scripts/demofusion.py

import random
import inspect
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import gradio as gr
from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers.image_processor import VaeImageProcessor
from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.models.attention_processor import AttnProcessor2_0, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor, XFormersAttnProcessor
from diffusers.models.lora import adjust_lora_scale_text_encoder
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import is_accelerate_available, is_accelerate_version
from diffusers.utils.torch_utils import randn_tensor
from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from modules import scripts, processing, shared, sd_models, devices


### Class definition
"""
Credits: https://github.com/PRIS-CV/DemoFusion
Source: https://github.com/PRIS-CV/DemoFusion/blob/main/pipeline_demofusion_sdxl.py
"""


def gaussian_kernel(kernel_size=3, sigma=1.0, channels=3):
    x_coord = torch.arange(kernel_size)
    gaussian_1d = torch.exp(-(x_coord - (kernel_size - 1) / 2) ** 2 / (2 * sigma ** 2))
    gaussian_1d = gaussian_1d / gaussian_1d.sum()
    gaussian_2d = gaussian_1d[:, None] * gaussian_1d[None, :]
    kernel = gaussian_2d[None, None, :, :].repeat(channels, 1, 1, 1)
    return kernel


def gaussian_filter(latents, kernel_size=3, sigma=1.0):
    channels = latents.shape[1]
    kernel = gaussian_kernel(kernel_size, sigma, channels).to(latents.device, latents.dtype)
    blurred_latents = F.conv2d(latents, kernel, padding=kernel_size//2, groups=channels)
    return blurred_latents


# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
    """
    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
    """
    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
    # rescale the results from guidance (fixes overexposure)
    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
    return noise_cfg


class DemoFusionSDXLPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin):
    model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        text_encoder_2: CLIPTextModelWithProjection,
        tokenizer: CLIPTokenizer,
        tokenizer_2: CLIPTokenizer,
        unet: UNet2DConditionModel,
        scheduler: KarrasDiffusionSchedulers,
        force_zeros_for_empty_prompt: bool = True,
    ):
        super().__init__()

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            text_encoder_2=text_encoder_2,
            tokenizer=tokenizer,
            tokenizer_2=tokenizer_2,
            unet=unet,
            scheduler=scheduler,
        )
        self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
        self.default_sample_size = self.unet.config.sample_size
        self.watermark = None

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
    def enable_vae_slicing(self):
        self.vae.enable_slicing()

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
    def disable_vae_slicing(self):
        self.vae.disable_slicing()

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
    def enable_vae_tiling(self):
        self.vae.enable_tiling()

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
    def disable_vae_tiling(self):
        self.vae.disable_tiling()

    def encode_prompt(
        self,
        prompt: str,
        prompt_2: Optional[str] = None,
        device: Optional[torch.device] = None,
        num_images_per_prompt: int = 1,
        do_classifier_free_guidance: bool = True,
        negative_prompt: Optional[str] = None,
        negative_prompt_2: Optional[str] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        lora_scale: Optional[float] = None,
    ):
        device = device or self._execution_device

        # set lora scale so that monkey patched LoRA
        # function of text encoder can correctly access it
        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
            self._lora_scale = lora_scale # pylint: disable=attribute-defined-outside-init
            # dynamically adjust the LoRA scale
            adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
            adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)

        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        # Define tokenizers and text encoders
        tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
        text_encoders = (
            [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
        )

        if prompt_embeds is None:
            prompt_2 = prompt_2 or prompt
            # textual inversion: procecss multi-vector tokens if necessary
            prompt_embeds_list = []
            prompts = [prompt, prompt_2]
            for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
                if isinstance(self, TextualInversionLoaderMixin):
                    prompt = self.maybe_convert_prompt(prompt, tokenizer)

                text_inputs = tokenizer(
                    prompt,
                    padding="max_length",
                    max_length=tokenizer.model_max_length,
                    truncation=True,
                    return_tensors="pt",
                )

                text_input_ids = text_inputs.input_ids
                untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids

                if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                    text_input_ids, untruncated_ids
                ):
                    removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
                    shared.log.warning(f"The following part of your input was truncated because CLIP can only handle sequences up to {tokenizer.model_max_length} tokens: {removed_text}")

                prompt_embeds = text_encoder(
                    text_input_ids.to(device),
                    output_hidden_states=True,
                )

                # We are only ALWAYS interested in the pooled output of the final text encoder
                pooled_prompt_embeds = prompt_embeds[0]
                prompt_embeds = prompt_embeds.hidden_states[-2]

                prompt_embeds_list.append(prompt_embeds)

            prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)

