# Copyright 2023 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect from typing import Any, Callable, Dict, List, Optional, Tuple, Union import numpy as np import PIL.Image import torch from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer from ...image_processor import PipelineImageInput, VaeImageProcessor from ...loaders import FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...models.attention_processor import ( AttnProcessor2_0, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor, XFormersAttnProcessor, ) from ...models.lora import adjust_lora_scale_text_encoder from ...schedulers import KarrasDiffusionSchedulers from ...utils import ( USE_PEFT_BACKEND, deprecate, is_invisible_watermark_available, is_torch_xla_available, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers, ) from ...utils.torch_utils import randn_tensor from ..pipeline_utils import DiffusionPipeline from .pipeline_output import StableDiffusionXLPipelineOutput if is_invisible_watermark_available(): from .watermark import StableDiffusionXLWatermarker if is_torch_xla_available(): import torch_xla.core.xla_model as xm XLA_AVAILABLE = True else: XLA_AVAILABLE = False logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import StableDiffusionXLInpaintPipeline >>> from diffusers.utils import load_image >>> pipe = StableDiffusionXLInpaintPipeline.from_pretrained( ... "stabilityai/stable-diffusion-xl-base-1.0", ... torch_dtype=torch.float16, ... variant="fp16", ... use_safetensors=True, ... ) >>> pipe.to("cuda") >>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" >>> mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" >>> init_image = load_image(img_url).convert("RGB") >>> mask_image = load_image(mask_url).convert("RGB") >>> prompt = "A majestic tiger sitting on a bench" >>> image = pipe( ... prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=50, strength=0.80 ... ).images[0] ``` """ # 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 def mask_pil_to_torch(mask, height, width): # preprocess mask if isinstance(mask, (PIL.Image.Image, np.ndarray)): mask = [mask] if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image): mask = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in mask] mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0) mask = mask.astype(np.float32) / 255.0 elif isinstance(mask, list) and isinstance(mask[0], np.ndarray): mask = np.concatenate([m[None, None, :] for m in mask], axis=0) mask = torch.from_numpy(mask) return mask def prepare_mask_and_masked_image(image, mask, height, width, return_image: bool = False): """ Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the ``image`` and ``1`` for the ``mask``. The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be binarized (``mask > 0.5``) and cast to ``torch.float32`` too. Args: image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint. It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width`` ``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``. mask (_type_): The mask to apply to the image, i.e. regions to inpaint. It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width`` ``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``. Raises: ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions. TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not (ot the other way around). Returns: tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4 dimensions: ``batch x channels x height x width``. """ # checkpoint. TOD(Yiyi) - need to clean this up later deprecation_message = "The prepare_mask_and_masked_image method is deprecated and will be removed in a future version. Please use VaeImageProcessor.preprocess instead" deprecate( "prepare_mask_and_masked_image", "0.30.0", deprecation_message, ) if image is None: raise ValueError("`image` input cannot be undefined.") if mask is None: raise ValueError("`mask_image` input cannot be undefined.") if isinstance(image, torch.Tensor): if not isinstance(mask, torch.Tensor): mask = mask_pil_to_torch(mask, height, width) if image.ndim == 3: image = image.unsqueeze(0) # Batch and add channel dim for single mask if mask.ndim == 2: mask = mask.unsqueeze(0).unsqueeze(0) # Batch single mask or add channel dim if mask.ndim == 3: # Single batched mask, no channel dim or single mask not batched but channel dim if mask.shape[0] == 1: mask = mask.unsqueeze(0) # Batched masks no channel dim else: mask = mask.unsqueeze(1) assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions" # assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions" assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size" # Check image is in [-1, 1] # if image.min() < -1 or image.max() > 1: # raise ValueError("Image should be in [-1, 1] range") # Check mask is in [0, 1] if mask.min() < 0 or mask.max() > 1: raise ValueError("Mask should be in [0, 1] range") # Binarize mask mask[mask < 0.5] = 0 mask[mask >= 0.5] = 1 # Image as float32 image = image.to(dtype=torch.float32) elif isinstance(mask, torch.Tensor): raise TypeError(f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not") else: # preprocess image if isinstance(image, (PIL.Image.Image, np.ndarray)): image = [image] if isinstance(image, list) and isinstance(image[0], PIL.Image.Image): # resize all images w.r.t passed height an width image = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in image] image = [np.array(i.convert("RGB"))[None, :] for i in image] image = np.concatenate(image, axis=0) elif isinstance(image, list) and isinstance(image[0], np.ndarray): image = np.concatenate([i[None, :] for i in image], axis=0) image = image.transpose(0, 3, 1, 2) image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0 