MatForger / pipeline.py

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	import inspect
	from typing import Any, Dict, List, Optional, Tuple, Union

	import numpy as np
	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	import torchvision.transforms.functional as TF
	from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
	from diffusers.loaders import FromSingleFileMixin
	from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import (
	EXAMPLE_DOC_STRING,
	rescale_noise_cfg,
	retrieve_timesteps,
	)
	from diffusers.schedulers import KarrasDiffusionSchedulers
	from diffusers.utils import (
	USE_PEFT_BACKEND,
	BaseOutput,
	deprecate,
	logging,
	replace_example_docstring,
	)
	from diffusers.utils.torch_utils import randn_tensor
	from PIL import Image

	from diffusers import AutoencoderKL, DiffusionPipeline, UNet2DConditionModel

	logger = logging.get_logger(__name__) # pylint: disable=invalid-name
	from dataclasses import dataclass


	def postprocess(
	image: torch.FloatTensor,
	output_type: str = "pil",
	):
	"""
	Postprocess the image output from tensor to `output_type`.

	Args:
	image (`torch.FloatTensor`):
	The image input, should be a pytorch tensor with shape `B x C x H x W`.
	output_type (`str`, optional, defaults to `pil`):
	The output type of the image, can be one of `pil`, `np`, `pt`, `latent`.

	Returns:
	`PIL.Image.Image`, `np.ndarray` or `torch.FloatTensor`:
	The postprocessed image.
	"""
	if not isinstance(image, torch.Tensor):
	raise ValueError(
	f"Input for postprocessing is in incorrect format: {type(image)}. We only support pytorch tensor"
	)
	if output_type not in ["latent", "pt", "np", "pil"]:
	deprecation_message = (
	f"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
	"`pil`, `np`, `pt`, `latent`"
	)
	deprecate(
	"Unsupported output_type", "1.0.0", deprecation_message, standard_warn=False
	)
	output_type = "np"

	image = image.detach().cpu()
	image = image.to(torch.float32)

	if output_type == "latent":
	return image

	# denormalize the image
	image = image.clamp(-1, 1) * 0.5 + 0.5

	materials = []
	for i in range(image.shape[0]):

	material = MatForgerMaterial()
	material.init_from_tensor(image[i])

	if output_type == "pt":
	material.to_pt()

	if output_type == "np":
	material.to_np()

	if output_type == "pil":
	material.to_pil()

	materials.append(material)

	return materials


	@dataclass
	class MatForgerMaterial:
	def __init__(
	self,
	basecolor: Optional[Union[Image.Image, np.ndarray, torch.FloatTensor]] = None,
	normal: Optional[Union[Image.Image, np.ndarray, torch.FloatTensor]] = None,
	height: Optional[Union[Image.Image, np.ndarray, torch.FloatTensor]] = None,
	roughness: Optional[Union[Image.Image, np.ndarray, torch.FloatTensor]] = None,
	metallic: Optional[Union[Image.Image, np.ndarray, torch.FloatTensor]] = None,
	):
	self.basecolor = basecolor
	self.normal = normal
	self.height = height
	self.roughness = roughness
	self.metallic = metallic

	def _to_numpy(self, image):
	if image is None:
	return None

	if isinstance(image, Image.Image):
	image = np.array(image)
	elif isinstance(image, torch.FloatTensor):
	image = image.cpu().numpy()
	return image

	def _to_pil(self, image):
	if image is None:
	return None

	if isinstance(image, np.ndarray):
	image = Image.fromarray(image)
	elif isinstance(image, torch.FloatTensor):
	image = TF.to_pil_image(image)
	return image

	def _to_pt(self, image):
	if image is None:
	return None

	if isinstance(image, np.ndarray):
	image = torch.from_numpy(image)
	elif isinstance(image, Image.Image):
	image = TF.to_tensor(image)
	return image

	def compute_normal_map_z_component(self, normal: torch.FloatTensor):
	"""
	Compute the z-component of the normal map for a tensor of shape (2, H, W).