        # get unconditional embeddings for classifier free guidance
        zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt # pylint: disable=no-member
        if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
            negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
        elif do_classifier_free_guidance and negative_prompt_embeds is None:
            negative_prompt = negative_prompt or ""
            negative_prompt_2 = negative_prompt_2 or negative_prompt

            uncond_tokens: List[str]
            if prompt is not None and type(prompt) is not type(negative_prompt):
                raise TypeError(
                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                    f" {type(prompt)}."
                )
            elif isinstance(negative_prompt, str):
                uncond_tokens = [negative_prompt, negative_prompt_2]
            elif batch_size != len(negative_prompt):
                raise ValueError(
                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                    " the batch size of `prompt`."
                )
            else:
                uncond_tokens = [negative_prompt, negative_prompt_2]

            negative_prompt_embeds_list = []
            for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
                if isinstance(self, TextualInversionLoaderMixin):
                    negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)

                max_length = prompt_embeds.shape[1]
                uncond_input = tokenizer(
                    negative_prompt,
                    padding="max_length",
                    max_length=max_length,
                    truncation=True,
                    return_tensors="pt",
                )

                negative_prompt_embeds = text_encoder(
                    uncond_input.input_ids.to(device),
                    output_hidden_states=True,
                )
                # We are only ALWAYS interested in the pooled output of the final text encoder
                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
                negative_prompt_embeds_list.append(negative_prompt_embeds)
            negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)

        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
        bs_embed, seq_len, _ = prompt_embeds.shape
        # duplicate text embeddings for each generation per prompt, using mps friendly method
        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)

        if do_classifier_free_guidance:
            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            seq_len = negative_prompt_embeds.shape[1]
            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)

        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
            bs_embed * num_images_per_prompt, -1
        )
        if do_classifier_free_guidance:
            negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
                bs_embed * num_images_per_prompt, -1
            )

        return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
    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 check_inputs(
        self,
        prompt,
        prompt_2,
        height,
        width,
        callback_steps,
        negative_prompt=None,
        negative_prompt_2=None,
        prompt_embeds=None,
        negative_prompt_embeds=None,
        pooled_prompt_embeds=None,
        negative_pooled_prompt_embeds=None,
        num_images_per_prompt=None,
    ):
        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)}."
            )

        if prompt is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
                " only forward one of the two."
            )
        elif prompt_2 is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
                " only forward one of the two."
            )
        elif prompt is None and prompt_embeds is None:
            raise ValueError(
                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
            )
        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
        elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
            raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")

        if negative_prompt is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )
        elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )

        if prompt_embeds is not None and negative_prompt_embeds is not None:
            if prompt_embeds.shape != negative_prompt_embeds.shape:
                raise ValueError(
                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
                    f" {negative_prompt_embeds.shape}."
                )

        if prompt_embeds is not None and pooled_prompt_embeds is None:
            raise ValueError(
                "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
            )

        if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
            raise ValueError(
                "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
            )

        # DemoFusion specific checks
        if max(height, width) % 1024 != 0:
            shared.log.error('DemoFusion: resolution={width}x{height} long side must be divisible by 1024')
            return None

        if num_images_per_prompt != 1:
            shared.log.warning('DemoFusion: number of images per prompt is not support and will be ignored')
            num_images_per_prompt = 1

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
    def prepare_latents(self, batch_size, num_channels_latents, height, width, 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)

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

    def _get_add_time_ids(self, original_size, crops_coords_top_left, target_size, dtype):
        add_time_ids = list(original_size + crops_coords_top_left + target_size)

        passed_add_embed_dim = (
            self.unet.config.addition_time_embed_dim * len(add_time_ids) + self.text_encoder_2.config.projection_dim
        )
        expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features

        if expected_add_embed_dim != passed_add_embed_dim:
            raise ValueError(
                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
            )

        add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
        return add_time_ids

    def get_views(self, height, width, window_size=128, stride=64, random_jitter=False):
        # Here, we define the mappings F_i (see Eq. 7 in the MultiDiffusion paper https://arxiv.org/abs/2302.08113)
        # if panorama's height/width < window_size, num_blocks of height/width should return 1
        height //= self.vae_scale_factor
        width //= self.vae_scale_factor
        num_blocks_height = int((height - window_size) / stride - 1e-6) + 2 if height > window_size else 1
        num_blocks_width = int((width - window_size) / stride - 1e-6) + 2 if width > window_size else 1
        total_num_blocks = int(num_blocks_height * num_blocks_width)
        views = []
        for i in range(total_num_blocks):
            h_start = int((i // num_blocks_width) * stride)
            h_end = h_start + window_size
            w_start = int((i % num_blocks_width) * stride)
            w_end = w_start + window_size

            if h_end > height:
                h_start = int(h_start + height - h_end)
                h_end = int(height)
            if w_end > width:
                w_start = int(w_start + width - w_end)
                w_end = int(width)
            if h_start < 0:
                h_end = int(h_end - h_start)
                h_start = 0
            if w_start < 0:
                w_end = int(w_end - w_start)
                w_start = 0