mask = mask_pil_to_torch(mask, height, width) mask[mask < 0.5] = 0 mask[mask >= 0.5] = 1 if image.shape[1] == 4: # images are in latent space and thus can't # be masked set masked_image to None # we assume that the checkpoint is not an inpainting # checkpoint. TOD(Yiyi) - need to clean this up later masked_image = None else: masked_image = image * (mask < 0.5) # n.b. ensure backwards compatibility as old function does not return image if return_image: return mask, masked_image, image return mask, masked_image class StableDiffusionXLInpaintPipeline( DiffusionPipeline, TextualInversionLoaderMixin, StableDiffusionXLLoraLoaderMixin, FromSingleFileMixin ): 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*: [`loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] - *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`] as well as the following saving methods: - *LoRA*: [`loaders.StableDiffusionXLLoraLoaderMixin.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`]. requires_aesthetics_score (`bool`, *optional*, defaults to `"False"`): Whether the `unet` requires a aesthetic_score condition to be passed during inference. Also see the config of `stabilityai/stable-diffusion-xl-refiner-1-0`. 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" _optional_components = ["tokenizer", "tokenizer_2", "text_encoder", "text_encoder_2"] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, text_encoder_2: CLIPTextModelWithProjection, tokenizer: CLIPTokenizer, tokenizer_2: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, requires_aesthetics_score: bool = False, 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.register_to_config(requires_aesthetics_score=requires_aesthetics_score) 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.mask_processor = VaeImageProcessor( vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True ) 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 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing 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() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_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() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling 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() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_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() # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt 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, clip_skip: Optional[int] = 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. clip_skip (`int`, *optional*): Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings. """ 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, StableDiffusionXLLoraLoaderMixin): self._lora_scale = lora_scale # dynamically adjust the LoRA scale if self.text_encoder is not None: if not USE_PEFT_BACKEND: adjust_lora_scale_text_encoder(self.text_encoder, lora_scale) else: scale_lora_layers(self.text_encoder, lora_scale) if self.text_encoder_2 is not None: if not USE_PEFT_BACKEND: adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale) else: scale_lora_layers(self.text_encoder_2, lora_scale) prompt = [prompt] if isinstance(prompt, str) else prompt if prompt is not None: 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 prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2 # textual inversion: procecss multi-vector tokens if necessary prompt_embeds_list = [] prompts = [prompt, prompt_2] for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders): if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, tokenizer) text_inputs = tokenizer( prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1]) 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] if clip_skip is None: prompt_embeds = prompt_embeds.hidden_states[-2] else: # "2" because SDXL always indexes from the penultimate layer. prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 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 # normalize str to list negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt negative_prompt_2 = ( batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2 ) 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 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) if self.text_encoder_2 is not None: prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) else: prompt_embeds = prompt_embeds.to(dtype=self.unet.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] if self.text_encoder_2 is not None: negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) else: negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.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 ) if self.text_encoder is not None: if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: # Retrieve the original scale by scaling back the LoRA layers unscale_lora_layers(self.text_encoder, lora_scale) if self.text_encoder_2 is not None: if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: # Retrieve the original scale by scaling back the LoRA layers unscale_lora_layers(self.text_encoder_2, lora_scale) 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, strength, callback_steps, negative_prompt=None, negative_prompt_2=None, prompt_embeds=None, negative_prompt_embeds=None, ): if strength < 0 or strength > 1: raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") 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}." ) def prepare_latents( self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None, image=None, timestep=None, is_strength_max=True, add_noise=True, return_noise=False, return_image_latents=False, ): 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 (image is None or timestep