	Parameters:
	- normal_map (torch.Tensor): A tensor of shape (2, H, W) containing the x and y components of the normal map.

	Returns:
	- A tensor of shape (1, H, W) containing the z-component of the normal map.
	"""
	# Normalize the normal map to the range [-1, 1]
	normal = normal * 2 - 1

	# Square the x and y components
	squared = normal**2

	# Sum along the first dimension (x^2 + y^2)
	sum_squared = squared.sum(dim=0, keepdim=True)

	# Compute z-component: sqrt(1 - (x^2 + y^2))
	z_component = torch.sqrt(1 - sum_squared).clamp(
	min=0
	) # Clamp to avoid negative values under sqrt

	normal = torch.cat([normal, z_component], dim=0)
	normal = normal * 0.5 + 0.5 # Denormalize to [0, 1]
	return normal

	def init_from_tensor(self, image: torch.FloatTensor):
	assert image.shape[0] >= 8, "Input tensor should have at least 8 channels"
	self.basecolor = image[:3]
	self.normal = self.compute_normal_map_z_component(image[3:5])
	self.height = image[5:6]
	self.roughness = image[6:7]
	self.metallic = image[7:8]

	def to_pt(self):
	# convert to pytorch tensor
	self.basecolor = self._to_pt(self.basecolor)
	self.normal = self._to_pt(self.normal)
	self.height = self._to_pt(self.height)
	self.roughness = self._to_pt(self.roughness)
	self.metallic = self._to_pt(self.metallic)

	def to_np(self):
	# convert to numpy
	self.basecolor = self._to_numpy(self.basecolor)
	self.normal = self._to_numpy(self.normal)
	self.height = self._to_numpy(self.height)
	self.roughness = self._to_numpy(self.roughness)
	self.metallic = self._to_numpy(self.metallic)

	def to_pil(self):
	# convert to PIL image
	self.basecolor = self._to_pil(self.basecolor)
	self.normal = self._to_pil(self.normal)
	self.height = self._to_pil(self.height)
	self.roughness = self._to_pil(self.roughness)
	self.metallic = self._to_pil(self.metallic)

	def as_dict(self):
	return {
	"basecolor": self.basecolor,
	"normal": self.normal,
	"height": self.height,
	"roughness": self.roughness,
	"metallic": self.metallic,
	}


	@dataclass
	class MatForgerPipelineOutput(BaseOutput):
	"""
	Output class for Stable Diffusion pipelines.

	Args:
	images (`List[PIL.Image.Image]` or `np.ndarray`)
	List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
	num_channels)`.
	"""

	images: List[MatForgerMaterial]


	class MatForgerPipeline(DiffusionPipeline, FromSingleFileMixin):

	model_cpu_offload_seq = "prompt_encoder->unet->vae"

	def __init__(
	self,
	vae: AutoencoderKL,
	unet: UNet2DConditionModel,
	prompt_encoder: nn.Module,
	scheduler: KarrasDiffusionSchedulers,
	):
	super().__init__()

	self.register_modules(
	vae=vae,
	unet=unet,
	prompt_encoder=prompt_encoder,
	scheduler=scheduler,
	)

	self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
	self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)

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

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

	def enable_vae_tiling(self):
	r"""
	Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
	compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
	processing larger images.
	"""
	self.vae.enable_tiling()

	def disable_vae_tiling(self):
	r"""
	Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
	computing decoding in one step.
	"""
	self.vae.disable_tiling()

	def encode_prompt(
	self,
	prompt,
	device,
	num_images_per_prompt,
	do_classifier_free_guidance,
	negative_prompt=None,
	prompt_embeds: Optional[torch.FloatTensor] = None,
	negative_prompt_embeds: Optional[torch.FloatTensor] = None,
	):
	r"""
	Encodes the prompt into text encoder hidden states.

