# Copyright 2024 Smoretalk, Inc. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect from typing import Any, Callable, Dict, List, Optional, Union import numpy as np import PIL.Image import torch import torch.nn.functional as F from packaging import version from transformers import ( CLIPImageProcessor, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection ) from diffusers.configuration_utils import FrozenDict from diffusers.image_processor import PipelineImageInput, VaeImageProcessor from diffusers.loaders import ( FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin, ) from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel from diffusers.models.attention_processor import ( Attention, AttnProcessor2_0, FusedAttnProcessor2_0, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor, XFormersAttnProcessor, ) from diffusers.models.lora import adjust_lora_scale_text_encoder from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.utils import ( PIL_INTERPOLATION, USE_PEFT_BACKEND, deprecate, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers, ) from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin from diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker from diffusers.utils.torch_utils import randn_tensor logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import StableDiffusionImg2ImgPipeline >>> pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) >>> pipe = pipe.to("cuda") >>> prompt = "a photo of an astronaut riding a horse on mars" >>> image = pipe(prompt).images[0] ``` """ class PAGIdentitySelfAttnProcessor: r""" Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). """ def __init__(self): if not hasattr(F, "scaled_dot_product_attention"): raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") def __call__( self, attn: Attention, hidden_states: torch.FloatTensor, encoder_hidden_states: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, temb: Optional[torch.FloatTensor] = None, *args, **kwargs, ) -> torch.FloatTensor: if len(args) > 0 or kwargs.get("scale", None) is not None: deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." deprecate("scale", "1.0.0", deprecation_message) residual = hidden_states if attn.spatial_norm is not None: hidden_states = attn.spatial_norm(hidden_states, temb) input_ndim = hidden_states.ndim if input_ndim == 4: batch_size, channel, height, width = hidden_states.shape hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) # chunk hidden_states_org, hidden_states_ptb = hidden_states.chunk(2) # original path batch_size, sequence_length, _ = hidden_states_org.shape if attention_mask is not None: attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) # scaled_dot_product_attention expects attention_mask shape to be # (batch, heads, source_length, target_length) attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) if attn.group_norm is not None: hidden_states_org = attn.group_norm(hidden_states_org.transpose(1, 2)).transpose(1, 2) query = attn.to_q(hidden_states_org) key = attn.to_k(hidden_states_org) value = attn.to_v(hidden_states_org) inner_dim = key.shape[-1] head_dim = inner_dim // attn.heads query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) # the output of sdp = (batch, num_heads, seq_len, head_dim) # TODO: add support for attn.scale when we move to Torch 2.1 hidden_states_org = F.scaled_dot_product_attention( query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False ) hidden_states_org = hidden_states_org.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) hidden_states_org = hidden_states_org.to(query.dtype) # linear proj hidden_states_org = attn.to_out[0](hidden_states_org) # dropout hidden_states_org = attn.to_out[1](hidden_states_org) if input_ndim == 4: hidden_states_org = hidden_states_org.transpose(-1, -2).reshape(batch_size, channel, height, width) # perturbed path (identity attention) batch_size, sequence_length, _ = hidden_states_ptb.shape if attention_mask is not None: attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) # scaled_dot_product_attention expects attention_mask shape to be # (batch, heads, source_length, target_length) attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) if attn.group_norm is not None: hidden_states_ptb = attn.group_norm(hidden_states_ptb.transpose(1, 2)).transpose(1, 2) value = attn.to_v(hidden_states_ptb) hidden_states_ptb = torch.zeros(value.shape).to(value.get_device()) # hidden_states_ptb = value hidden_states_ptb = hidden_states_ptb.to(query.dtype) # linear proj hidden_states_ptb = attn.to_out[0](hidden_states_ptb) # dropout hidden_states_ptb = attn.to_out[1](hidden_states_ptb) if input_ndim == 4: hidden_states_ptb = hidden_states_ptb.transpose(-1, -2).reshape(batch_size, channel, height, width) # cat hidden_states = torch.cat([hidden_states_org, hidden_states_ptb]) if attn.residual_connection: hidden_states = hidden_states + residual hidden_states = hidden_states / attn.rescale_output_factor return hidden_states class PAGCFGIdentitySelfAttnProcessor: r""" Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). """ def __init__(self): if not hasattr(F, "scaled_dot_product_attention"): raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") def __call__( self, attn: Attention, hidden_states: torch.FloatTensor, encoder_hidden_states: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, temb: Optional[torch.FloatTensor] = None, *args, **kwargs, ) -> torch.FloatTensor: if len(args) > 0 or kwargs.get("scale", None) is not None: deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." deprecate("scale", "1.0.0", deprecation_message) residual = hidden_states if attn.spatial_norm is not None: hidden_states = attn.spatial_norm(hidden_states, temb) input_ndim = hidden_states.ndim if input_ndim == 4: batch_size, channel, height, width = hidden_states.shape hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) # chunk hidden_states_uncond, hidden_states_org, hidden_states_ptb = hidden_states.chunk(3) hidden_states_org = torch.cat([hidden_states_uncond, hidden_states_org]) # original path batch_size, sequence_length, _ = hidden_states_org.shape if attention_mask is not None: attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) # scaled_dot_product_attention expects attention_mask shape to be # (batch, heads, source_length, target_length) attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) if attn.group_norm is not None: hidden_states_org = attn.group_norm(hidden_states_org.transpose(1, 2)).transpose(1, 2) query = attn.to_q(hidden_states_org) key = attn.to_k(hidden_states_org) value = attn.to_v(hidden_states_org) inner_dim = key.shape[-1] head_dim = inner_dim // attn.heads query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) # the output of sdp = (batch, num_heads, seq_len, head_dim) # TODO: add support for attn.scale when we move to Torch 2.1 hidden_states_org = F.scaled_dot_product_attention( query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False ) hidden_states_org = hidden_states_org.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) hidden_states_org = hidden_states_org.to(query.dtype) # linear proj hidden_states_org = attn.to_out[0](hidden_states_org) # dropout hidden_states_org = attn.to_out[1](hidden_states_org) if input_ndim == 4: hidden_states_org = hidden_states_org.transpose(-1, -2).reshape(batch_size, channel, height, width) # perturbed path (identity attention) batch_size, sequence_length, _ = hidden_states_ptb.shape if attention_mask is not None: attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) # scaled_dot_product_attention expects attention_mask shape to be # (batch, heads, source_length, target_length) attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) if attn.group_norm is not None: hidden_states_ptb = attn.group_norm(hidden_states_ptb.transpose(1, 2)).transpose(1, 2) value = attn.to_v(hidden_states_ptb) hidden_states_ptb = value hidden_states_ptb = hidden_states_ptb.to(query.dtype) # linear proj hidden_states_ptb = attn.to_out[0](hidden_states_ptb) # dropout hidden_states_ptb = attn.to_out[1](hidden_states_ptb) if input_ndim == 4: hidden_states_ptb = hidden_states_ptb.transpose(-1, -2).reshape(batch_size, channel, height, width) # cat hidden_states = torch.cat([hidden_states_org, hidden_states_ptb]) if attn.residual_connection: hidden_states = hidden_states + residual hidden_states = hidden_states / attn.rescale_output_factor return hidden_states # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): """ Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4 """ std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True) std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True) # rescale the results from guidance (fixes overexposure) noise_pred_rescaled = noise_cfg * (std_text / std_cfg) # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg return noise_cfg def retrieve_latents( encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample" ): if hasattr(encoder_output, "latent_dist") and sample_mode == "sample": return encoder_output.latent_dist.sample(generator) elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax": return encoder_output.latent_dist.mode() elif hasattr(encoder_output, "latents"): return encoder_output.latents else: raise AttributeError("Could not access latents of provided encoder_output") def preprocess(image): deprecation_message = "The preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead" deprecate("preprocess", "1.0.0", deprecation_message, standard_warn=False) if isinstance(image, torch.Tensor): return image elif isinstance(image, PIL.Image.Image): image = [image] if isinstance(image[0], PIL.Image.Image): w, h = image[0].size w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] image = np.concatenate(image, axis=0) image = np.array(image).astype(np.float32) / 255.0 image = image.transpose(0, 3, 1, 2) image = 2.0 * image - 1.0 image = torch.from_numpy(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, dim=0) return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps def retrieve_timesteps( scheduler, num_inference_steps: Optional[int] = None, device: Optional[Union[str, torch.device]] = None, timesteps: Optional[List[int]] = None, **kwargs, ): """ Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. Args: scheduler (`SchedulerMixin`): The scheduler to get timesteps from. num_inference_steps (`int`): The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` must be `None`. device (`str` or `torch.device`, *optional*): The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. timesteps (`List[int]`, *optional*): Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps` must be `None`. Returns: `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the second element is the number of inference steps. """ if timesteps is not None: accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) if not accepts_timesteps: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" f" timestep schedules. Please check whether you are using the correct scheduler." ) scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) timesteps = scheduler.timesteps num_inference_steps = len(timesteps) else: scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) timesteps = scheduler.timesteps return timesteps, num_inference_steps class StableDiffusionImg2ImgPipeline( DiffusionPipeline, StableDiffusionMixin, FromSingleFileMixin, TextualInversionLoaderMixin, LoraLoaderMixin, IPAdapterMixin, ): r""" Pipeline