is None) and not is_strength_max: raise ValueError( "Since strength < 1. initial latents are to be initialised as a combination of Image + Noise." "However, either the image or the noise timestep has not been provided." ) if image.shape[1] == 4: image_latents = image.to(device=device, dtype=dtype) elif return_image_latents or (latents is None and not is_strength_max): image = image.to(device=device, dtype=dtype) image_latents = self._encode_vae_image(image=image, generator=generator) image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1) if latents is None and add_noise: noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) # if strength is 1. then initialise the latents to noise, else initial to image + noise latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep) # if pure noise then scale the initial latents by the Scheduler's init sigma latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents elif add_noise: noise = latents.to(device) latents = noise * self.scheduler.init_noise_sigma else: noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) latents = image_latents.to(device) outputs = (latents,) if return_noise: outputs += (noise,) if return_image_latents: outputs += (image_latents,) return outputs def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator): dtype = image.dtype if self.vae.config.force_upcast: image = image.float() self.vae.to(dtype=torch.float32) if isinstance(generator, list): image_latents = [ self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i]) for i in range(image.shape[0]) ] image_latents = torch.cat(image_latents, dim=0) else: image_latents = self.vae.encode(image).latent_dist.sample(generator=generator) if self.vae.config.force_upcast: self.vae.to(dtype) image_latents = image_latents.to(dtype) image_latents = self.vae.config.scaling_factor * image_latents return image_latents def prepare_mask_latents( self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance ): # resize the mask to latents shape as we concatenate the mask to the latents # we do that before converting to dtype to avoid breaking in case we're using cpu_offload # and half precision mask = torch.nn.functional.interpolate( mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor) ) mask = mask.to(device=device, dtype=dtype) # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method if mask.shape[0] < batch_size: if not batch_size % mask.shape[0] == 0: raise ValueError( "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to" f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number" " of masks that you pass is divisible by the total requested batch size." ) mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1) mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask if masked_image is not None and masked_image.shape[1] == 4: masked_image_latents = masked_image else: masked_image_latents = None if masked_image is not None: if masked_image_latents is None: masked_image = masked_image.to(device=device, dtype=dtype) masked_image_latents = self._encode_vae_image(masked_image, generator=generator) if masked_image_latents.shape[0] < batch_size: if not batch_size % masked_image_latents.shape[0] == 0: raise ValueError( "The passed images and the required batch size don't match. Images are supposed to be duplicated" f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed." " Make sure the number of images that you pass is divisible by the total requested batch size." ) masked_image_latents = masked_image_latents.repeat( batch_size // masked_image_latents.shape[0], 1, 1, 1 ) masked_image_latents = ( torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents ) # aligning device to prevent device errors when concating it with the latent model input masked_image_latents = masked_image_latents.to(device=device, dtype=dtype) return mask, masked_image_latents # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline.get_timesteps def get_timesteps(self, num_inference_steps, strength, device, denoising_start=None): # get the original timestep using init_timestep if denoising_start is None: init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0) else: t_start = 0 timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :] # Strength is irrelevant if we directly request a timestep to start at; # that is, strength is determined by the denoising_start instead. if denoising_start is not None: discrete_timestep_cutoff = int( round( self.scheduler.config.num_train_timesteps - (denoising_start * self.scheduler.config.num_train_timesteps) ) ) num_inference_steps = (timesteps < discrete_timestep_cutoff).sum().item() if self.scheduler.order == 2 and num_inference_steps % 2 == 0: # if the scheduler is a 2nd order scheduler we might have to do +1 # because `num_inference_steps` might be even given that every timestep # (except the highest one) is duplicated. If `num_inference_steps` is even it would # mean that we cut the timesteps in the middle of the denoising step # (between 1st and 2nd devirative) which leads to incorrect results. By adding 1 # we ensure that the denoising process always ends after the 2nd derivate step of the scheduler num_inference_steps = num_inference_steps + 1 # because t_n+1 >= t_n, we slice the timesteps starting from the end timesteps = timesteps[-num_inference_steps:] return timesteps, num_inference_steps return timesteps, num_inference_steps - t_start # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline._get_add_time_ids def _get_add_time_ids( self, original_size, crops_coords_top_left, target_size, aesthetic_score, negative_aesthetic_score, negative_original_size, negative_crops_coords_top_left, negative_target_size, dtype, text_encoder_projection_dim=None, ): if