            if random_jitter:
                jitter_range = (window_size - stride) // 4
                w_jitter = 0
                h_jitter = 0
                if (w_start != 0) and (w_end != width):
                    w_jitter = random.randint(-jitter_range, jitter_range)
                elif (w_start == 0) and (w_end != width):
                    w_jitter = random.randint(-jitter_range, 0)
                elif (w_start != 0) and (w_end == width):
                    w_jitter = random.randint(0, jitter_range)
                if (h_start != 0) and (h_end != height):
                    h_jitter = random.randint(-jitter_range, jitter_range)
                elif (h_start == 0) and (h_end != height):
                    h_jitter = random.randint(-jitter_range, 0)
                elif (h_start != 0) and (h_end == height):
                    h_jitter = random.randint(0, jitter_range)
                h_start += (h_jitter + jitter_range)
                h_end += (h_jitter + jitter_range)
                w_start += (w_jitter + jitter_range)
                w_end += (w_jitter + jitter_range)

            views.append((h_start, h_end, w_start, w_end))
        return views

    def tiled_decode(self, latents, current_height, current_width):
        core_size = self.unet.config.sample_size // 4
        core_stride = core_size
        pad_size = self.unet.config.sample_size // 4 * 3
        decoder_view_batch_size = 1

        if self.lowvram:
            core_stride = core_size // 2
            pad_size = core_size

        views = self.get_views(current_height, current_width, stride=core_stride, window_size=core_size)
        views_batch = [views[i : i + decoder_view_batch_size] for i in range(0, len(views), decoder_view_batch_size)]
        latents_ = F.pad(latents, (pad_size, pad_size, pad_size, pad_size), 'constant', 0)
        image = torch.zeros(latents.size(0), 3, current_height, current_width).to(latents.device)
        count = torch.zeros_like(image).to(latents.device)
        # get the latents corresponding to the current view coordinates
        with self.progress_bar(total=len(views_batch)) as progress_bar:
            for j, batch_view in enumerate(views_batch):
                len(batch_view)
                latents_for_view = torch.cat(
                    [
                        latents_[:, :, h_start:h_end+pad_size*2, w_start:w_end+pad_size*2]
                        for h_start, h_end, w_start, w_end in batch_view
                    ]
                ).to(self.vae.device)
                image_patch = self.vae.decode(latents_for_view / self.vae.config.scaling_factor, return_dict=False)[0]
                h_start, h_end, w_start, w_end = views[j]
                h_start, h_end, w_start, w_end = h_start * self.vae_scale_factor, h_end * self.vae_scale_factor, w_start * self.vae_scale_factor, w_end * self.vae_scale_factor
                p_h_start, p_h_end, p_w_start, p_w_end = pad_size * self.vae_scale_factor, image_patch.size(2) - pad_size * self.vae_scale_factor, pad_size * self.vae_scale_factor, image_patch.size(3) - pad_size * self.vae_scale_factor
                image[:, :, h_start:h_end, w_start:w_end] += image_patch[:, :, p_h_start:p_h_end, p_w_start:p_w_end].to(latents.device)
                count[:, :, h_start:h_end, w_start:w_end] += 1
                progress_bar.update()
        image = image / count

        return image

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
    def upcast_vae(self):
        dtype = self.vae.dtype
        self.vae.to(dtype=torch.float32)
        use_torch_2_0_or_xformers = isinstance(
            self.vae.decoder.mid_block.attentions[0].processor,
            (
                AttnProcessor2_0,
                XFormersAttnProcessor,
                LoRAXFormersAttnProcessor,
                LoRAAttnProcessor2_0,
            ),
        )
        # if xformers or torch_2_0 is used attention block does not need
        # to be in float32 which can save lots of memory
        if use_torch_2_0_or_xformers:
            self.vae.post_quant_conv.to(dtype)
            self.vae.decoder.conv_in.to(dtype)
            self.vae.decoder.mid_block.to(dtype)