for text-guided image-to-image generation using Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.). The pipeline also inherits the following loading methods: - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters 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 ([clip-vit-large-patch14](https://huggingface.co./openai/clip-vit-large-patch14)). tokenizer ([`~transformers.CLIPTokenizer`]): A `CLIPTokenizer` to tokenize text. unet ([`UNet2DConditionModel`]): A `UNet2DConditionModel` to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please refer to the [model card](https://huggingface.co./runwayml/stable-diffusion-v1-5) for more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. """ model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae" _optional_components = ["safety_checker", "feature_extractor", "image_encoder"] _exclude_from_cpu_offload = ["safety_checker"] _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, image_encoder: CLIPVisionModelWithProjection = None, requires_safety_checker: bool = True, ): super().__init__() if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " "to update the config accordingly as leaving `steps_offset` might led to incorrect results" " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" " file" ) deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["steps_offset"] = 1 scheduler._internal_dict = FrozenDict(new_config) if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." " `clip_sample` should be set to False in the configuration file. Please make sure to update the" " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" ) deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["clip_sample"] = False scheduler._internal_dict = FrozenDict(new_config) if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( version.parse(unet.config._diffusers_version).base_version ) < version.parse("0.9.0.dev0") is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: deprecation_message = ( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" " the `unet/config.json` file" ) deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(unet.config) new_config["sample_size"] = 64 unet._internal_dict = FrozenDict(new_config) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, image_encoder=image_encoder, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, lora_scale: Optional[float] = None, **kwargs, ): deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple." deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False) prompt_embeds_tuple = self.encode_prompt( prompt=prompt, device=device, num_images_per_prompt=num_images_per_prompt, do_classifier_free_guidance=do_classifier_free_guidance, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, lora_scale=lora_scale, **kwargs, ) # concatenate for backwards comp prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]]) return prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt def encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, lora_scale: Optional[float] = None, clip_skip: Optional[int] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. 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. """ # set lora scale so that monkey patched LoRA # function of text encoder can correctly access it if lora_scale is not None and isinstance(self, LoraLoaderMixin): self._lora_scale = lora_scale # dynamically adjust the LoRA scale 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 prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: process multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None if clip_skip is None: prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask) prompt_embeds = prompt_embeds[0] else: prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True ) # Access the `hidden_states` first, that contains a tuple of # all the hidden states from the encoder layers. Then index into # the tuple to access the hidden states from the desired layer. prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)] # We also need to apply the final LayerNorm here to not mess with the # representations. The `last_hidden_states` that we typically use for # obtaining the final prompt representations passes through the LayerNorm # layer. prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) if self.text_encoder is not None: prompt_embeds_dtype = self.text_encoder.dtype elif self.unet is not None: prompt_embeds_dtype = self.unet.dtype else: prompt_embeds_dtype = prompt_embeds.dtype prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: process multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND: # Retrieve the original scale by scaling back the LoRA layers unscale_lora_layers(self.text_encoder, lora_scale) return prompt_embeds, negative_prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None): dtype = next(self.image_encoder.parameters()).dtype if not isinstance(image, torch.Tensor): image = self.feature_extractor(image, return_tensors="pt").pixel_values image = image.to(device=device, dtype=dtype) if output_hidden_states: image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2] image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0) uncond_image_enc_hidden_states = self.image_encoder( torch.zeros_like(image), output_hidden_states=True ).hidden_states[-2] uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave( num_images_per_prompt, dim=0 ) return image_enc_hidden_states, uncond_image_enc_hidden_states else: image_embeds = self.image_encoder(image).image_embeds image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0) uncond_image_embeds = torch.zeros_like(image_embeds) return