path: root/pipelines/stable_diffusion/vlpn_stable_diffusion.py

import inspect
import warnings
import math
from typing import List, Dict, Any, Optional, Union, Callable, Literal

import numpy as np
import torch
import torch.nn.functional as F
import PIL

from diffusers.configuration_utils import FrozenDict
from diffusers.utils import is_accelerate_available
from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    UNet2DConditionModel,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import logging, randn_tensor
from transformers import CLIPTextModel, CLIPTokenizer

from models.clip.util import unify_input_ids, get_extended_embeddings
from util.noise import perlin_noise

logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


def preprocess(image):
    w, h = image.size
    w, h = map(lambda x: x - x % 8, (w, h))  # resize to integer multiple of 8
    image = image.resize((w, h), resample=PIL.Image.LANCZOS)
    image = np.array(image).astype(np.float32) / 255.0
    image = image[None].transpose(0, 3, 1, 2)
    image = torch.from_numpy(image)
    return 2.0 * image - 1.0


def gaussian_blur_2d(img, kernel_size, sigma):
    ksize_half = (kernel_size - 1) * 0.5

    x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)

    pdf = torch.exp(-0.5 * (x / sigma).pow(2))

    x_kernel = pdf / pdf.sum()
    x_kernel = x_kernel.to(device=img.device, dtype=img.dtype)

    kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :])
    kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1])

    padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2]

    img = F.pad(img, padding, mode="reflect")
    img = F.conv2d(img, kernel2d, groups=img.shape[-3])

    return img


class CrossAttnStoreProcessor:
    def __init__(self):
        self.attention_probs = None

    def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None):
        batch_size, sequence_length, _ = hidden_states.shape
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.cross_attention_norm:
            encoder_hidden_states = attn.norm_cross(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        query = attn.head_to_batch_dim(query)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)

        self.attention_probs = attn.get_attention_scores(query, key, attention_mask)
        hidden_states = torch.bmm(self.attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        return hidden_states


class VlpnStableDiffusion(DiffusionPipeline):
    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        unet: UNet2DConditionModel,
        scheduler: Union[
            DDIMScheduler,
            PNDMScheduler,
            LMSDiscreteScheduler,
            EulerDiscreteScheduler,
            EulerAncestralDiscreteScheduler,
            DPMSolverMultistepScheduler,
        ],
    ):
        super().__init__()

        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
            warnings.warn(
                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
                " file",
                DeprecationWarning,
            )
            new_config = dict(scheduler.config)
            new_config["steps_offset"] = 1
            scheduler._internal_dict = FrozenDict(new_config)

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
        )
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)

    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
        r"""
        Enable sliced attention computation.
        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
        in several steps. This is useful to save some memory in exchange for a small speed decrease.
        Args:
            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
                `attention_head_dim` must be a multiple of `slice_size`.
        """
        if slice_size == "auto":
            if isinstance(self.unet.config.attention_head_dim, int):
                # half the attention head size is usually a good trade-off between
                # speed and memory
                slice_size = self.unet.config.attention_head_dim // 2
            else:
                # if `attention_head_dim` is a list, take the smallest head size
                slice_size = min(self.unet.config.attention_head_dim)

        self.unet.set_attention_slice(slice_size)

    def disable_attention_slicing(self):
        r"""
        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
        back to computing attention in one step.
        """
        # set slice_size = `None` to disable `attention slicing`
        self.enable_attention_slicing(None)

    def enable_sequential_cpu_offload(self):
        r"""
        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
        """
        if is_accelerate_available():
            from accelerate import cpu_offload
        else:
            raise ImportError("Please install accelerate via `pip install accelerate`")

        device = torch.device("cuda")

        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
            if cpu_offloaded_model is not None:
                cpu_offload(cpu_offloaded_model, device)

    def enable_vae_slicing(self):
        r"""
        Enable sliced VAE decoding.
        When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
        steps. This is useful to save some memory and allow larger batch sizes.
        """
        self.vae.enable_slicing()

    def disable_vae_slicing(self):
        r"""
        Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
        computing decoding in one step.
        """
        self.vae.disable_slicing()

