v0.27.1/pipeline_demofusion_sdxl.py

import inspect
import os
import random
import warnings
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import matplotlib.pyplot as plt
import torch
import torch.nn.functional as F
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.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import (
 is_accelerate_available,
 is_accelerate_version,
 is_invisible_watermark_available,
 logging,
 replace_example_docstring,
)
from diffusers.utils.torch_utils import randn_tensor


if is_invisible_watermark_available():
 from diffusers.pipelines.stable_diffusion_xl.watermark import (
 StableDiffusionXLWatermarker,
 )


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

EXAMPLE_DOC_STRING = """
 Examples:
 ```py
 >>> import torch
 >>> from diffusers import StableDiffusionXLPipeline

 >>> pipe = StableDiffusionXLPipeline.from_pretrained(
 ... "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
 ... )
 >>> pipe = pipe.to("cuda")

 >>> prompt = "a photo of an astronaut riding a horse on mars"
 >>> image = pipe(prompt).images[0]
 ```
"""


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, StableDiffusionMixin, FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
):
 r"""
 Pipeline for text-to-image generation using Stable Diffusion XL.

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

 In addition the pipeline inherits the following loading methods:
 - *LoRA*: [`StableDiffusionXLPipeline.load_lora_weights`]
 - *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`]

 as well as the following saving methods:
 - *LoRA*: [`loaders.StableDiffusionXLPipeline.save_lora_weights`]

 Args:
 vae ([`AutoencoderKL`]):
 Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
 text_encoder ([`CLIPTextModel`]):
 Frozen text-encoder. Stable Diffusion XL uses the text portion of
 [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
 the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
 text_encoder_2 ([` CLIPTextModelWithProjection`]):
 Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
 [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
 specifically the
 [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
 variant.
 tokenizer (`CLIPTokenizer`):
 Tokenizer of class
 [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
 tokenizer_2 (`CLIPTokenizer`):
 Second Tokenizer of class
 [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
 unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
 scheduler ([`SchedulerMixin`]):
 A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
 [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
 force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
 Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
 `stabilityai/stable-diffusion-xl-base-1-0`.
 add_watermarker (`bool`, *optional*):
 Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to
 watermark output images. If not defined, it will default to True if the package is installed, otherwise no
 watermarker will be used.
 """

 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,
 add_watermarker: Optional[bool] = None,
 ):
 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

 add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()

 if add_watermarker:
 self.watermark = StableDiffusionXLWatermarker()
 else:
 self.watermark = None

 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,
 ):
 r"""
 Encodes the prompt into text encoder hidden states.

 Args:
 prompt (`str` or `List[str]`, *optional*):
 prompt to be encoded
 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
 device: (`torch.device`):
 torch device
 num_images_per_prompt (`int`):
 number of images that should be generated per prompt
 do_classifier_free_guidance (`bool`):
 whether to use classifier free guidance or not
 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
 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.
 lora_scale (`float`, *optional*):
 A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
 """
 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

 # 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: process 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])
 logger.warning(
 "The following part of your input was truncated because CLIP can only handle sequences up to"
 f" {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
 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:
 raise ValueError(
 f"the larger one of `height` and `width` has to be divisible by 1024 but are {height} and {width}."
 )

 if num_images_per_prompt != 1:
 warnings.warn("num_images_per_prompt != 1 is not supported by DemoFusion 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):
 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

 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
 ]
 )
 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]
 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()
 @replace_example_docstring(EXAMPLE_DOC_STRING)
 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] = 3.0,
 cosine_scale_2: Optional[float] = 1.0,
 cosine_scale_3: Optional[float] = 1.0,
 sigma: Optional[float] = 0.8,
 show_image: 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 standerd value of the gaussian filter.
 show_image (`bool`, defaults to False):
 Determine whether to show intermediate results during generation.

 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

 # 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. TODO: 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)

 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
 - (denoising_end * self.scheduler.config.num_train_timesteps)
 )
 )
 num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
 timesteps = timesteps[:num_inference_steps]

 output_images = []

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

 print("### Phase 1 Denoising ###")
 with self.progress_bar(total=num_inference_steps) as progress_bar:
 for i, t in enumerate(timesteps):
 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()
 if not 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 needs_upcasting:
 self.upcast_vae()
 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 print("### Phase 1 Decoding ###")
 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)
 if show_image:
 plt.figure(figsize=(10, 10))
 plt.imshow(image[0])
 plt.axis("off") # Turn off axis numbers and ticks
 plt.show()
 output_images.append(image[0])

 ####################################################### Phase 2+ #####################################################

 for current_scale_num in range(2, scale_num + 1):
 print("### Phase {} Denoising ###".format(current_scale_num))
 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,
 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()
 )

 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)
 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)
 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 not 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 needs_upcasting:
 self.upcast_vae()
 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)

 print("### Phase {} Decoding ###".format(current_scale_num))
 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)
 else:
 image = latents

 if not output_type == "latent":
 image = self.image_processor.postprocess(image, output_type=output_type)
 if show_image:
 plt.figure(figsize=(10, 10))
 plt.imshow(image[0])
 plt.axis("off") # Turn off axis numbers and ticks
 plt.show()
 output_images.append(image[0])

 # Offload all models
 self.maybe_free_model_hooks()

 return output_images

 # 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):
 # 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(getattr(component, "_hf_hook"), CpuOffload)
 is_sequential_cpu_offload = isinstance(getattr(component, "_hf_hook"), AlignDevicesHook)
 logger.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()

 @classmethod
 def save_lora_weights(
 self,
 save_directory: Union[str, os.PathLike],
 unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
 text_encoder_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
 text_encoder_2_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
 is_main_process: bool = True,
 weight_name: str = None,
 save_function: Callable = None,
 safe_serialization: bool = True,
 ):
 state_dict = {}

 def pack_weights(layers, prefix):
 layers_weights = layers.state_dict() if isinstance(layers, torch.nn.Module) else layers
 layers_state_dict = {f"{prefix}.{module_name}": param for module_name, param in layers_weights.items()}
 return layers_state_dict

 if not (unet_lora_layers or text_encoder_lora_layers or text_encoder_2_lora_layers):
 raise ValueError(
 "You must pass at least one of `unet_lora_layers`, `text_encoder_lora_layers` or `text_encoder_2_lora_layers`."
 )

 if unet_lora_layers:
 state_dict.update(pack_weights(unet_lora_layers, "unet"))

 if text_encoder_lora_layers and text_encoder_2_lora_layers:
 state_dict.update(pack_weights(text_encoder_lora_layers, "text_encoder"))
 state_dict.update(pack_weights(text_encoder_2_lora_layers, "text_encoder_2"))

 self.write_lora_layers(
 state_dict=state_dict,
 save_directory=save_directory,
 is_main_process=is_main_process,
 weight_name=weight_name,
 save_function=save_function,
 safe_serialization=safe_serialization,
 )

 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)