image_embeds, uncond_image_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds def prepare_ip_adapter_image_embeds( self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance ): if ip_adapter_image_embeds is None: if not isinstance(ip_adapter_image, list): ip_adapter_image = [ip_adapter_image] if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers): raise ValueError( f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters." ) image_embeds = [] for single_ip_adapter_image, image_proj_layer in zip( ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers ): output_hidden_state = not isinstance(image_proj_layer, ImageProjection) single_image_embeds, single_negative_image_embeds = self.encode_image( single_ip_adapter_image, device, 1, output_hidden_state ) single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0) single_negative_image_embeds = torch.stack( [single_negative_image_embeds] * num_images_per_prompt, dim=0 ) if do_classifier_free_guidance: single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds]) single_image_embeds = single_image_embeds.to(device) image_embeds.append(single_image_embeds) else: repeat_dims = [1] image_embeds = [] for single_image_embeds in ip_adapter_image_embeds: if do_classifier_free_guidance: single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2) single_image_embeds = single_image_embeds.repeat( num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:])) ) single_negative_image_embeds = single_negative_image_embeds.repeat( num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:])) ) single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds]) else: single_image_embeds = single_image_embeds.repeat( num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:])) ) image_embeds.append(single_image_embeds) return image_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead" deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, strength, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ip_adapter_image=None, ip_adapter_image_embeds=None, callback_on_step_end_tensor_inputs=None, ): if strength < 0 or strength > 1: raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if callback_on_step_end_tensor_inputs is not None and not all( k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs ): raise ValueError( f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) if ip_adapter_image is not None and ip_adapter_image_embeds is not None: raise ValueError( "Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined." ) if ip_adapter_image_embeds is not None: if not isinstance(ip_adapter_image_embeds, list): raise ValueError( f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}" ) elif ip_adapter_image_embeds[0].ndim not in [3, 4]: raise ValueError( f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D" ) def get_timesteps(self, num_inference_steps, strength, device): # get the original timestep using init_timestep init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0) timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :] if hasattr(self.scheduler, "set_begin_index"): self.scheduler.set_begin_index(t_start * self.scheduler.order) return timesteps, num_inference_steps - t_start def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None): if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)): raise ValueError( f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}" ) image = image.to(device=device, dtype=dtype) batch_size = batch_size * num_images_per_prompt if image.shape[1] == 4: init_latents = image else: if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) elif isinstance(generator, list): init_latents = [ retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i]) for i in range(batch_size) ] init_latents = torch.cat(init_latents, dim=0) else: init_latents = retrieve_latents(self.vae.encode(image), generator=generator) init_latents = self.vae.config.scaling_factor * init_latents if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0: # expand init_latents for batch_size deprecation_message = ( f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial" " images (`image`). Initial images are now duplicating to match the number of text prompts. Note" " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update" " your script to pass as many initial images as text prompts to suppress this warning." ) deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False) additional_image_per_prompt = batch_size // init_latents.shape[0] init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0) elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0: raise ValueError( f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts." ) else: init_latents = torch.cat([init_latents], dim=0) shape = init_latents.shape noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) # get latents init_latents = self.scheduler.add_noise(init_latents, noise, timestep) latents = init_latents return latents # 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.FloatTensor: """ See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298 Args: timesteps (`torch.Tensor`): generate embedding vectors at these timesteps embedding_dim (`int`, *optional*, defaults to 512): dimension of the embeddings to generate dtype: data type of the generated embeddings Returns: `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)` """ assert len(w.shape) == 1 w = w * 1000.0 half_dim = embedding_dim // 2 emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb) emb = w.to(dtype)[:, None] * emb[None, :] emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) if embedding_dim % 2 == 1: # zero pad emb = torch.nn.functional.pad(emb, (0, 1)) assert