InvokeAI/invokeai/backend/stable_diffusion/diffusers_pipeline.py

from __future__ import annotations

import dataclasses
import inspect
import math
import secrets
from dataclasses import dataclass, field
from typing import Any, Callable, Generic, List, Optional, Type, Union

import PIL.Image
import einops
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 .diffusion import (
    AttentionMapSaver,
    InvokeAIDiffuserComponent,
    PostprocessingSettings,
    BasicConditioningInfo,
)
from ..util import normalize_device


@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),
            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 ConditioningData:
    unconditioned_embeddings: BasicConditioningInfo
    text_embeddings: BasicConditioningInfo
    guidance_scale: Union[float, List[float]]
    """
    Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
    `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf).
    Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate
    images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
    """
    extra: Optional[InvokeAIDiffuserComponent.ExtraConditioningInfo] = None
    scheduler_args: dict[str, Any] = field(default_factory=dict)
    """
    Additional arguments to pass to invokeai_diffuser.do_latent_postprocessing().
    """
    postprocessing_settings: Optional[PostprocessingSettings] = None

    @property
    def dtype(self):
        return self.text_embeddings.dtype

    def add_scheduler_args_if_applicable(self, scheduler, **kwargs):
        scheduler_args = dict(self.scheduler_args)
        step_method = inspect.signature(scheduler.step)
        for name, value in kwargs.items():
            try:
                step_method.bind_partial(**{name: value})
            except TypeError:
                # FIXME: don't silently discard arguments
                pass  # debug("%s does not accept argument named %r", scheduler, name)
            else:
                scheduler_args[name] = value
        return dataclasses.replace(self, scheduler_args=scheduler_args)


@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,
            text_encoder,
            tokenizer,
            unet,
            scheduler,
            safety_checker,
            feature_extractor,
            requires_safety_checker,
        )

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
            # FIXME: can't currently register control module
            # control_model=control_model,
        )
        self.invokeai_diffuser = InvokeAIDiffuserComponent(self.unet, self._unet_forward)
        self.control_model = control_model

    def _adjust_memory_efficient_attention(self, latents: torch.Tensor):
        """
        if xformers is available, use it, otherwise use sliced attention.
        """
        config = InvokeAIAppConfig.get_config()
        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,
        mask: 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 is_inpainting_model(self.unet):
                # You'd think the inpainting model wouldn't be paying attention to the area it is going to repaint
                # (that's why there's a mask!) but it seems to really want that blanked out.
                # masked_latents = latents * torch.where(mask < 0.5, 1, 0) TODO: inpaint/outpaint/infill

                # TODO: we should probably pass this in so we don't have to try/finally around setting it.
                self.invokeai_diffuser.model_forward_callback = AddsMaskLatents(self._unet_forward, mask, orig_latents)
            else:
                # 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)
                    )

                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,
                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,
        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

        extra_conditioning_info = conditioning_data.extra
        with self.invokeai_diffuser.custom_attention_context(
            self.invokeai_diffuser.model,
            extra_conditioning_info=extra_conditioning_info,
            step_count=len(self.scheduler.timesteps),
        ):
            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,
                )
                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,
    ):
        # 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)

        # default is no controlnet, so set controlnet processing output to None
        controlnet_down_block_samples, controlnet_mid_block_sample = None, None
        if control_data is not None:
            controlnet_down_block_samples, controlnet_mid_block_sample = 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,
            )

        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=controlnet_down_block_samples,  # from controlnet(s)
            mid_block_additional_residual=controlnet_mid_block_sample,  # from controlnet(s)
        )

        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,
        )

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

        # 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)

        return step_output

    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