    @torch.no_grad()
    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        prompt_2: Optional[Union[str, List[str]]] = None,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 50,
        denoising_end: Optional[float] = None,
        guidance_scale: float = 5.0,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        negative_prompt_2: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.FloatTensor] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = False,
        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
        callback_steps: int = 1,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        guidance_rescale: float = 0.0,
        original_size: Optional[Tuple[int, int]] = None,
        crops_coords_top_left: Tuple[int, int] = (0, 0),
        target_size: Optional[Tuple[int, int]] = None,
        negative_original_size: Optional[Tuple[int, int]] = None,
        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
        negative_target_size: Optional[Tuple[int, int]] = None,
        ################### DemoFusion specific parameters ####################
        view_batch_size: int = 16,
        multi_decoder: bool = True,
        stride: Optional[int] = 64,
        cosine_scale_1: Optional[float] = 3.,
        cosine_scale_2: Optional[float] = 1.,
        cosine_scale_3: Optional[float] = 1.,
        sigma: Optional[float] = 1.0,
        lowvram: bool = False,
    ):
        r"""
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                instead.
            prompt_2 (`str` or `List[str]`, *optional*):
                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
                used in both text-encoders
            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The height in pixels of the generated image. This is set to 1024 by default for the best results.
                Anything below 512 pixels won't work well for
                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
                and checkpoints that are not specifically fine-tuned on low resolutions.
            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The width in pixels of the generated image. This is set to 1024 by default for the best results.
                Anything below 512 pixels won't work well for
                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
                and checkpoints that are not specifically fine-tuned on low resolutions.
            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.
            denoising_end (`float`, *optional*):
                When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
                completed before it is intentionally prematurely terminated. As a result, the returned sample will
                still retain a substantial amount of noise as determined by the discrete timesteps selected by the
                scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
                "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
            guidance_scale (`float`, *optional*, defaults to 5.0):
                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.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
                less than `1`).
            negative_prompt_2 (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
                `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            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` or `List[torch.Generator]`, *optional*):
                One or a list of [torch generator(s)](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`.
            prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
                If not provided, pooled text embeddings will be generated from `prompt` input argument.
            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
                input argument.
            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_xl.StableDiffusionXLPipelineOutput`] instead
                of a plain tuple.
            callback (`Callable`, *optional*):
                A function that will be called every `callback_steps` steps during inference. The function will be
                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function will be called. If not specified, the callback will be
                called at every step.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            guidance_rescale (`float`, *optional*, defaults to 0.7):
                Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
                Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
                [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
                Guidance rescale factor should fix overexposure when using zero terminal SNR.
            original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
                If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
                `original_size` defaults to `(width, height)` if not specified. Part of SDXL's micro-conditioning as
                explained in section 2.2 of
                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
            crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
                `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
                `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
                `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
            target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
                For most cases, `target_size` should be set to the desired height and width of the generated image. If
                not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in
                section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
            negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
                To negatively condition the generation process based on a specific image resolution. Part of SDXL's
                micro-conditioning as explained in section 2.2 of
                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
            negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
                To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
                micro-conditioning as explained in section 2.2 of
                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
            negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
                To negatively condition the generation process based on a target image resolution. It should be as same
                as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
            ################### DemoFusion specific parameters ####################
            view_batch_size (`int`, defaults to 16):
                The batch size for multiple denoising paths. Typically, a larger batch size can result in higher
                efficiency but comes with increased GPU memory requirements.
            multi_decoder (`bool`, defaults to True):
                Determine whether to use a tiled decoder. Generally, when the resolution exceeds 3072x3072,
                a tiled decoder becomes necessary.
            stride (`int`, defaults to 64):
                The stride of moving local patches. A smaller stride is better for alleviating seam issues,
                but it also introduces additional computational overhead and inference time.
            cosine_scale_1 (`float`, defaults to 3):
                Control the strength of skip-residual. For specific impacts, please refer to Appendix C
                in the DemoFusion paper.
            cosine_scale_2 (`float`, defaults to 1):
                Control the strength of dilated sampling. For specific impacts, please refer to Appendix C
                in the DemoFusion paper.
            cosine_scale_3 (`float`, defaults to 1):
                Control the strength of the gaussion filter. For specific impacts, please refer to Appendix C
                in the DemoFusion paper.
            sigma (`float`, defaults to 1):
                The standard value of the gaussian filter.
            show_image (`bool`, defaults to False):
                Determine whether to show intermediate results during generation.
            lowvram (`bool`, defaults to False):
                Try to fit in 8 Gb of VRAM, with xformers installed.

        Examples:

        Returns:
            a `list` with the generated images at each phase.
        """

        # 0. Default height and width to unet
        height = height or self.default_sample_size * self.vae_scale_factor
        width = width or self.default_sample_size * self.vae_scale_factor

        x1_size = self.default_sample_size * self.vae_scale_factor

        height_scale = height / x1_size
        width_scale = width / x1_size
        scale_num = int(max(height_scale, width_scale))
        aspect_ratio = min(height_scale, width_scale) / max(height_scale, width_scale)

        original_size = original_size or (height, width)
        target_size = target_size or (height, width)

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            prompt_2,
            height,
            width,
            callback_steps,
            negative_prompt,
            negative_prompt_2,
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
            num_images_per_prompt,
        )

        # 2. Define call parameters
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        device = self._execution_device
        self.lowvram = lowvram # pylint: disable=attribute-defined-outside-init
        if self.lowvram:
            self.vae.cpu()
            self.unet.cpu()
            self.text_encoder.to(device)
            self.text_encoder_2.to(device)

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0

        # 3. Encode input prompt
        text_encoder_lora_scale = (
            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
        )
        (
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
        ) = self.encode_prompt(
            prompt=prompt,
            prompt_2=prompt_2,
            device=device,
            num_images_per_prompt=num_images_per_prompt,
            do_classifier_free_guidance=do_classifier_free_guidance,
            negative_prompt=negative_prompt,
            negative_prompt_2=negative_prompt_2,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
            lora_scale=text_encoder_lora_scale,
        )

