# 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 import math from typing import Any, Callable, Dict, List, Optional, Tuple, Union import torch import torch.nn.functional as F from transformers import ( CLIPImageProcessor, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection, ) from ...image_processor import PipelineImageInput, VaeImageProcessor from ...loaders import ( FromSingleFileMixin, IPAdapterMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin, ) from ...models import AutoencoderKL, UNet2DConditionModel from ...models.attention_processor import ( Attention, AttnProcessor, AttnProcessor2_0, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor, XFormersAttnProcessor, ) from ...models.lora import adjust_lora_scale_text_encoder from ...schedulers import DDIMScheduler, DPMSolverMultistepScheduler from ...utils import ( USE_PEFT_BACKEND, 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 LEditsPPDiffusionPipelineOutput, LEditsPPInversionPipelineOutput if is_invisible_watermark_available(): from ..stable_diffusion_xl.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 >>> import PIL >>> import requests >>> from io import BytesIO >>> from diffusers import LEditsPPPipelineStableDiffusionXL >>> pipe = LEditsPPPipelineStableDiffusionXL.from_pretrained( ... "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16 ... ) >>> pipe = pipe.to("cuda") >>> def download_image(url): ... response = requests.get(url) ... return PIL.Image.open(BytesIO(response.content)).convert("RGB") >>> img_url = "https://www.aiml.informatik.tu-darmstadt.de/people/mbrack/tennis.jpg" >>> image = download_image(img_url) >>> _ = pipe.invert(image=image, num_inversion_steps=50, skip=0.2) >>> edited_image = pipe( ... editing_prompt=["tennis ball", "tomato"], ... reverse_editing_direction=[True, False], ... edit_guidance_scale=[5.0, 10.0], ... edit_threshold=[0.9, 0.85], ... ).images[0] ``` """ # Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LeditsAttentionStore class LeditsAttentionStore: @staticmethod def get_empty_store(): return {"down_cross": [], "mid_cross": [], "up_cross": [], "down_self": [], "mid_self": [], "up_self": []} def __call__(self, attn, is_cross: bool, place_in_unet: str, editing_prompts, PnP=False): # attn.shape = batch_size * head_size, seq_len query, seq_len_key if attn.shape[1] <= self.max_size: bs = 1 + int(PnP) + editing_prompts skip = 2 if PnP else 1 # skip PnP & unconditional attn = torch.stack(attn.split(self.batch_size)).permute(1, 0, 2, 3) source_batch_size = int(attn.shape[1] // bs) self.forward(attn[:, skip * source_batch_size :], is_cross, place_in_unet) def forward(self, attn, is_cross: bool, place_in_unet: str): key = f"{place_in_unet}_{'cross' if is_cross else 'self'}" self.step_store[key].append(attn) def between_steps(self, store_step=True): if store_step: if self.average: if len(self.attention_store) == 0: self.attention_store = self.step_store else: for key in self.attention_store: for i in range(len(self.attention_store[key])): self.attention_store[key][i] += self.step_store[key][i] else: if len(self.attention_store) == 0: self.attention_store = [self.step_store] else: self.attention_store.append(self.step_store) self.cur_step += 1 self.step_store = self.get_empty_store() def get_attention(self, step: int): if self.average: attention = { key: [item / self.cur_step for item in self.attention_store[key]] for key in self.attention_store } else: assert step is not None attention = self.attention_store[step] return attention def aggregate_attention( self, attention_maps, prompts, res: Union[int, Tuple[int]], from_where: List[str], is_cross: bool, select: int ): out = [[] for x in range(self.batch_size)] if isinstance(res, int): num_pixels = res**2 resolution = (res, res) else: num_pixels = res[0] * res[1] resolution = res[:2] for location in from_where: for bs_item in attention_maps[f"{location}_{'cross' if is_cross else 'self'}"]: for batch, item in enumerate(bs_item): if item.shape[1] == num_pixels: cross_maps = item.reshape(len(prompts), -1, *resolution, item.shape[-1])[select] out[batch].append(cross_maps) out = torch.stack([torch.cat(x, dim=0) for x in out]) # average over heads out = out.sum(1) / out.shape[1] return out def __init__(self, average: bool, batch_size=1, max_resolution=16, max_size: int = None): self.step_store = self.get_empty_store() self.attention_store = [] self.cur_step = 0 self.average = average self.batch_size = batch_size if max_size is None: self.max_size = max_resolution**2 elif max_size is not None and max_resolution is None: self.max_size = max_size else: raise ValueError("Only allowed to set one of max_resolution or max_size") # Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LeditsGaussianSmoothing class LeditsGaussianSmoothing: def __init__(self, device): kernel_size = [3, 3] sigma = [0.5, 0.5] # The gaussian kernel is the product of the gaussian function of each dimension. kernel = 1 meshgrids = torch.meshgrid([torch.arange(size, dtype=torch.float32) for size in kernel_size]) for size, std, mgrid in zip(kernel_size, sigma, meshgrids): mean = (size - 1) / 2 kernel *= 1 / (std * math.sqrt(2 * math.pi)) * torch.exp(-(((mgrid - mean) / (2 * std)) ** 2)) # Make sure sum of values in gaussian kernel equals 1. kernel = kernel / torch.sum(kernel) # Reshape to depthwise convolutional weight kernel = kernel.view(1, 1, *kernel.size()) kernel = kernel.repeat(1, *[1] * (kernel.dim() - 1)) self.weight = kernel.to(device) def __call__(self, input): """ Arguments: Apply gaussian filter to input. input (torch.Tensor): Input to apply gaussian filter on. Returns: filtered (torch.Tensor): Filtered output. """ return F.conv2d(input, weight=self.weight.to(input.dtype)) # Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LEDITSCrossAttnProcessor class LEDITSCrossAttnProcessor: def __init__(self, attention_store, place_in_unet, pnp, editing_prompts): self.attnstore = attention_store self.place_in_unet = place_in_unet self.editing_prompts = editing_prompts self.pnp = pnp def __call__( self, attn: Attention, hidden_states, encoder_hidden_states, attention_mask=None, temb=None, ): batch_size, sequence_length, _ = ( hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape ) attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) query = attn.to_q(hidden_states) if encoder_hidden_states is None: encoder_hidden_states = hidden_states elif attn.norm_cross: encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) key = attn.to_k(encoder_hidden_states) value = attn.to_v(encoder_hidden_states) query = attn.head_to_batch_dim(query) key = attn.head_to_batch_dim(key) value = attn.head_to_batch_dim(value) attention_probs = attn.get_attention_scores(query, key, attention_mask) self.attnstore( attention_probs, is_cross=True, place_in_unet=self.place_in_unet, editing_prompts=self.editing_prompts, PnP=self.pnp, ) hidden_states = torch.bmm(attention_probs, value) hidden_states = attn.batch_to_head_dim(hidden_states) # linear proj hidden_states = attn.to_out[0](hidden_states) # dropout hidden_states = attn.to_out[1](hidden_states) hidden_states = hidden_states / attn.rescale_output_factor return hidden_states class LEditsPPPipelineStableDiffusionXL( DiffusionPipeline, FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin, IPAdapterMixin, ): """ Pipeline for textual image editing using LEDits++ with Stable Diffusion XL. This model inherits from [`DiffusionPipeline`] and builds on the [`StableDiffusionXLPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.). In addition the pipeline inherits the following loading methods: - *LoRA*: [`LEditsPPPipelineStableDiffusionXL.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 ([`~transformers.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 ([`~transformers.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 ([`~transformers.CLIPTokenizer`]): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). tokenizer_2 ([`~transformers.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 ([`DPMSolverMultistepScheduler`] or [`DDIMScheduler`]): A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of [`DPMSolverMultistepScheduler`] or [`DDIMScheduler`]. If any other scheduler is passed it will automatically be set to [`DPMSolverMultistepScheduler`]. 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", "image_encoder", "feature_extractor", ] _callback_tensor_inputs = [ "latents", "prompt_embeds", "negative_prompt_embeds", "add_text_embeds", "add_time_ids", "negative_pooled_prompt_embeds", "negative_add_time_ids", ] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, text_encoder_2: CLIPTextModelWithProjection, tokenizer: CLIPTokenizer, tokenizer_2: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: Union[DPMSolverMultistepScheduler, DDIMScheduler], image_encoder: CLIPVisionModelWithProjection = None, feature_extractor: CLIPImageProcessor = None, 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, image_encoder=image_encoder, feature_extractor=feature_extractor, ) 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) if not isinstance(scheduler, DDIMScheduler) and not isinstance(scheduler, DPMSolverMultistepScheduler): self.scheduler = DPMSolverMultistepScheduler.from_config( scheduler.config, algorithm_type="sde-dpmsolver++", solver_order=2 ) logger.warning( "This pipeline only supports DDIMScheduler and DPMSolverMultistepScheduler. " "The scheduler has been changed to DPMSolverMultistepScheduler." ) 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 self.inversion_steps = None def encode_prompt( self, device: Optional[torch.device] = None, num_images_per_prompt: int = 1, negative_prompt: Optional[str] = None, negative_prompt_2: Optional[str] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, negative_pooled_prompt_embeds: Optional[torch.Tensor] = None, lora_scale: Optional[float] = None, clip_skip: Optional[int] = None, enable_edit_guidance: bool = True, editing_prompt: Optional[str] = None, editing_prompt_embeds: Optional[torch.Tensor] = None, editing_pooled_prompt_embeds: Optional[torch.Tensor] = None, ) -> object: r""" Encodes the prompt into text encoder hidden states. Args: device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt 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. 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 negative_prompt_embeds (`torch.Tensor`, *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. negative_pooled_prompt_embeds (`torch.Tensor`, *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. enable_edit_guidance (`bool`): Whether to guide towards an editing prompt or not. editing_prompt (`str` or `List[str]`, *optional*): Editing prompt(s) to be encoded. If not defined and 'enable_edit_guidance' is True, one has to pass `editing_prompt_embeds` instead. editing_prompt_embeds (`torch.Tensor`, *optional*): Pre-generated edit text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided and 'enable_edit_guidance' is True, editing_prompt_embeds will be generated from `editing_prompt` input argument. editing_pooled_prompt_embeds (`torch.Tensor`, *optional*): Pre-generated edit pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, pooled editing_pooled_prompt_embeds will be generated from `editing_prompt` input argument. """ 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) batch_size = self.batch_size # 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] ) num_edit_tokens = 0 # get unconditional embeddings for classifier free guidance zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt if 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 batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but image inversion " f" has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of the input images." ) 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) uncond_input = tokenizer( negative_prompt, padding="max_length", max_length=tokenizer.model_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 zero_out_negative_prompt: negative_prompt_embeds = torch.zeros_like(negative_prompt_embeds) negative_pooled_prompt_embeds = torch.zeros_like(negative_pooled_prompt_embeds) if enable_edit_guidance and editing_prompt_embeds is None: editing_prompt_2 = editing_prompt editing_prompts = [editing_prompt, editing_prompt_2] edit_prompt_embeds_list = [] for editing_prompt, tokenizer, text_encoder in zip(editing_prompts, tokenizers, text_encoders): if isinstance(self, TextualInversionLoaderMixin): editing_prompt = self.maybe_convert_prompt(editing_prompt, tokenizer) max_length = negative_prompt_embeds.shape[1] edit_concepts_input = tokenizer( # [x for item in editing_prompt for x in repeat(item, batch_size)], editing_prompt, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", return_length=True, ) num_edit_tokens = edit_concepts_input.length - 2 edit_concepts_embeds = text_encoder( edit_concepts_input.input_ids.to(device), output_hidden_states=True, ) # We are only ALWAYS interested in the pooled output of the final text encoder editing_pooled_prompt_embeds = edit_concepts_embeds[0] if clip_skip is None: edit_concepts_embeds = edit_concepts_embeds.hidden_states[-2] else: # "2" because SDXL always indexes from the penultimate layer. edit_concepts_embeds = edit_concepts_embeds.hidden_states[-(clip_skip + 2)] edit_prompt_embeds_list.append(edit_concepts_embeds) edit_concepts_embeds = torch.concat(edit_prompt_embeds_list, dim=-1) elif not enable_edit_guidance: edit_concepts_embeds = None editing_pooled_prompt_embeds = None negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) bs_embed, seq_len, _ = negative_prompt_embeds.shape # 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) if enable_edit_guidance: bs_embed_edit, seq_len, _ = edit_concepts_embeds.shape edit_concepts_embeds = edit_concepts_embeds.to(dtype=self.text_encoder_2.dtype, device=device) edit_concepts_embeds = edit_concepts_embeds.repeat(1, num_images_per_prompt, 1) edit_concepts_embeds = edit_concepts_embeds.view(bs_embed_edit * num_images_per_prompt, seq_len, -1) negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( bs_embed * num_images_per_prompt, -1 ) if enable_edit_guidance: editing_pooled_prompt_embeds = editing_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( bs_embed_edit * 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 ( negative_prompt_embeds, edit_concepts_embeds, negative_pooled_prompt_embeds, editing_pooled_prompt_embeds, num_edit_tokens, ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, eta, generator=None): # 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, negative_prompt=None, negative_prompt_2=None, negative_prompt_embeds=None, negative_pooled_prompt_embeds=None, ): 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 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`." ) # Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def prepare_latents(self, device, latents): 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, text_encoder_projection_dim=None ): 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) + 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: 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 # 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.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding def get_guidance_scale_embedding( self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32 ) -> torch.Tensor: """ See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298 Args: w (`torch.Tensor`): Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings. embedding_dim (`int`, *optional*, defaults to 512): Dimension of the embeddings to generate. dtype (`torch.dtype`, *optional*, defaults to `torch.float32`): Data type of the generated embeddings. Returns: `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`. """ assert len(w.shape) == 1 w = w * 1000.0 half_dim = embedding_dim // 2 emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb) emb = w.to(dtype)[:, None] * emb[None, :] emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) if embedding_dim % 2 == 1: # zero pad emb = torch.nn.functional.pad(emb, (0, 1)) assert emb.shape == (w.shape[0], embedding_dim) return emb @property def guidance_scale(self): return self._guidance_scale @property def guidance_rescale(self): return self._guidance_rescale @property def clip_skip(self): return self._clip_skip # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. @property def do_classifier_free_guidance(self): return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None @property def cross_attention_kwargs(self): return self._cross_attention_kwargs @property def denoising_end(self): return self._denoising_end @property def num_timesteps(self): return self._num_timesteps # Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LEditsPPPipelineStableDiffusion.prepare_unet def prepare_unet(self, attention_store, PnP: bool = False): attn_procs = {} for name in self.unet.attn_processors.keys(): if name.startswith("mid_block"): place_in_unet = "mid" elif name.startswith("up_blocks"): place_in_unet = "up" elif name.startswith("down_blocks"): place_in_unet = "down" else: continue if "attn2" in name and place_in_unet != "mid": attn_procs[name] = LEDITSCrossAttnProcessor( attention_store=attention_store, place_in_unet=place_in_unet, pnp=PnP, editing_prompts=self.enabled_editing_prompts, ) else: attn_procs[name] = AttnProcessor() self.unet.set_attn_processor(attn_procs) @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, denoising_end: Optional[float] = None, negative_prompt: Optional[Union[str, List[str]]] = None, negative_prompt_2: Optional[Union[str, List[str]]] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, negative_pooled_prompt_embeds: Optional[torch.Tensor] = None, ip_adapter_image: Optional[PipelineImageInput] = None, output_type: Optional[str] = "pil", return_dict: bool = True, cross_attention_kwargs: Optional[Dict[str, Any]] = None, guidance_rescale: float = 0.0, crops_coords_top_left: Tuple[int, int] = (0, 0), target_size: Optional[Tuple[int, int]] = None, editing_prompt: Optional[Union[str, List[str]]] = None, editing_prompt_embeddings: Optional[torch.Tensor] = None, editing_pooled_prompt_embeds: Optional[torch.Tensor] = None, reverse_editing_direction: Optional[Union[bool, List[bool]]] = False, edit_guidance_scale: Optional[Union[float, List[float]]] = 5, edit_warmup_steps: Optional[Union[int, List[int]]] = 0, edit_cooldown_steps: Optional[Union[int, List[int]]] = None, edit_threshold: Optional[Union[float, List[float]]] = 0.9, sem_guidance: Optional[List[torch.Tensor]] = None, use_cross_attn_mask: bool = False, use_intersect_mask: bool = False, user_mask: Optional[torch.Tensor] = None, attn_store_steps: Optional[List[int]] = [], store_averaged_over_steps: bool = True, clip_skip: Optional[int] = None, callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, callback_on_step_end_tensor_inputs: List[str] = ["latents"], **kwargs, ): r""" The call function to the pipeline for editing. The [`~pipelines.ledits_pp.LEditsPPPipelineStableDiffusionXL.invert`] method has to be called beforehand. Edits will always be performed for the last inverted image(s). Args: 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 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 negative_prompt_embeds (`torch.Tensor`, *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. negative_pooled_prompt_embeds (`torch.Tensor`, *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. ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. 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.Tensor)`. 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. 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). editing_prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. The image is reconstructed by setting `editing_prompt = None`. Guidance direction of prompt should be specified via `reverse_editing_direction`. editing_prompt_embeddings (`torch.Tensor`, *optional*): Pre-generated edit text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, editing_prompt_embeddings will be generated from `editing_prompt` input argument. editing_pooled_prompt_embeddings (`torch.Tensor`, *optional*): Pre-generated pooled edit text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, editing_prompt_embeddings will be generated from `editing_prompt` input argument. reverse_editing_direction (`bool` or `List[bool]`, *optional*, defaults to `False`): Whether the corresponding prompt in `editing_prompt` should be increased or decreased. edit_guidance_scale (`float` or `List[float]`, *optional*, defaults to 5): Guidance scale for guiding the image generation. If provided as list values should correspond to `editing_prompt`. `edit_guidance_scale` is defined as `s_e` of equation 12 of [LEDITS++ Paper](https://arxiv.org/abs/2301.12247). edit_warmup_steps (`float` or `List[float]`, *optional*, defaults to 10): Number of diffusion steps (for each prompt) for which guidance is not applied. edit_cooldown_steps (`float` or `List[float]`, *optional*, defaults to `None`): Number of diffusion steps (for each prompt) after which guidance is no longer applied. edit_threshold (`float` or `List[float]`, *optional*, defaults to 0.9): Masking threshold of guidance. Threshold should be proportional to the image region that is modified. 'edit_threshold' is defined as 'λ' of equation 12 of [LEDITS++ Paper](https://arxiv.org/abs/2301.12247). sem_guidance (`List[torch.Tensor]`, *optional*): List of pre-generated guidance vectors to be applied at generation. Length of the list has to correspond to `num_inference_steps`. use_cross_attn_mask: Whether cross-attention masks are used. Cross-attention masks are always used when use_intersect_mask is set to true. Cross-attention masks are defined as 'M^1' of equation 12 of [LEDITS++ paper](https://arxiv.org/pdf/2311.16711.pdf). use_intersect_mask: Whether the masking term is calculated as intersection of cross-attention masks and masks derived from the noise estimate. Cross-attention mask are defined as 'M^1' and masks derived from the noise estimate are defined as 'M^2' of equation 12 of [LEDITS++ paper](https://arxiv.org/pdf/2311.16711.pdf). user_mask: User-provided mask for even better control over the editing process. This is helpful when LEDITS++'s implicit masks do not meet user preferences. attn_store_steps: Steps for which the attention maps are stored in the AttentionStore. Just for visualization purposes. store_averaged_over_steps: Whether the attention maps for the 'attn_store_steps' are stored averaged over the diffusion steps. If False, attention maps for each step are stores separately. Just for visualization purposes. 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. callback_on_step_end (`Callable`, *optional*): A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by `callback_on_step_end_tensor_inputs`. callback_on_step_end_tensor_inputs (`List`, *optional*): The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the `._callback_tensor_inputs` attribute of your pipeline class. Examples: Returns: [`~pipelines.ledits_pp.LEditsPPDiffusionPipelineOutput`] or `tuple`: [`~pipelines.ledits_pp.LEditsPPDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images. """ if self.inversion_steps is None: raise ValueError( "You need to invert an input image first before calling the pipeline. The `invert` method has to be called beforehand. Edits will always be performed for the last inverted image(s)." ) eta = self.eta num_images_per_prompt = 1 latents = self.init_latents zs = self.zs self.scheduler.set_timesteps(len(self.scheduler.timesteps)) if use_intersect_mask: use_cross_attn_mask = True if use_cross_attn_mask: self.smoothing = LeditsGaussianSmoothing(self.device) if user_mask is not None: user_mask = user_mask.to(self.device) # TODO: Check inputs # 1. Check inputs. Raise error if not correct # self.check_inputs( # callback_steps, # negative_prompt, # negative_prompt_2, # prompt_embeds, # negative_prompt_embeds, # pooled_prompt_embeds, # negative_pooled_prompt_embeds, # ) self._guidance_rescale = guidance_rescale self._clip_skip = clip_skip self._cross_attention_kwargs = cross_attention_kwargs self._denoising_end = denoising_end # 2. Define call parameters batch_size = self.batch_size device = self._execution_device if editing_prompt: enable_edit_guidance = True if isinstance(editing_prompt, str): editing_prompt = [editing_prompt] self.enabled_editing_prompts = len(editing_prompt) elif editing_prompt_embeddings is not None: enable_edit_guidance = True self.enabled_editing_prompts = editing_prompt_embeddings.shape[0] else: self.enabled_editing_prompts = 0 enable_edit_guidance = False # 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, edit_prompt_embeds, negative_pooled_prompt_embeds, pooled_edit_embeds, num_edit_tokens, ) = self.encode_prompt( device=device, num_images_per_prompt=num_images_per_prompt, negative_prompt=negative_prompt, negative_prompt_2=negative_prompt_2, negative_prompt_embeds=negative_prompt_embeds, negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, lora_scale=text_encoder_lora_scale, clip_skip=self.clip_skip, enable_edit_guidance=enable_edit_guidance, editing_prompt=editing_prompt, editing_prompt_embeds=editing_prompt_embeddings, editing_pooled_prompt_embeds=editing_pooled_prompt_embeds, ) # 4. Prepare timesteps # self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.inversion_steps t_to_idx = {int(v): k for k, v in enumerate(timesteps)} if use_cross_attn_mask: self.attention_store = LeditsAttentionStore( average=store_averaged_over_steps, batch_size=batch_size, max_size=(latents.shape[-2] / 4.0) * (latents.shape[-1] / 4.0), max_resolution=None, ) self.prepare_unet(self.attention_store) resolution = latents.shape[-2:] att_res = (int(resolution[0] / 4), int(resolution[1] / 4)) # 5. Prepare latent variables latents = self.prepare_latents(device=device, latents=latents) # 6. Prepare extra step kwargs. extra_step_kwargs = self.prepare_extra_step_kwargs(eta) if self.text_encoder_2 is None: text_encoder_projection_dim = int(negative_pooled_prompt_embeds.shape[-1]) else: text_encoder_projection_dim = self.text_encoder_2.config.projection_dim # 7. Prepare added time ids & embeddings add_text_embeds = negative_pooled_prompt_embeds add_time_ids = self._get_add_time_ids( self.size, crops_coords_top_left, self.size, dtype=negative_pooled_prompt_embeds.dtype, text_encoder_projection_dim=text_encoder_projection_dim, ) if enable_edit_guidance: prompt_embeds = torch.cat([prompt_embeds, edit_prompt_embeds], dim=0) add_text_embeds = torch.cat([add_text_embeds, pooled_edit_embeds], dim=0) edit_concepts_time_ids = add_time_ids.repeat(edit_prompt_embeds.shape[0], 1) add_time_ids = torch.cat([add_time_ids, edit_concepts_time_ids], dim=0) self.text_cross_attention_maps = [editing_prompt] if isinstance(editing_prompt, str) else editing_prompt 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) if ip_adapter_image is not None: # TODO: fix image encoding image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt) if self.do_classifier_free_guidance: image_embeds = torch.cat([negative_image_embeds, image_embeds]) image_embeds = image_embeds.to(device) # 8. Denoising loop self.sem_guidance = None self.activation_mask = None if ( self.denoising_end is not None and isinstance(self.denoising_end, float) and self.denoising_end > 0 and self.denoising_end < 1 ): discrete_timestep_cutoff = int( round( self.scheduler.config.num_train_timesteps - (self.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] # 9. Optionally get Guidance Scale Embedding timestep_cond = None if self.unet.config.time_cond_proj_dim is not None: guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) timestep_cond = self.get_guidance_scale_embedding( guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim ).to(device=device, dtype=latents.dtype) self._num_timesteps = len(timesteps) with self.progress_bar(total=self._num_timesteps) 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] * (1 + self.enabled_editing_prompts)) 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} if ip_adapter_image is not None: added_cond_kwargs["image_embeds"] = image_embeds 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] noise_pred_out = noise_pred.chunk(1 + self.enabled_editing_prompts) # [b,4, 64, 64] noise_pred_uncond = noise_pred_out[0] noise_pred_edit_concepts = noise_pred_out[1:] noise_guidance_edit = torch.zeros( noise_pred_uncond.shape, device=self.device, dtype=noise_pred_uncond.dtype, ) if sem_guidance is not None and len(sem_guidance) > i: noise_guidance_edit += sem_guidance[i].to(self.device) elif enable_edit_guidance: if self.activation_mask is None: self.activation_mask = torch.zeros( (len(timesteps), self.enabled_editing_prompts, *noise_pred_edit_concepts[0].shape) ) if self.sem_guidance is None: self.sem_guidance = torch.zeros((len(timesteps), *noise_pred_uncond.shape)) # noise_guidance_edit = torch.zeros_like(noise_guidance) for c, noise_pred_edit_concept in enumerate(noise_pred_edit_concepts): if isinstance(edit_warmup_steps, list): edit_warmup_steps_c = edit_warmup_steps[c] else: edit_warmup_steps_c = edit_warmup_steps if i < edit_warmup_steps_c: continue if isinstance(edit_guidance_scale, list): edit_guidance_scale_c = edit_guidance_scale[c] else: edit_guidance_scale_c = edit_guidance_scale if isinstance(edit_threshold, list): edit_threshold_c = edit_threshold[c] else: edit_threshold_c = edit_threshold if isinstance(reverse_editing_direction, list): reverse_editing_direction_c = reverse_editing_direction[c] else: reverse_editing_direction_c = reverse_editing_direction if isinstance(edit_cooldown_steps, list): edit_cooldown_steps_c = edit_cooldown_steps[c] elif edit_cooldown_steps is None: edit_cooldown_steps_c = i + 1 else: edit_cooldown_steps_c = edit_cooldown_steps if i >= edit_cooldown_steps_c: continue noise_guidance_edit_tmp = noise_pred_edit_concept - noise_pred_uncond if reverse_editing_direction_c: noise_guidance_edit_tmp = noise_guidance_edit_tmp * -1 noise_guidance_edit_tmp = noise_guidance_edit_tmp * edit_guidance_scale_c if user_mask is not None: noise_guidance_edit_tmp = noise_guidance_edit_tmp * user_mask if use_cross_attn_mask: out = self.attention_store.aggregate_attention( attention_maps=self.attention_store.step_store, prompts=self.text_cross_attention_maps, res=att_res, from_where=["up", "down"], is_cross=True, select=self.text_cross_attention_maps.index(editing_prompt[c]), ) attn_map = out[:, :, :, 1 : 1 + num_edit_tokens[c]] # 0 -> startoftext # average over all tokens if attn_map.shape[3] != num_edit_tokens[c]: raise ValueError( f"Incorrect shape of attention_map. Expected size {num_edit_tokens[c]}, but found {attn_map.shape[3]}!" ) attn_map = torch.sum(attn_map, dim=3) # gaussian_smoothing attn_map = F.pad(attn_map.unsqueeze(1), (1, 1, 1, 1), mode="reflect") attn_map = self.smoothing(attn_map).squeeze(1) # torch.quantile function expects float32 if attn_map.dtype == torch.float32: tmp = torch.quantile(attn_map.flatten(start_dim=1), edit_threshold_c, dim=1) else: tmp = torch.quantile( attn_map.flatten(start_dim=1).to(torch.float32), edit_threshold_c, dim=1 ).to(attn_map.dtype) attn_mask = torch.where( attn_map >= tmp.unsqueeze(1).unsqueeze(1).repeat(1, *att_res), 1.0, 0.0 ) # resolution must match latent space dimension attn_mask = F.interpolate( attn_mask.unsqueeze(1), noise_guidance_edit_tmp.shape[-2:], # 64,64 ).repeat(1, 4, 1, 1) self.activation_mask[i, c] = attn_mask.detach().cpu() if not use_intersect_mask: noise_guidance_edit_tmp = noise_guidance_edit_tmp * attn_mask if use_intersect_mask: noise_guidance_edit_tmp_quantile = torch.abs(noise_guidance_edit_tmp) noise_guidance_edit_tmp_quantile = torch.sum( noise_guidance_edit_tmp_quantile, dim=1, keepdim=True ) noise_guidance_edit_tmp_quantile = noise_guidance_edit_tmp_quantile.repeat( 1, self.unet.config.in_channels, 1, 1 ) # torch.quantile function expects float32 if noise_guidance_edit_tmp_quantile.dtype == torch.float32: tmp = torch.quantile( noise_guidance_edit_tmp_quantile.flatten(start_dim=2), edit_threshold_c, dim=2, keepdim=False, ) else: tmp = torch.quantile( noise_guidance_edit_tmp_quantile.flatten(start_dim=2).to(torch.float32), edit_threshold_c, dim=2, keepdim=False, ).to(noise_guidance_edit_tmp_quantile.dtype) intersect_mask = ( torch.where( noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None], torch.ones_like(noise_guidance_edit_tmp), torch.zeros_like(noise_guidance_edit_tmp), ) * attn_mask ) self.activation_mask[i, c] = intersect_mask.detach().cpu() noise_guidance_edit_tmp = noise_guidance_edit_tmp * intersect_mask elif not use_cross_attn_mask: # calculate quantile noise_guidance_edit_tmp_quantile = torch.abs(noise_guidance_edit_tmp) noise_guidance_edit_tmp_quantile = torch.sum( noise_guidance_edit_tmp_quantile, dim=1, keepdim=True ) noise_guidance_edit_tmp_quantile = noise_guidance_edit_tmp_quantile.repeat(1, 4, 1, 1) # torch.quantile function expects float32 if noise_guidance_edit_tmp_quantile.dtype == torch.float32: tmp = torch.quantile( noise_guidance_edit_tmp_quantile.flatten(start_dim=2), edit_threshold_c, dim=2, keepdim=False, ) else: tmp = torch.quantile( noise_guidance_edit_tmp_quantile.flatten(start_dim=2).to(torch.float32), edit_threshold_c, dim=2, keepdim=False, ).to(noise_guidance_edit_tmp_quantile.dtype) self.activation_mask[i, c] = ( torch.where( noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None], torch.ones_like(noise_guidance_edit_tmp), torch.zeros_like(noise_guidance_edit_tmp), ) .detach() .cpu() ) noise_guidance_edit_tmp = torch.where( noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None], noise_guidance_edit_tmp, torch.zeros_like(noise_guidance_edit_tmp), ) noise_guidance_edit += noise_guidance_edit_tmp self.sem_guidance[i] = noise_guidance_edit.detach().cpu() noise_pred = noise_pred_uncond + noise_guidance_edit # compute the previous noisy sample x_t -> x_t-1 if enable_edit_guidance and self.guidance_rescale > 0.0: # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf noise_pred = rescale_noise_cfg( noise_pred, noise_pred_edit_concepts.mean(dim=0, keepdim=False), guidance_rescale=self.guidance_rescale, ) idx = t_to_idx[int(t)] latents = self.scheduler.step( noise_pred, t, latents, variance_noise=zs[idx], **extra_step_kwargs, return_dict=False )[0] # step callback if use_cross_attn_mask: store_step = i in attn_store_steps self.attention_store.between_steps(store_step) if callback_on_step_end is not None: callback_kwargs = {} for k in callback_on_step_end_tensor_inputs: callback_kwargs[k] = locals()[k] callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) latents = callback_outputs.pop("latents", latents) prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds) negative_pooled_prompt_embeds = callback_outputs.pop( "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds ) add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids) # negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids) # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > 0 and (i + 1) % self.scheduler.order == 0): progress_bar.update() 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: image = latents if not output_type == "latent": # 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 LEditsPPDiffusionPipelineOutput(images=image, nsfw_content_detected=None) @torch.no_grad() # Modified from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LEditsPPPipelineStableDiffusion.encode_image def encode_image(self, image, dtype=None, height=None, width=None, resize_mode="default", crops_coords=None): image = self.image_processor.preprocess( image=image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords ) resized = self.image_processor.postprocess(image=image, output_type="pil") if max(image.shape[-2:]) > self.vae.config["sample_size"] * 1.5: logger.warning( "Your input images far exceed the default resolution of the underlying diffusion model. " "The output images may contain severe artifacts! " "Consider down-sampling the input using the `height` and `width` parameters" ) image = image.to(self.device, dtype=dtype) needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast if needs_upcasting: image = image.float() self.upcast_vae() x0 = self.vae.encode(image).latent_dist.mode() x0 = x0.to(dtype) # cast back to fp16 if needed if needs_upcasting: self.vae.to(dtype=torch.float16) x0 = self.vae.config.scaling_factor * x0 return x0, resized @torch.no_grad() def invert( self, image: PipelineImageInput, source_prompt: str = "", source_guidance_scale=3.5, negative_prompt: str = None, negative_prompt_2: str = None, num_inversion_steps: int = 50, skip: float = 0.15, generator: Optional[torch.Generator] = None, crops_coords_top_left: Tuple[int, int] = (0, 0), num_zero_noise_steps: int = 3, cross_attention_kwargs: Optional[Dict[str, Any]] = None, ): r""" The function to the pipeline for image inversion as described by the [LEDITS++ Paper](https://arxiv.org/abs/2301.12247). If the scheduler is set to [`~schedulers.DDIMScheduler`] the inversion proposed by [edit-friendly DPDM](https://arxiv.org/abs/2304.06140) will be performed instead. Args: image (`PipelineImageInput`): Input for the image(s) that are to be edited. Multiple input images have to default to the same aspect ratio. source_prompt (`str`, defaults to `""`): Prompt describing the input image that will be used for guidance during inversion. Guidance is disabled if the `source_prompt` is `""`. source_guidance_scale (`float`, defaults to `3.5`): Strength of guidance during inversion. 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_inversion_steps (`int`, defaults to `50`): Number of total performed inversion steps after discarding the initial `skip` steps. skip (`float`, defaults to `0.15`): Portion of initial steps that will be ignored for inversion and subsequent generation. Lower values will lead to stronger changes to the input image. `skip` has to be between `0` and `1`. generator (`torch.Generator`, *optional*): A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make inversion deterministic. 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). num_zero_noise_steps (`int`, defaults to `3`): Number of final diffusion steps that will not renoise the current image. If no steps are set to zero SD-XL in combination with [`DPMSolverMultistepScheduler`] will produce noise artifacts. 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). Returns: [`~pipelines.ledits_pp.LEditsPPInversionPipelineOutput`]: Output will contain the resized input image(s) and respective VAE reconstruction(s). """ # Reset attn processor, we do not want to store attn maps during inversion self.unet.set_attn_processor(AttnProcessor()) self.eta = 1.0 self.scheduler.config.timestep_spacing = "leading" self.scheduler.set_timesteps(int(num_inversion_steps * (1 + skip))) self.inversion_steps = self.scheduler.timesteps[-num_inversion_steps:] timesteps = self.inversion_steps num_images_per_prompt = 1 device = self._execution_device # 0. Ensure that only uncond embedding is used if prompt = "" if source_prompt == "": # noise pred should only be noise_pred_uncond source_guidance_scale = 0.0 do_classifier_free_guidance = False else: do_classifier_free_guidance = source_guidance_scale > 1.0 # 1. prepare image x0, resized = self.encode_image(image, dtype=self.text_encoder_2.dtype) width = x0.shape[2] * self.vae_scale_factor height = x0.shape[3] * self.vae_scale_factor self.size = (height, width) self.batch_size = x0.shape[0] # 2. get embeddings text_encoder_lora_scale = ( cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None ) if isinstance(source_prompt, str): source_prompt = [source_prompt] * self.batch_size ( negative_prompt_embeds, prompt_embeds, negative_pooled_prompt_embeds, edit_pooled_prompt_embeds, _, ) = self.encode_prompt( device=device, num_images_per_prompt=num_images_per_prompt, negative_prompt=negative_prompt, negative_prompt_2=negative_prompt_2, editing_prompt=source_prompt, lora_scale=text_encoder_lora_scale, enable_edit_guidance=do_classifier_free_guidance, ) if self.text_encoder_2 is None: text_encoder_projection_dim = int(negative_pooled_prompt_embeds.shape[-1]) else: text_encoder_projection_dim = self.text_encoder_2.config.projection_dim # 3. Prepare added time ids & embeddings add_text_embeds = negative_pooled_prompt_embeds add_time_ids = self._get_add_time_ids( self.size, crops_coords_top_left, self.size, dtype=negative_prompt_embeds.dtype, text_encoder_projection_dim=text_encoder_projection_dim, ) if do_classifier_free_guidance: negative_prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) add_text_embeds = torch.cat([add_text_embeds, edit_pooled_prompt_embeds], dim=0) add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0) negative_prompt_embeds = negative_prompt_embeds.to(device) add_text_embeds = add_text_embeds.to(device) add_time_ids = add_time_ids.to(device).repeat(self.batch_size * num_images_per_prompt, 1) # autoencoder reconstruction if self.vae.dtype == torch.float16 and self.vae.config.force_upcast: self.upcast_vae() x0_tmp = x0.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) image_rec = self.vae.decode( x0_tmp / self.vae.config.scaling_factor, return_dict=False, generator=generator )[0] elif self.vae.config.force_upcast: x0_tmp = x0.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) image_rec = self.vae.decode( x0_tmp / self.vae.config.scaling_factor, return_dict=False, generator=generator )[0] else: image_rec = self.vae.decode(x0 / self.vae.config.scaling_factor, return_dict=False, generator=generator)[0] image_rec = self.image_processor.postprocess(image_rec, output_type="pil") # 5. find zs and xts variance_noise_shape = (num_inversion_steps, *x0.shape) # intermediate latents t_to_idx = {int(v): k for k, v in enumerate(timesteps)} xts = torch.zeros(size=variance_noise_shape, device=self.device, dtype=negative_prompt_embeds.dtype) for t in reversed(timesteps): idx = num_inversion_steps - t_to_idx[int(t)] - 1 noise = randn_tensor(shape=x0.shape, generator=generator, device=self.device, dtype=x0.dtype) xts[idx] = self.scheduler.add_noise(x0, noise, t.unsqueeze(0)) xts = torch.cat([x0.unsqueeze(0), xts], dim=0) # noise maps zs = torch.zeros(size=variance_noise_shape, device=self.device, dtype=negative_prompt_embeds.dtype) self.scheduler.set_timesteps(len(self.scheduler.timesteps)) for t in self.progress_bar(timesteps): idx = num_inversion_steps - t_to_idx[int(t)] - 1 # 1. predict noise residual xt = xts[idx + 1] latent_model_input = torch.cat([xt] * 2) if do_classifier_free_guidance else xt latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=negative_prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, added_cond_kwargs=added_cond_kwargs, return_dict=False, )[0] # 2. perform guidance if do_classifier_free_guidance: noise_pred_out = noise_pred.chunk(2) noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1] noise_pred = noise_pred_uncond + source_guidance_scale * (noise_pred_text - noise_pred_uncond) xtm1 = xts[idx] z, xtm1_corrected = compute_noise(self.scheduler, xtm1, xt, t, noise_pred, self.eta) zs[idx] = z # correction to avoid error accumulation xts[idx] = xtm1_corrected self.init_latents = xts[-1] zs = zs.flip(0) if num_zero_noise_steps > 0: zs[-num_zero_noise_steps:] = torch.zeros_like(zs[-num_zero_noise_steps:]) self.zs = zs return LEditsPPInversionPipelineOutput(images=resized, vae_reconstruction_images=image_rec) # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.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 # Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.compute_noise_ddim def compute_noise_ddim(scheduler, prev_latents, latents, timestep, noise_pred, eta): # 1. get previous step value (=t-1) prev_timestep = timestep - scheduler.config.num_train_timesteps // scheduler.num_inference_steps # 2. compute alphas, betas alpha_prod_t = scheduler.alphas_cumprod[timestep] alpha_prod_t_prev = ( scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else scheduler.final_alpha_cumprod ) beta_prod_t = 1 - alpha_prod_t # 3. compute predicted original sample from predicted noise also called # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5) # 4. Clip "predicted x_0" if scheduler.config.clip_sample: pred_original_sample = torch.clamp(pred_original_sample, -1, 1) # 5. compute variance: "sigma_t(η)" -> see formula (16) # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1) variance = scheduler._get_variance(timestep, prev_timestep) std_dev_t = eta * variance ** (0.5) # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * noise_pred # modifed so that updated xtm1 is returned as well (to avoid error accumulation) mu_xt = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction if variance > 0.0: noise = (prev_latents - mu_xt) / (variance ** (0.5) * eta) else: noise = torch.tensor([0.0]).to(latents.device) return noise, mu_xt + (eta * variance**0.5) * noise # Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.compute_noise_sde_dpm_pp_2nd def compute_noise_sde_dpm_pp_2nd(scheduler, prev_latents, latents, timestep, noise_pred, eta): def first_order_update(model_output, sample): # timestep, prev_timestep, sample): sigma_t, sigma_s = scheduler.sigmas[scheduler.step_index + 1], scheduler.sigmas[scheduler.step_index] alpha_t, sigma_t = scheduler._sigma_to_alpha_sigma_t(sigma_t) alpha_s, sigma_s = scheduler._sigma_to_alpha_sigma_t(sigma_s) lambda_t = torch.log(alpha_t) - torch.log(sigma_t) lambda_s = torch.log(alpha_s) - torch.log(sigma_s) h = lambda_t - lambda_s mu_xt = (sigma_t / sigma_s * torch.exp(-h)) * sample + (alpha_t * (1 - torch.exp(-2.0 * h))) * model_output mu_xt = scheduler.dpm_solver_first_order_update( model_output=model_output, sample=sample, noise=torch.zeros_like(sample) ) sigma = sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) if sigma > 0.0: noise = (prev_latents - mu_xt) / sigma else: noise = torch.tensor([0.0]).to(sample.device) prev_sample = mu_xt + sigma * noise return noise, prev_sample def second_order_update(model_output_list, sample): # timestep_list, prev_timestep, sample): sigma_t, sigma_s0, sigma_s1 = ( scheduler.sigmas[scheduler.step_index + 1], scheduler.sigmas[scheduler.step_index], scheduler.sigmas[scheduler.step_index - 1], ) alpha_t, sigma_t = scheduler._sigma_to_alpha_sigma_t(sigma_t) alpha_s0, sigma_s0 = scheduler._sigma_to_alpha_sigma_t(sigma_s0) alpha_s1, sigma_s1 = scheduler._sigma_to_alpha_sigma_t(sigma_s1) lambda_t = torch.log(alpha_t) - torch.log(sigma_t) lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0) lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1) m0, m1 = model_output_list[-1], model_output_list[-2] h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1 r0 = h_0 / h D0, D1 = m0, (1.0 / r0) * (m0 - m1) mu_xt = ( (sigma_t / sigma_s0 * torch.exp(-h)) * sample + (alpha_t * (1 - torch.exp(-2.0 * h))) * D0 + 0.5 * (alpha_t * (1 - torch.exp(-2.0 * h))) * D1 ) sigma = sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) if sigma > 0.0: noise = (prev_latents - mu_xt) / sigma else: noise = torch.tensor([0.0]).to(sample.device) prev_sample = mu_xt + sigma * noise return noise, prev_sample if scheduler.step_index is None: scheduler._init_step_index(timestep) model_output = scheduler.convert_model_output(model_output=noise_pred, sample=latents) for i in range(scheduler.config.solver_order - 1): scheduler.model_outputs[i] = scheduler.model_outputs[i + 1] scheduler.model_outputs[-1] = model_output if scheduler.lower_order_nums < 1: noise, prev_sample = first_order_update(model_output, latents) else: noise, prev_sample = second_order_update(scheduler.model_outputs, latents) if scheduler.lower_order_nums < scheduler.config.solver_order: scheduler.lower_order_nums += 1 # upon completion increase step index by one scheduler._step_index += 1 return noise, prev_sample # Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.compute_noise def compute_noise(scheduler, *args): if isinstance(scheduler, DDIMScheduler): return compute_noise_ddim(scheduler, *args) elif ( isinstance(scheduler, DPMSolverMultistepScheduler) and scheduler.config.algorithm_type == "sde-dpmsolver++" and scheduler.config.solver_order == 2 ): return compute_noise_sde_dpm_pp_2nd(scheduler, *args) else: raise NotImplementedError