	Args:
	prompt (`str` or `List[str]`, optional):
	prompt to be encoded
	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`).
	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.
	"""
	if (
	prompt is not None
	and isinstance(prompt, str)
	or isinstance(prompt, Image.Image)
	):
	batch_size = 1
	elif prompt is not None and isinstance(prompt, list):
	batch_size = len(prompt)
	else:
	batch_size = prompt_embeds.shape[0]

	if prompt_embeds is None:
	prompt_embeds = self.prompt_encoder.encode_prompt(prompt)

	if self.prompt_encoder is not None:
	prompt_embeds_dtype = self.prompt_encoder.dtype
	elif self.unet is not None:
	prompt_embeds_dtype = self.unet.dtype
	else:
	prompt_embeds_dtype = prompt_embeds.dtype

	prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_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 and negative_prompt_embeds is None:
	negative_prompt_embeds = self.prompt_encoder.encode_prompt(
	[""] * batch_size # TODO: Make this customizable
	)
	# get unconditional embeddings for classifier free guidance
	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=prompt_embeds_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
	)

	return prompt_embeds, negative_prompt_embeds

	def decode_latents(self, latents):
	deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
	deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)

	latents = 1 / self.vae.config.scaling_factor * latents
	image = self.vae.decode(latents, return_dict=False)[0]
	image = (image / 2 + 0.5).clamp(0, 1)
	# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
	image = image.cpu().permute(0, 2, 3, 1).float().numpy()
	return image

	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,
	height,
	width,
	negative_prompt=None,
	prompt_embeds=None,
	negative_prompt_embeds=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 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 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, list, Image.Image))):
	raise ValueError(
	f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
	)

	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."
	)

	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}."
	)

	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 enable_freeu(self, s1: float, s2: float, b1: float, b2: float):
	r"""Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.

	The suffixes after the scaling factors represent the stages where they are being applied.

	Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values
	that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.

	Args:
	s1 (`float`):
	Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
	mitigate "oversmoothing effect" in the enhanced denoising process.
	s2 (`float`):
	Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
	mitigate "oversmoothing effect" in the enhanced denoising process.
	b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
	b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
	"""
	if not hasattr(self, "unet"):
	raise ValueError("The pipeline must have `unet` for using FreeU.")
	self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2)

	def disable_freeu(self):
	"""Disables the FreeU mechanism if enabled."""
	self.unet.disable_freeu()

	# Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
	def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
	"""
	See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298

	Args:
	timesteps (`torch.Tensor`):
	generate embedding vectors at these timesteps
	embedding_dim (`int`, optional, defaults to 512):
	dimension of the embeddings to generate
	dtype:
	data type of the generated embeddings

	Returns:
	`torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
	"""
	assert len(w.shape) == 1
	w = w * 1000.0

	half_dim = embedding_dim // 2
	emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
	emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
	emb = w.to(dtype)[:, None] * emb[None, :]
	emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
	if embedding_dim % 2 == 1: # zero pad
	emb = torch.nn.functional.pad(emb, (0, 1))
	assert emb.shape == (w.shape[0], embedding_dim)
	return emb

	# def patch image
	def patch_image(
	self,
	image: torch.FloatTensor,
	patch_size: int,
	overlap: float = 0.5,
	) -> torch.FloatTensor:
	r"""
	Patch the input image into smaller patches.

	Args:
	image (`torch.Tensor`):
	The input image tensor to be patched. The tensor should have shape `(B, C, H, W)`.
	patch_size (`int`):
	The size of the patch.
	overlap (`float`, optional, defaults to `0.25`):
	The overlap between patches.