        # 4. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)

        timesteps = self.scheduler.timesteps

        # 5. Prepare latent variables
        num_channels_latents = self.unet.config.in_channels
        latents = self.prepare_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height // scale_num,
            width // scale_num,
            prompt_embeds.dtype,
            device,
            generator,
            latents,
        )

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

        # 7. Prepare added time ids & embeddings
        add_text_embeds = pooled_prompt_embeds
        add_time_ids = self._get_add_time_ids(
            original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype
        )
        if negative_original_size is not None and negative_target_size is not None:
            negative_add_time_ids = self._get_add_time_ids(
                negative_original_size,
                negative_crops_coords_top_left,
                negative_target_size,
                dtype=prompt_embeds.dtype,
            )
        else:
            negative_add_time_ids = add_time_ids

        if do_classifier_free_guidance:
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
            add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
        del negative_prompt_embeds, negative_pooled_prompt_embeds, negative_add_time_ids

        prompt_embeds = prompt_embeds.to(device)
        add_text_embeds = add_text_embeds.to(device)
        add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)

        # 8. Denoising loop
        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)

        # 7.1 Apply denoising_end
        if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1:
            discrete_timestep_cutoff = int(
                round(
                    self.scheduler.config.num_train_timesteps # pylint: disable=no-member
                    - (denoising_end * self.scheduler.config.num_train_timesteps) # pylint: disable=no-member
                )
            )
            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
            timesteps = timesteps[:num_inference_steps]

        output_images = []

    ############################################################### Phase 1 #################################################################

        if self.lowvram:
            self.text_encoder.cpu()
            self.text_encoder_2.cpu()

        shared.log.debug('DemoFusion: phase=1 denoising')
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):

                if self.lowvram:
                    self.vae.cpu()
                    self.unet.to(device)

                latents_for_view = latents

                # expand the latents if we are doing classifier free guidance
                latent_model_input = (
                    latents.repeat_interleave(2, dim=0)
                    if do_classifier_free_guidance
                    else latents
                )
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

                # predict the noise residual
                added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
                noise_pred = self.unet(
                    latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    cross_attention_kwargs=cross_attention_kwargs,
                    added_cond_kwargs=added_cond_kwargs,
                    return_dict=False,
                )[0]

                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = noise_pred[::2], noise_pred[1::2]
                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                if do_classifier_free_guidance and guidance_rescale > 0.0:
                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]

                # 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:
                        step_idx = i // getattr(self.scheduler, "order", 1)
                        callback(step_idx, t, latents)

            anchor_mean = latents.mean()
            anchor_std = latents.std()
            del latents_for_view, latent_model_input, noise_pred, noise_pred_text, noise_pred_uncond
            if self.lowvram:
                latents = latents.cpu()
                torch.cuda.empty_cache()
            if output_type != "latent":
                # make sure the VAE is in float32 mode, as it overflows in float16
                needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast

                if self.lowvram:
                    needs_upcasting = False # use madebyollin/sdxl-vae-fp16-fix in lowvram mode!
                    self.unet.cpu()
                    self.vae.to(device)

                if needs_upcasting:
                    self.upcast_vae()
                    latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
                    shared.log.debug('DemoFusion: phase=1 decoding')
                if self.lowvram and multi_decoder:
                    current_width_height = self.unet.config.sample_size * self.vae_scale_factor
                    image = self.tiled_decode(latents, current_width_height, current_width_height)
                else:
                    image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
                # cast back to fp16 if needed
                if needs_upcasting:
                    self.vae.to(dtype=torch.float16)
                image = self.image_processor.postprocess(image, output_type=output_type)
                output_images.append(image[0])
            else:
                output_images.append(latents)

    ####################################################### Phase 2+ #####################################################
        for current_scale_num in range(2, scale_num + 1):
            if self.lowvram:
                latents = latents.to(device)
                self.unet.to(device)
                torch.cuda.empty_cache()
            shared.log.debug(f'DemoFusion: phase={current_scale_num} denoising')
            current_height = self.unet.config.sample_size * self.vae_scale_factor * current_scale_num
            current_width = self.unet.config.sample_size * self.vae_scale_factor * current_scale_num
            if height > width:
                current_width = int(current_width * aspect_ratio)
            else:
                current_height = int(current_height * aspect_ratio)

            latents = F.interpolate(latents.to(device), size=(int(current_height / self.vae_scale_factor), int(current_width / self.vae_scale_factor)), mode='bicubic')

            noise_latents = []
            noise = torch.randn_like(latents)
            for timestep in timesteps:
                noise_latent = self.scheduler.add_noise(latents, noise, timestep.unsqueeze(0))
                noise_latents.append(noise_latent)
            latents = noise_latents[0]

            with self.progress_bar(total=num_inference_steps) as progress_bar:
                for i, t in enumerate(timesteps):
                    count = torch.zeros_like(latents)
                    value = torch.zeros_like(latents)
                    cosine_factor = 0.5 * (1 + torch.cos(torch.pi * (self.scheduler.config.num_train_timesteps - t) / self.scheduler.config.num_train_timesteps)).cpu() # pylint: disable=no-member

                    c1 = cosine_factor ** cosine_scale_1
                    latents = latents * (1 - c1) + noise_latents[i] * c1