    @property
    def execution_device(self):
        r"""
        Returns the device on which the pipeline's models will be executed. After calling
        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
        hooks.
        """
        if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
            return self.device
        for module in self.unet.modules():
            if (
                hasattr(module, "_hf_hook")
                and hasattr(module._hf_hook, "execution_device")
                and module._hf_hook.execution_device is not None
            ):
                return torch.device(module._hf_hook.execution_device)
        return self.device

    def check_inputs(
        self,
        prompt: Union[str, List[str], List[int], List[List[int]]],
        negative_prompt: Optional[Union[str, List[str], List[int], List[List[int]]]],
        width: int,
        height: int,
        strength: float,
        callback_steps: Optional[int]
    ):
        if isinstance(prompt, str) or (isinstance(prompt, list) and isinstance(prompt[0], int)):
            prompt = [prompt]

        if negative_prompt is None:
            negative_prompt = ""

        if isinstance(negative_prompt, str) or (isinstance(negative_prompt, list) and isinstance(negative_prompt[0], int)):
            negative_prompt = [negative_prompt] * len(prompt)

        if not isinstance(prompt, list):
            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")

        if not isinstance(negative_prompt, list):
            raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}")

        if len(negative_prompt) != len(prompt):
            raise ValueError(
                f"`prompt` and `negative_prompt` have to be the same length, but are {len(prompt)} and {len(negative_prompt)}")

        if strength < 0 or strength > 1:
            raise ValueError(f"`strength` should in [0.0, 1.0] but is {strength}")

        if height % 8 != 0 or width % 8 != 0:
            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")

        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
        ):
            raise ValueError(
                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                f" {type(callback_steps)}."
            )

        return prompt, negative_prompt

    def encode_prompt(
        self,
        prompt: Union[List[str], List[List[int]]],
        negative_prompt: Union[List[str], List[List[int]]],
        num_images_per_prompt: int,
        do_classifier_free_guidance: bool,
        device
    ):
        if isinstance(prompt[0], str):
            text_input_ids = self.tokenizer(prompt, padding="do_not_pad").input_ids
        else:
            text_input_ids = prompt

        text_input_ids *= num_images_per_prompt

        if do_classifier_free_guidance:
            if isinstance(prompt[0], str):
                unconditional_input_ids = self.tokenizer(negative_prompt, padding="do_not_pad").input_ids
            else:
                unconditional_input_ids = negative_prompt
            unconditional_input_ids *= num_images_per_prompt
            text_input_ids = unconditional_input_ids + text_input_ids

        text_inputs = unify_input_ids(self.tokenizer, text_input_ids)
        text_input_ids = text_inputs.input_ids

        if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
            attention_mask = text_inputs.attention_mask.to(device)
        else:
            attention_mask = None

        prompt_embeds = get_extended_embeddings(self.text_encoder, text_input_ids.to(device), attention_mask)
        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)

        return prompt_embeds

    def get_timesteps(self, num_inference_steps, strength, device):
        # get the original timestep using init_timestep
        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)

        t_start = max(num_inference_steps - init_timestep, 0)
        timesteps = self.scheduler.timesteps[t_start:]

        timesteps = timesteps.to(device)

        return timesteps, num_inference_steps - t_start

    def prepare_brightness_offset(self, batch_size, height, width, dtype, device, generator=None):
        offset_image = perlin_noise(
            (batch_size, 1, width, height),
            res=1,
            generator=generator,
            dtype=dtype,
            device=device
        )
        offset_latents = self.vae.encode(offset_image).latent_dist.sample(generator=generator)
        offset_latents = self.vae.config.scaling_factor * offset_latents
        return offset_latents

    def prepare_latents_from_image(self, init_image, timestep, batch_size, brightness_offset, dtype, device, generator=None):
        init_image = init_image.to(device=device, dtype=dtype)
        latents = self.vae.encode(init_image).latent_dist.sample(generator=generator)
        latents = self.vae.config.scaling_factor * latents

        if batch_size % latents.shape[0] != 0:
            raise ValueError(
                f"Cannot duplicate `init_image` of batch size {latents.shape[0]} to {batch_size} text prompts."
            )
        else:
            batch_multiplier = batch_size // latents.shape[0]
            latents = torch.cat([latents] * batch_multiplier, dim=0)

        # add noise to latents using the timesteps
        noise = torch.randn(latents.shape, generator=generator, device=device, dtype=dtype)

        if brightness_offset != 0:
            noise += brightness_offset * self.prepare_brightness_offset(
                batch_size, init_image.shape[3], init_image.shape[2], dtype, device, generator
            )

        # get latents
        latents = self.scheduler.add_noise(latents, noise, timestep)

        return latents

    def prepare_latents(self, batch_size, num_channels_latents, height, width, brightness_offset, dtype, device, generator, latents=None):
        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
        if isinstance(generator, list) and len(generator) != batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
            )

        if latents is None:
            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
        else:
            latents = latents.to(device)

        if brightness_offset != 0:
            latents += brightness_offset * self.prepare_brightness_offset(
                batch_size, height, width, dtype, device, generator
            )

