_like(latents[:1], device=latents.device, dtype=latents.dtype),
            ).add_mask_channels(latents)

        # First three args should be positional, not keywords, so torch hooks can see them.
        return self.unet(
            latents,
            t,
            text_embeddings,
            cross_attention_kwargs=cross_attention_kwargs,
            **kwargs,
        ).sample