                    ############################################# MultiDiffusion #############################################

                    views = self.get_views(current_height, current_width, stride=stride, window_size=self.unet.config.sample_size, random_jitter=True)
                    views_batch = [views[i : i + view_batch_size] for i in range(0, len(views), view_batch_size)]

                    jitter_range = (self.unet.config.sample_size - stride) // 4
                    latents_ = F.pad(latents, (jitter_range, jitter_range, jitter_range, jitter_range), 'constant', 0)

                    count_local = torch.zeros_like(latents_)
                    value_local = torch.zeros_like(latents_)

                    for _j, batch_view in enumerate(views_batch):
                        vb_size = len(batch_view)

                        # get the latents corresponding to the current view coordinates
                        latents_for_view = torch.cat(
                            [
                                latents_[:, :, h_start:h_end, w_start:w_end]
                                for h_start, h_end, w_start, w_end in batch_view
                            ]
                        )

                        # expand the latents if we are doing classifier free guidance
                        latent_model_input = latents_for_view
                        latent_model_input = (
                            latent_model_input.repeat_interleave(2, dim=0)
                            if do_classifier_free_guidance
                            else latent_model_input
                        )
                        latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

                        prompt_embeds_input = torch.cat([prompt_embeds] * vb_size)
                        add_text_embeds_input = torch.cat([add_text_embeds] * vb_size)
                        add_time_ids_input = []
                        for h_start, _h_end, w_start, _w_end in batch_view:
                            add_time_ids_ = add_time_ids.clone()
                            add_time_ids_[:, 2] = h_start * self.vae_scale_factor
                            add_time_ids_[:, 3] = w_start * self.vae_scale_factor
                            add_time_ids_input.append(add_time_ids_)
                        add_time_ids_input = torch.cat(add_time_ids_input)

                        # predict the noise residual
                        added_cond_kwargs = {"text_embeds": add_text_embeds_input, "time_ids": add_time_ids_input}
                        noise_pred = self.unet(
                            latent_model_input,
                            t,
                            encoder_hidden_states=prompt_embeds_input,
                            cross_attention_kwargs=cross_attention_kwargs,
                            added_cond_kwargs=added_cond_kwargs,
                            return_dict=False,
                        )[0]

                        if do_classifier_free_guidance:
                            noise_pred_uncond, noise_pred_text = noise_pred[::2], noise_pred[1::2]
                            noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                        if do_classifier_free_guidance and guidance_rescale > 0.0:
                            # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
                            noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)

                        # compute the previous noisy sample x_t -> x_t-1
                        self.scheduler._init_step_index(t) # pylint: disable=no-member
                        latents_denoised_batch = self.scheduler.step(
                            noise_pred, t, latents_for_view, **extra_step_kwargs, return_dict=False)[0]

                        # extract value from batch
                        for latents_view_denoised, (h_start, h_end, w_start, w_end) in zip(
                            latents_denoised_batch.chunk(vb_size), batch_view
                        ):
                            value_local[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised
                            count_local[:, :, h_start:h_end, w_start:w_end] += 1

                    value_local = value_local[: ,:, jitter_range: jitter_range + current_height // self.vae_scale_factor, jitter_range: jitter_range + current_width // self.vae_scale_factor]
                    count_local = count_local[: ,:, jitter_range: jitter_range + current_height // self.vae_scale_factor, jitter_range: jitter_range + current_width // self.vae_scale_factor]

                    c2 = cosine_factor ** cosine_scale_2

                    value += value_local / count_local * (1 - c2)
                    count += torch.ones_like(value_local) * (1 - c2)

                    ############################################# Dilated Sampling #############################################

                    views = [[h, w] for h in range(current_scale_num) for w in range(current_scale_num)]
                    views_batch = [views[i : i + view_batch_size] for i in range(0, len(views), view_batch_size)]

                    h_pad = (current_scale_num - (latents.size(2) % current_scale_num)) % current_scale_num
                    w_pad = (current_scale_num - (latents.size(3) % current_scale_num)) % current_scale_num
                    latents_ = F.pad(latents, (w_pad, 0, h_pad, 0), 'constant', 0)

                    count_global = torch.zeros_like(latents_)
                    value_global = torch.zeros_like(latents_)

                    c3 = 0.99 * cosine_factor ** cosine_scale_3 + 1e-2
                    std_, mean_ = latents_.std(), latents_.mean()
                    latents_gaussian = gaussian_filter(latents_, kernel_size=(2*current_scale_num-1), sigma=sigma*c3)
                    latents_gaussian = (latents_gaussian - latents_gaussian.mean()) / latents_gaussian.std() * std_ + mean_

                    for _j, batch_view in enumerate(views_batch):
                        latents_for_view = torch.cat(
                            [
                                latents_[:, :, h::current_scale_num, w::current_scale_num]
                                for h, w in batch_view
                            ]
                        )
                        latents_for_view_gaussian = torch.cat(
                            [
                                latents_gaussian[:, :, h::current_scale_num, w::current_scale_num]
                                for h, w in batch_view
                            ]
                        )