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * self.scheduler.init_noise_sigma
        return latents

    def prepare_extra_step_kwargs(self, generator, eta):
        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]

        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
        extra_step_kwargs = {}
        if accepts_eta:
            extra_step_kwargs["eta"] = eta

        # check if the scheduler accepts generator
        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
        if accepts_generator:
            extra_step_kwargs["generator"] = generator
        return extra_step_kwargs

    def decode_latents(self, latents):
        latents = 1 / self.vae.config.scaling_factor * latents
        image = self.vae.decode(latents.to(dtype=self.vae.dtype)).sample
        image = (image / 2 + 0.5).clamp(0, 1)
        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
        return image

    @torch.no_grad()
    def __call__(
        self,
        prompt: Union[str, List[str], List[int], List[List[int]]],
        negative_prompt: Optional[Union[str, List[str], List[int], List[List[int]]]] = None,
        num_images_per_prompt: int = 1,
        strength: float = 1.0,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        sag_scale: float = 0.75,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        image: Optional[Union[torch.FloatTensor, PIL.Image.Image]] = None,
        brightness_offset: Union[float, torch.FloatTensor] = 0,
        output_type: str = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
        callback_steps: int = 1,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
    ):
        r"""
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`):
                The prompt or prompts to guide the image generation.
            strength (`float`, *optional*, defaults to 0.8):
                Conceptually, indicates how much to transform the reference `init_image`. Must be between 0 and 1.
                `init_image` will be used as a starting point, adding more noise to it the larger the `strength`. The
                number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
                noise will be maximum and the denoising process will run for the full number of iterations specified in
                `num_inference_steps`. A value of 1, therefore, essentially ignores `init_image`.
            height (`int`, *optional*, defaults to 768):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to 768):
                The width in pixels of the generated image.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                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.
            eta (`float`, *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                [`schedulers.DDIMScheduler`], will be ignored for others.
            generator (`torch.Generator`, *optional*):
                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
                deterministic.
            latents (`torch.FloatTensor`, *optional*):
                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor will ge generated by sampling using the supplied random `generator`.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttnProcessor` as defined under
                `self.processor` in
                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).

        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
            When returning a tuple, the first element is a list with the generated images, and the second element is a
            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
            (nsfw) content, according to the `safety_checker`.
        """
        # 0. Default height and width to unet
        height = height or self.unet.config.sample_size * self.vae_scale_factor
        width = width or self.unet.config.sample_size * self.vae_scale_factor

        # 1. Check inputs. Raise error if not correct
        prompt, negative_prompt = self.check_inputs(prompt, negative_prompt, width, height, strength, callback_steps)

        # 2. Define call parameters
        batch_size = len(prompt)
        device = self.execution_device
        num_channels_latents = self.unet.in_channels
        do_classifier_free_guidance = guidance_scale > 1.0
        do_self_attention_guidance = sag_scale > 0.0
        prep_from_image = isinstance(image, PIL.Image.Image)

        # 3. Encode input prompt
        prompt_embeds = self.encode_prompt(
            prompt,
            negative_prompt,
            num_images_per_prompt,
            do_classifier_free_guidance,
            device
        )

        # 4. Prepare latent variables
        if isinstance(image, PIL.Image.Image):
            image = preprocess(image)

        # 5. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)

        # 6. Prepare latent variables
        if prep_from_image:
            latents = self.prepare_latents_from_image(
                image,
                latent_timestep,
                batch_size * num_images_per_prompt,
                brightness_offset,
                prompt_embeds.dtype,
                device,
                generator,
            )
        else:
            latents = self.prepare_latents(
                batch_size,
                num_channels_latents,
                height,
                width,
                brightness_offset,
                prompt_embeds.dtype,
                device,
                generator,
                image,
            )

        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        # 8. Denoising loo
        if do_self_attention_guidance:
            store_processor = CrossAttnStoreProcessor()
            self.unet.mid_block.attentions[0].transformer_blocks[0].attn1.processor = store_processor

        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                # expand the latents if we are doing classifier free guidance
                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

                # predict the noise residual
                noise_pred = self.unet(
                    latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    cross_attention_kwargs=cross_attention_kwargs,
                ).sample