self.config.requires_aesthetics_score: add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,)) add_neg_time_ids = list( negative_original_size + negative_crops_coords_top_left + (negative_aesthetic_score,) ) else: add_time_ids = list(original_size + crops_coords_top_left + target_size) add_neg_time_ids = list(negative_original_size + crops_coords_top_left + negative_target_size) passed_add_embed_dim = ( self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim ) expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features if ( expected_add_embed_dim > passed_add_embed_dim and (expected_add_embed_dim - passed_add_embed_dim) == self.unet.config.addition_time_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. Please make sure to enable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=True)` to make sure `aesthetic_score` {aesthetic_score} and `negative_aesthetic_score` {negative_aesthetic_score} is correctly used by the model." ) elif ( expected_add_embed_dim < passed_add_embed_dim and (passed_add_embed_dim - expected_add_embed_dim) == self.unet.config.addition_time_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. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the model." ) elif 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) add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype) return add_time_ids, add_neg_time_ids # 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) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_freeu 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) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_freeu def disable_freeu(self): """Disables the FreeU mechanism if enabled.""" self.unet.disable_freeu() @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, image: PipelineImageInput = None, mask_image: PipelineImageInput = None, masked_image_latents: torch.FloatTensor = None, height: Optional[int] = None, width: Optional[int] = None, strength: float = 0.9999, num_inference_steps: int = 50, denoising_start: Optional[float] = None, denoising_end: Optional[float] = None, guidance_scale: float = 7.5, 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 = True, 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: Tuple[int, int] = None, crops_coords_top_left: Tuple[int, int] = (0, 0), target_size: 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, aesthetic_score: float = 6.0, negative_aesthetic_score: float = 2.5, clip_skip: Optional[int] = None, ): 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 image (`PIL.Image.Image`): `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will be masked out with `mask_image` and repainted according to `prompt`. mask_image (`PIL.Image.Image`): `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`. 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. strength (`float`, *optional*, defaults to 0.9999): Conceptually, indicates how much to transform the masked portion of the reference `image`. Must be between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores the masked portion of the reference `image`. Note that in the case of `denoising_start` being declared as an integer, the value of `strength` will be ignored. 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_start (`float`, *optional*): When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and it is assumed that the passed `image` is a partly denoised image. Note that when this is specified, strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline is integrated into a "Mixture of Denoisers" multi-pipeline setup, as detailed in [**Refining the Image Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output). 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 (ca. final 20% of timesteps still needed) and should be denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the final 20% of 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 7.5): 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 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. 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`, *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`. 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.StableDiffusionPipelineOutput`] 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). 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 `(height, width)` 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 `(height, width)`. 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. aesthetic_score (`float`, *optional*, defaults to 6.0): Used to simulate an aesthetic score of the generated image by influencing the positive text condition. 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_aesthetic_score (`float`, *optional*, defaults to 2.5): 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). Can be used to simulate an aesthetic score of the generated image by influencing the negative text condition. clip_skip (`int`, *optional*): Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings. Examples: Returns: [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a `tuple. `tuple. When returning a tuple, the first element is a list with the generated images. """ # 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 self.check_inputs( prompt, prompt_2, height, width, strength, callback_steps, negative_prompt, negative_prompt_2, prompt_embeds, negative_prompt_embeds, ) # 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, clip_skip=clip_skip, ) # 4. set timesteps def denoising_value_valid(dnv): return isinstance(denoising_end, float) and 0 < dnv < 1 self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps, num_inference_steps = self.get_timesteps( num_inference_steps, strength, device, denoising_start=denoising_start if denoising_value_valid else None ) # check that number of inference steps is not < 1 - as this doesn't make sense if num_inference_steps < 1: raise ValueError( f"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline" f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline." ) # at which timestep to set the initial noise (n.b. 50% if strength is 0.5) latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise is_strength_max = strength == 1.0 # 5. Preprocess mask and image init_image = self.image_processor.preprocess(image, height=height, width=width) init_image = init_image.to(dtype=torch.float32) mask = self.mask_processor.preprocess(mask_image, height=height, width=width) if masked_image_latents is not None: masked_image = masked_image_latents elif init_image.shape[1] == 4: # if images are in latent space, we can't mask it masked_image = None else: masked_image = init_image * (mask < 0.5) # 6. Prepare latent variables num_channels_latents = self.vae.config.latent_channels num_channels_unet = self.unet.config.in_channels return_image_latents = num_channels_unet == 4 add_noise = True if denoising_start is None else False latents_outputs = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, image=init_image, timestep=latent_timestep, is_strength_max=is_strength_max, add_noise=add_noise, return_noise=True, return_image_latents=return_image_latents, ) if return_image_latents: latents, noise, image_latents = latents_outputs else: latents, noise = latents_outputs # 7. Prepare mask latent variables mask, masked_image_latents = self.prepare_mask_latents( mask, masked_image, batch_size * num_images_per_prompt, height, width, prompt_embeds.dtype, device, generator, do_classifier_free_guidance, ) # 8. Check that sizes of mask, masked image and latents match if num_channels_unet == 9: # default case for runwayml/stable-diffusion-inpainting num_channels_mask = mask.shape[1] num_channels_masked_image = masked_image_latents.shape[1] if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels: raise ValueError( f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects" f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +" f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}" f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of" " `pipeline.unet` or your `mask_image` or `image` input." ) elif num_channels_unet != 4: raise ValueError( f"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}." ) # 8.1 Prepare extra step kwargs. extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline height, width = latents.shape[-2:] height = height * self.vae_scale_factor width = width * self.vae_scale_factor original_size = original_size or (height, width) target_size = target_size or (height, width) # 10. Prepare added time ids & embeddings if negative_original_size is None: negative_original_size = original_size if negative_target_size is None: negative_target_size = target_size add_text_embeds = pooled_prompt_embeds if self.text_encoder_2 is None: text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1]) else: text_encoder_projection_dim = self.text_encoder_2.config.projection_dim add_time_ids, add_neg_time_ids = self._get_add_time_ids( original_size, crops_coords_top_left, target_size, aesthetic_score, negative_aesthetic_score, negative_original_size, negative_crops_coords_top_left, negative_target_size, dtype=prompt_embeds.dtype, text_encoder_projection_dim=text_encoder_projection_dim, ) add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1) 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_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1) add_time_ids = torch.cat([add_neg_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) # 11. Denoising loop num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) if ( denoising_end is not None and denoising_start is not None and denoising_value_valid(denoising_end) and denoising_value_valid(denoising_start) and denoising_start >= denoising_end ): raise ValueError( f"`denoising_start`: {denoising_start} cannot be larger than or equal to `denoising_end`: " + f" {denoising_end} when using type float." ) elif denoising_end is not None and denoising_value_valid(denoising_end): 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] with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents # concat latents, mask, masked_image_latents in the channel dimension latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) if num_channels_unet == 9: latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1) # 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.chunk(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] if num_channels_unet == 4: init_latents_proper = image_latents if do_classifier_free_guidance: init_mask, _ = mask.chunk(2) else: init_mask = mask if i < len(timesteps) - 1: noise_timestep = timesteps[i + 1] init_latents_proper = self.scheduler.add_noise( init_latents_proper, noise, torch.tensor([noise_timestep]) ) latents = (1 - init_mask) * init_latents_proper + init_mask * latents # 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) if XLA_AVAILABLE: xm.mark_step() 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) 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: return StableDiffusionXLPipelineOutput(images=latents) # apply watermark if available if self.watermark is not None: image = self.watermark.apply_watermark(image) image = self.image_processor.postprocess(image, output_type=output_type) # Offload all models self.maybe_free_model_hooks() if not return_dict: return (image,) return StableDiffusionXLPipelineOutput(images=image)