                        vb_size = latents_for_view.size(0)

                        # expand the latents if we are doing classifier free guidance
                        latent_model_input = latents_for_view_gaussian
                        latent_model_input = (
                            latent_model_input.repeat_interleave(2, dim=0)
                            if do_classifier_free_guidance
                            else latent_model_input
                        )
                        latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

                        prompt_embeds_input = torch.cat([prompt_embeds] * vb_size)
                        add_text_embeds_input = torch.cat([add_text_embeds] * vb_size)
                        add_time_ids_input = torch.cat([add_time_ids] * vb_size)

                        # predict the noise residual
                        added_cond_kwargs = {"text_embeds": add_text_embeds_input, "time_ids": add_time_ids_input}
                        noise_pred = self.unet(
                            latent_model_input,
                            t,
                            encoder_hidden_states=prompt_embeds_input,
                            cross_attention_kwargs=cross_attention_kwargs,
                            added_cond_kwargs=added_cond_kwargs,
                            return_dict=False,
                        )[0]

                        if do_classifier_free_guidance:
                            noise_pred_uncond, noise_pred_text = noise_pred[::2], noise_pred[1::2]
                            noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                        if do_classifier_free_guidance and guidance_rescale > 0.0:
                            # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
                            noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)

                        # compute the previous noisy sample x_t -> x_t-1
                        self.scheduler._init_step_index(t) # pylint: disable=no-member
                        latents_denoised_batch = self.scheduler.step(
                            noise_pred, t, latents_for_view, **extra_step_kwargs, return_dict=False)[0]

                        # extract value from batch
                        for latents_view_denoised, (h, w) in zip(
                            latents_denoised_batch.chunk(vb_size), batch_view
                        ):
                            value_global[:, :, h::current_scale_num, w::current_scale_num] += latents_view_denoised
                            count_global[:, :, h::current_scale_num, w::current_scale_num] += 1

                    c2 = cosine_factor ** cosine_scale_2

                    value_global = value_global[: ,:, h_pad:, w_pad:]

                    value += value_global * c2
                    count += torch.ones_like(value_global) * c2

                           ###########################################################

                    latents = torch.where(count > 0, value / count, value)

                    # 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:
                            step_idx = i // getattr(self.scheduler, "order", 1)
                            callback(step_idx, t, latents)

    #########################################################################################################################################

                latents = (latents - latents.mean()) / latents.std() * anchor_std + anchor_mean
                if self.lowvram:
                    latents = latents.cpu()
                    torch.cuda.empty_cache()
                if output_type != "latent":
                    # make sure the VAE is in float32 mode, as it overflows in float16
                    needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast

                    if self.lowvram:
                        needs_upcasting = False # use madebyollin/sdxl-vae-fp16-fix in lowvram mode!
                        self.unet.cpu()
                        self.vae.to(device)

                    if needs_upcasting:
                        self.upcast_vae()
                        latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)

                    shared.log.debug(f'DemoFusion: phase={current_scale_num} decoding')
                    if multi_decoder:
                        image = self.tiled_decode(latents, current_height, current_width)
                    else:
                        image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]

                    # cast back to fp16 if needed
                    if needs_upcasting:
                        self.vae.to(dtype=torch.float16)
                    image = self.image_processor.postprocess(image, output_type=output_type)
                    output_images.append(image[0])
                else:
                    image = latents
                    output_images.append(image)

        # Offload all models
        self.maybe_free_model_hooks()
        output = ImagePipelineOutput(images=output_images)
        return output

    # Overrride to properly handle the loading and unloading of the additional text encoder.
    def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs): # pylint: disable=arguments-differ
        # We could have accessed the unet config from `lora_state_dict()` too. We pass
        # it here explicitly to be able to tell that it's coming from an SDXL
        # pipeline.