	Returns:
	`torch.Tensor`:
	The patched image tensor.
	"""
	# Get the number of channels
	B, C, H, W = image.shape

	# Calculate the stride for unfolding
	stride = int(patch_size * (1 - overlap))

	# Calculate required padding for height and width
	pad_height = (H - patch_size) % stride
	pad_width = (W - patch_size) % stride

	# Adjust padding to fully cover the image dimensions
	if pad_height > 0:
	pad_height = stride - pad_height
	if pad_width > 0:
	pad_width = stride - pad_width

	# Apply padding symmetrically to the bottom and right sides
	image = F.pad(image, (0, pad_width, 0, pad_height), mode="circular", value=0)
	H_padded, W_padded = image.shape[-2:]

	# Unfold the padded image tensor into patches
	image = image.unfold(2, patch_size, stride).unfold(3, patch_size, stride)

	image = image.permute(0, 2, 3, 1, 4, 5)
	image = image.reshape(-1, C, patch_size, patch_size)
	return image, (H_padded, W_padded)

	# def unpatch image with overlap
	def unpatch_image(
	self,
	patches: torch.FloatTensor,
	batch_size: int,
	output_size: Tuple[int, int],
	patch_size: int,
	crop_size: Optional[Tuple[int, int]] = None,
	overlap: float = 0.25,
	) -> torch.FloatTensor:
	"""
	Reconstruct the original image from its patches using fold, averaging the overlaps.

	Args:
	patches (torch.Tensor): The patches of the image with shape `(B, C, H, W)`,
	where `B` is the effective batch size (number of patches),
	`C` is the channel depth, and `H`, `W` are the patch height and width.
	batch_size (int): The effective batch size (number of patches).
	output_size (tuple): The height and width of the original image before patching.
	patch_size (int): The height and width of each patch (assuming square patches).
	crop_size (tuple, optional): The height and width of the cropped image.
	overlap (`float`, optional, defaults to `0.25`):
	The overlap between patches.

	Returns:
	torch.Tensor: The reconstructed images of shape `(B, C, H, W)`.
	"""
	# Set crop size if not provided
	if crop_size is None:
	crop_size = output_size

	# Calculate the stride for folding
	stride = int(patch_size * (1 - overlap))

	# Calculate the number of patches per image
	num_patches_per_image = patches.shape[0] // batch_size

	patches = patches.view(
	batch_size, num_patches_per_image, patches.shape[1], patch_size, patch_size
	)
	patches = patches.permute(0, 2, 3, 4, 1).contiguous()
	patches = patches.view(
	batch_size, patches.shape[1] * patch_size * patch_size, -1
	)

	# Use fold to reconstruct the images
	reconstructed = F.fold(
	patches, output_size=output_size, kernel_size=patch_size, stride=stride
	)

	# For averaging the overlaps, create a tensor of ones and fold it
	mask = torch.ones_like(patches)
	mask = F.fold(
	mask, output_size=output_size, kernel_size=patch_size, stride=stride
	)

	# Average the accumulated values in the overlaps
	reconstructed /= mask

	# Crop the reconstructed image to the desired size
	reconstructed = reconstructed[..., : crop_size[0], : crop_size[1]]

	return reconstructed

	@property
	def guidance_scale(self):
	return self._guidance_scale

	@property
	def guidance_rescale(self):
	return self._guidance_rescale

	# 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.
	@property
	def do_classifier_free_guidance(self):
	return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None

	@property
	def cross_attention_kwargs(self):
	return self._cross_attention_kwargs

	@property
	def num_timesteps(self):
	return self._num_timesteps

	@property
	def interrupt(self):
	return self._interrupt

	@torch.no_grad()
	# @replace_example_docstring(EXAMPLE_DOC_STRING)
	def __call__(
	self,
	prompt: Union[
	str, List[str], PipelineImageInput, List[PipelineImageInput]
	] = None,
	height: Optional[int] = None,
	width: Optional[int] = None,
	tileable: bool = False,
	patched: bool = False,
	num_inference_steps: int = 50,
	timesteps: List[int] = None,
	guidance_scale: float = 7.5,
	negative_prompt: 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,
	output_type: Optional[str] = "pil",
	return_dict: bool = True,
	cross_attention_kwargs: Optional[Dict[str, Any]] = None,
	guidance_rescale: float = 0.0,
	**kwargs,
	):