                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                if do_self_attention_guidance:
                    # classifier-free guidance produces two chunks of attention map
                    # and we only use unconditional one according to equation (24)
                    # in https://arxiv.org/pdf/2210.00939.pdf
                    if do_classifier_free_guidance:
                        # DDIM-like prediction of x0
                        pred_x0 = self.pred_x0(latents, noise_pred_uncond, t)
                        # get the stored attention maps
                        uncond_attn, cond_attn = store_processor.attention_probs.chunk(2)
                        # self-attention-based degrading of latents
                        degraded_latents = self.sag_masking(
                            pred_x0, uncond_attn, t, self.pred_epsilon(latents, noise_pred_uncond, t)
                        )
                        uncond_emb, _ = prompt_embeds.chunk(2)
                        # forward and give guidance
                        degraded_pred = self.unet(degraded_latents, t, encoder_hidden_states=uncond_emb).sample
                        noise_pred += sag_scale * (noise_pred_uncond - degraded_pred)
                    else:
                        # DDIM-like prediction of x0
                        pred_x0 = self.pred_x0(latents, noise_pred, t)
                        # get the stored attention maps
                        cond_attn = store_processor.attention_probs
                        # self-attention-based degrading of latents
                        degraded_latents = self.sag_masking(
                            pred_x0, cond_attn, t, self.pred_epsilon(latents, noise_pred, t)
                        )
                        # forward and give guidance
                        degraded_pred = self.unet(degraded_latents, t, encoder_hidden_states=prompt_embeds).sample
                        noise_pred += sag_scale * (noise_pred - degraded_pred)

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample

                # call the callback, if provided
                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        callback(i, t, latents)

        # 9. Post-processing
        image = self.decode_latents(latents)

        # 10. Run safety checker
        has_nsfw_concept = None

        # 11. Convert to PIL
        if output_type == "pil":
            image = self.numpy_to_pil(image)

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

    # Self-Attention-Guided (SAG) Stable Diffusion

    def sag_masking(self, original_latents, attn_map, t, eps):
        # Same masking process as in SAG paper: https://arxiv.org/pdf/2210.00939.pdf
        bh, hw1, hw2 = attn_map.shape
        b, latent_channel, latent_h, latent_w = original_latents.shape
        h = self.unet.attention_head_dim
        if isinstance(h, list):
            h = h[-1]
        map_size = math.isqrt(hw1)

        # Produce attention mask
        attn_map = attn_map.reshape(b, h, hw1, hw2)
        attn_mask = attn_map.mean(1, keepdim=False).sum(1, keepdim=False) > 1.0
        attn_mask = (
            attn_mask.reshape(b, map_size, map_size).unsqueeze(1).repeat(1, latent_channel, 1, 1).type(attn_map.dtype)
        )
        attn_mask = torch.nn.functional.interpolate(attn_mask, (latent_h, latent_w))

        # Blur according to the self-attention mask
        degraded_latents = gaussian_blur_2d(original_latents, kernel_size=9, sigma=1.0)
        degraded_latents = degraded_latents * attn_mask + original_latents * (1 - attn_mask)

        # Noise it again to match the noise level
        degraded_latents = self.scheduler.add_noise(degraded_latents, noise=eps, timesteps=t)

        return degraded_latents

    # Modified from diffusers.schedulers.scheduling_ddim.DDIMScheduler.step
    # Note: there are some schedulers that clip or do not return x_0 (PNDMScheduler, DDIMScheduler, etc.)
    def pred_x0(self, sample, model_output, timestep):
        alpha_prod_t = self.scheduler.alphas_cumprod[timestep]

        beta_prod_t = 1 - alpha_prod_t
        if self.scheduler.config.prediction_type == "epsilon":
            pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
        elif self.scheduler.config.prediction_type == "sample":
            pred_original_sample = model_output
        elif self.scheduler.config.prediction_type == "v_prediction":
            pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
            # predict V
            model_output = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
        else:
            raise ValueError(
                f"prediction_type given as {self.scheduler.config.prediction_type} must be one of `epsilon`, `sample`,"
                " or `v_prediction`"
            )

        return pred_original_sample

    def pred_epsilon(self, sample, model_output, timestep):
        alpha_prod_t = self.scheduler.alphas_cumprod[timestep]

        beta_prod_t = 1 - alpha_prod_t
        if self.scheduler.config.prediction_type == "epsilon":
            pred_eps = model_output
        elif self.scheduler.config.prediction_type == "sample":
            pred_eps = (sample - (alpha_prod_t**0.5) * model_output) / (beta_prod_t**0.5)
        elif self.scheduler.config.prediction_type == "v_prediction":
            pred_eps = (beta_prod_t**0.5) * sample + (alpha_prod_t**0.5) * model_output
        else:
            raise ValueError(
                f"prediction_type given as {self.scheduler.config.prediction_type} must be one of `epsilon`, `sample`,"
                " or `v_prediction`"
            )

        return pred_eps