# 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, 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 >>> from diffusers import LEditsPPPipelineStableDiffusionXL >>> from diffusers.utils import load_image >>> pipe = LEditsPPPipelineStableDiffusionXL.from_pretrained( ... "stabilityai/stable-diffusion-xl-base-1.0", variant="fp16", torch_dtype=torch.float16 ... ) >>> pipe.enable_vae_tiling() >>> pipe = pipe.to("cuda") >>> img_url = "https://www.aiml.informatik.tu-darmstadt.de/people/mbrack/tennis.jpg" >>> image = load_image(img_url).resize((1024, 1024)) >>> _ = 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], indexing="ij") 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) if getattr(self, "vae", None) else 8 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 if hasattr(self, "unet") and self.unet is not None and hasattr(self.unet.config, "sample_size") else 128 ) 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://huggingface.co/papers/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, ), ) # 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://huggingface.co/papers/2205.11487 . `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 def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() def enable_vae_tiling(self): r""" Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow processing larger images. """ self.vae.enable_tiling() def disable_vae_tiling(self): r""" Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to computing decoding in one step. """ self.vae.disable_tiling() # 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://huggingface.co/papers/2305.08891) `guidance_scale` is defined as `φ` in equation 16. of [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891). 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://huggingface.co/papers/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://huggingface.co/papers/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://huggingface.co/papers/2311.16711). 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://huggingface.co/papers/2311.16711). 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://huggingface.co/papers/2305.08891 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 ) height, width = image.shape[-2:] if height % 32 != 0 or width % 32 != 0: raise ValueError( "Image height and width must be a factor of 32. " "Consider down-sampling the input using the `height` and `width` parameters" ) 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, height: Optional[int] = None, width: Optional[int] = None, resize_mode: Optional[str] = "default", crops_coords: Optional[Tuple[int, int, int, int]] = None, ): r""" The function to the pipeline for image inversion as described by the [LEDITS++ Paper](https://huggingface.co/papers/2301.12247). If the scheduler is set to [`~schedulers.DDIMScheduler`] the inversion proposed by [edit-friendly DPDM](https://huggingface.co/papers/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). """ if height is not None and height % 32 != 0 or width is not None and width % 32 != 0: raise ValueError("height and width must be a factor of 32.") # 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, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords, ) 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): r""" Rescales `noise_cfg` tensor based on `guidance_rescale` to improve image quality and fix overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891). Args: noise_cfg (`torch.Tensor`): The predicted noise tensor for the guided diffusion process. noise_pred_text (`torch.Tensor`): The predicted noise tensor for the text-guided diffusion process. guidance_rescale (`float`, *optional*, defaults to 0.0): A rescale factor applied to the noise predictions. Returns: noise_cfg (`torch.Tensor`): The rescaled noise prediction tensor. """ 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://huggingface.co/papers/2010.02502 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://huggingface.co/papers/2010.02502 pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * noise_pred # modified 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