        # Remove any existing hooks.
        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
            from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module
        else:
            raise ImportError("Offloading requires `accelerate v0.17.0` or higher.")

        is_model_cpu_offload = False
        is_sequential_cpu_offload = False
        recursive = False
        for _, component in self.components.items():
            if isinstance(component, torch.nn.Module):
                if hasattr(component, "_hf_hook"):
                    is_model_cpu_offload = isinstance(component._hf_hook, CpuOffload) # pylint: disable=protected-access
                    is_sequential_cpu_offload = isinstance(component._hf_hook, AlignDevicesHook) # pylint: disable=protected-access
                    shared.log.info("Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again.")
                    recursive = is_sequential_cpu_offload
                    remove_hook_from_module(component, recurse=recursive)
        state_dict, network_alphas = self.lora_state_dict(
            pretrained_model_name_or_path_or_dict,
            unet_config=self.unet.config,
            **kwargs,
        )
        self.load_lora_into_unet(state_dict, network_alphas=network_alphas, unet=self.unet)

        text_encoder_state_dict = {k: v for k, v in state_dict.items() if "text_encoder." in k}
        if len(text_encoder_state_dict) > 0:
            self.load_lora_into_text_encoder(
                text_encoder_state_dict,
                network_alphas=network_alphas,
                text_encoder=self.text_encoder,
                prefix="text_encoder",
                lora_scale=self.lora_scale,
            )

        text_encoder_2_state_dict = {k: v for k, v in state_dict.items() if "text_encoder_2." in k}
        if len(text_encoder_2_state_dict) > 0:
            self.load_lora_into_text_encoder(
                text_encoder_2_state_dict,
                network_alphas=network_alphas,
                text_encoder=self.text_encoder_2,
                prefix="text_encoder_2",
                lora_scale=self.lora_scale,
            )

        # Offload back.
        if is_model_cpu_offload:
            self.enable_model_cpu_offload()
        elif is_sequential_cpu_offload:
            self.enable_sequential_cpu_offload()

    def _remove_text_encoder_monkey_patch(self):
        self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder)
        self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder_2)


### Script definition

class Script(scripts.Script):
    def title(self):
        return 'DemoFusion'

    def show(self, is_img2img):
        return not is_img2img if shared.backend == shared.Backend.DIFFUSERS else False

    # return signature is array of gradio components
    def ui(self, _is_img2img):
        with gr.Row():
            gr.HTML('<a href="https://github.com/PRIS-CV/DemoFusion">&nbsp DemoFusion</a><br>')
        with gr.Row():
            cosine_scale_1 = gr.Slider(minimum=0, maximum=5, step=0.1, value=3, label="Cosine scale 1")
            cosine_scale_2 = gr.Slider(minimum=0, maximum=5, step=0.1, value=1, label="Cosine scale 2")
            cosine_scale_3 = gr.Slider(minimum=0, maximum=5, step=0.1, value=1, label="Cosine scale 3")
        with gr.Row():
            view_batch_size = gr.Slider(minimum=4, maximum=32, step=4, value=8, label="Denoising batch size")
            sigma = gr.Slider(minimum=0.1, maximum=1, step=0.1, value=0.8, label="Sigma")
            stride = gr.Slider(minimum=8, maximum=96, step=8, value=64, label="Stride")
        with gr.Row():
            multi_decoder = gr.Checkbox(label="Multi decoder", value=True)
        return [cosine_scale_1, cosine_scale_2, cosine_scale_3, sigma, view_batch_size, stride, multi_decoder]

    def run(self, p: processing.StableDiffusionProcessing, cosine_scale_1, cosine_scale_2, cosine_scale_3, sigma, view_batch_size, stride, multi_decoder): # pylint: disable=arguments-differ
        c = shared.sd_model.__class__.__name__ if shared.sd_model is not None else ''
        if c != 'StableDiffusionXLPipeline':
            shared.log.warning(f'DemoFusion: pipeline={c} required=StableDiffusionXLPipeline')
            return None
        p.task_args['cosine_scale_1'] = cosine_scale_1
        p.task_args['cosine_scale_2'] = cosine_scale_2
        p.task_args['cosine_scale_3'] = cosine_scale_3
        p.task_args['sigma'] = sigma
        p.task_args['view_batch_size'] = view_batch_size
        p.task_args['stride'] = stride
        p.task_args['multi_decoder'] = multi_decoder
        p.task_args['output_type'] = 'np'
        p.task_args['low_vram'] = True
        shared.log.debug(f'DemoFusion: {p.task_args}')
        old_pipe = shared.sd_model
        new_pipe = DemoFusionSDXLPipeline(
            vae = shared.sd_model.vae,
            text_encoder=shared.sd_model.text_encoder,
            text_encoder_2=shared.sd_model.text_encoder_2,
            tokenizer=shared.sd_model.tokenizer,
            tokenizer_2=shared.sd_model.tokenizer_2,
            unet=shared.sd_model.unet,
            scheduler=shared.sd_model.scheduler,
            force_zeros_for_empty_prompt=shared.opts.diffusers_force_zeros,
        )
        shared.sd_model = new_pipe
        sd_models.move_model(shared.sd_model, devices.device) # move pipeline to device
        sd_models.set_diffuser_options(shared.sd_model, vae=None, op='model')
        shared.log.debug(f'DemoFusion create: pipeline={shared.sd_model.__class__.__name__}')
        processed = processing.process_images(p)
        shared.sd_model = old_pipe
        return processed