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)