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

	# 1. Check inputs. Raise error if not correct
	self.check_inputs(
	prompt,
	height,
	width,
	negative_prompt,
	prompt_embeds,
	negative_prompt_embeds,
	)

	self._guidance_scale = guidance_scale
	self._guidance_rescale = guidance_rescale
	self._cross_attention_kwargs = cross_attention_kwargs
	self._interrupt = False

	# 2. Define call parameters
	if prompt is not None and (
	isinstance(prompt, str) or isinstance(prompt, Image.Image)
	):
	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

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

	# For classifier free guidance, we need to do two forward passes.
	# Here we concatenate the unconditional and text embeddings into a single batch
	# to avoid doing two forward passes
	if self.do_classifier_free_guidance:
	prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])

	# 4. Prepare timesteps
	timesteps, num_inference_steps = retrieve_timesteps(
	self.scheduler, num_inference_steps, device, 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,
	width,
	prompt_embeds.dtype,
	device,
	generator,
	latents,
	)

	# 6. Prepare extra step kwargs.
	extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

	# 6.2 Optionally get Guidance Scale Embedding
	timestep_cond = None
	if self.unet.config.time_cond_proj_dim is not None:
	guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(
	batch_size * num_images_per_prompt
	)
	timestep_cond = self.get_guidance_scale_embedding(
	guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
	).to(device=device, dtype=latents.dtype)

	# 7. Denoising loop
	self._num_timesteps = len(timesteps)
	with self.progress_bar(total=num_inference_steps) as progress_bar:
	for i, t in enumerate(timesteps):
	if self.interrupt:
	continue

	# If patched diffusion
	if patched:
	B = latents.shape[0]
	# patch the latents
	latents, size_padded = self.patch_image(
	latents, patch_size=32, overlap=0.0
	)
	# TODO: Improve prompt repeat when patching
	Bp = latents.shape[0]
	if prompt_embeds.shape[0] != Bp * 2:
	prompt_embeds = prompt_embeds.repeat_interleave(Bp // B, dim=0)

	# expand the latents if we are doing classifier free guidance
	latent_model_input = (
	torch.cat([latents] * 2)
	if self.do_classifier_free_guidance
	else latents
	)
	latent_model_input = self.scheduler.scale_model_input(
	latent_model_input, t
	)

	# predict the noise residual
	noise_pred = self.unet(
	latent_model_input,
	t,
	encoder_hidden_states=prompt_embeds,
	timestep_cond=timestep_cond,
	cross_attention_kwargs=self.cross_attention_kwargs,
	return_dict=False,
	)[0]

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

	if self.do_classifier_free_guidance and self.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=self.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]

	if patched:
	# unpatch the latents
	latents = self.unpatch_image(
	latents, B, size_padded, patch_size=32, overlap=0.0
	)

	# noise rolling, baby!
	# Based on 5.1. in https://arxiv.org/pdf/2309.01700.pdf
	if tileable:
	roll_h = torch.randint(0, height, (1,)).item()
	roll_w = torch.randint(0, width, (1,)).item()
	latents = torch.roll(latents, shifts=(roll_h, roll_w), dims=(2, 3))

	# call the callback, if provided
	if i == len(timesteps) - 1 or (i + 1) % self.scheduler.order == 0:
	progress_bar.update()

	if not output_type == "latent":
	if tileable:
	# decode padded latent to preserve tileability
	l_height = height // self.vae_scale_factor
	l_width = width // self.vae_scale_factor
	latents = TF.center_crop(
	latents.repeat(1, 1, 3, 3), (l_height + 4, l_width + 4)
	)

	# decode the latents
	image = self.vae.decode(
	latents / self.vae.config.scaling_factor,
	return_dict=False,
	generator=generator,
	)[0]

	# crop to original size
	image = TF.center_crop(image, (height, width))
	else:
	image = latents

	image = postprocess(image, output_type=output_type)

	# Offload all models
	self.maybe_free_model_hooks()

	if not return_dict:
	return image

	return MatForgerPipelineOutput(images=image)