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) if output_type not in ["latent", "pt", "np", "pil"]: deprecation_message = ( f"the output_type {output_type} is outdated and has been set to `np`.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)