emb.shape == (w.shape[0], embedding_dim) return emb def pred_z0(self, sample, model_output, timestep): alpha_prod_t = self.scheduler.alphas_cumprod[timestep].to(sample.device) beta_prod_t = 1 - alpha_prod_t if self.scheduler.config.prediction_type == "epsilon": pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5) elif self.scheduler.config.prediction_type == "sample": pred_original_sample = model_output elif self.scheduler.config.prediction_type == "v_prediction": pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output # predict V model_output = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample else: raise ValueError( f"prediction_type given as {self.scheduler.config.prediction_type} must be one of `epsilon`, `sample`," " or `v_prediction`" ) return pred_original_sample def pred_x0(self, latents, noise_pred, t, generator, device, prompt_embeds, output_type): pred_z0 = self.pred_z0(latents, noise_pred, t) pred_x0 = self.vae.decode(pred_z0 / self.vae.config.scaling_factor, return_dict=False, generator=generator)[0] pred_x0, ____ = self.run_safety_checker(pred_x0, device, prompt_embeds.dtype) do_denormalize = [True] * pred_x0.shape[0] pred_x0 = self.image_processor.postprocess(pred_x0, output_type=output_type, do_denormalize=do_denormalize) return pred_x0 @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 num_timesteps(self): return self._num_timesteps @property def interrupt(self): return self._interrupt @property def pag_scale(self): return self._pag_scale @property def do_adversarial_guidance(self): return self._pag_scale > 0 @property def pag_adaptive_scaling(self): return self._pag_adaptive_scaling @property def do_pag_adaptive_scaling(self): return self._pag_adaptive_scaling > 0 @property def pag_drop_rate(self): return self._pag_drop_rate @property def pag_applied_layers(self): return self._pag_applied_layers @property def pag_applied_layers_index(self): return self._pag_applied_layers_index @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, image: PipelineImageInput = None, strength: float = 0.8, num_inference_steps: int = 50, timesteps: List[int] = None, guidance_scale: float = 7.5, pag_scale: float = 0.0, pag_adaptive_scaling: float = 0.0, pag_drop_rate: float = 0.5, pag_applied_layers: List[str] = ["down"], # ['down', 'mid', 'up'] pag_applied_layers_index: List[str] = ["d4"], # ['d4', 'd5', 'm0'] negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, # latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ip_adapter_image: Optional[PipelineImageInput] = None, ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None, output_type: Optional[str] = "pil", return_dict: bool = True, cross_attention_kwargs: Optional[Dict[str, Any]] = None, guidance_rescale: float = 0.0, 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 generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`. image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`): `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image latents as `image`, but if passing latents directly it is not encoded again. strength (`float`, *optional*, defaults to 0.8): Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a starting point and more noise is added the higher the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising process runs for the full number of iterations specified in `num_inference_steps`. A value of 1 essentially ignores `image`. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. timesteps (`List[int]`, *optional*): Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. Must be in descending order. guidance_scale (`float`, *optional*, defaults to 7.5): A higher guidance scale value encourages the model to generate images closely linked to the text `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide what to not include in image generation. If not defined, you need to pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor is generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, text embeddings are generated from the `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument. ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*): Pre-generated image embeddings for IP-Adapter. If not provided, embeddings are computed from the `ip_adapter_image` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generated image. Choose between `PIL.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). guidance_rescale (`float`, *optional*, defaults to 0.0): Guidance rescale factor from [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. 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.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned, otherwise a `tuple` is returned where the first element is a list with the generated images and the second element is a list of `bool`s indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content. """ callback = kwargs.pop("callback", None) callback_steps = kwargs.pop("callback_steps", None) if callback is not None: deprecate( "callback", "1.0.0", "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`", ) if callback_steps is not None: deprecate( "callback_steps", "1.0.0", "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`", ) # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, ip_adapter_image, ip_adapter_image_embeds, callback_on_step_end_tensor_inputs, ) self._guidance_scale = guidance_scale self._guidance_rescale = guidance_rescale self._clip_skip = clip_skip self._cross_attention_kwargs = cross_attention_kwargs self._interrupt = False self._pag_scale = pag_scale self._pag_adaptive_scaling = pag_adaptive_scaling self._pag_drop_rate = pag_drop_rate self._pag_applied_layers = pag_applied_layers self._pag_applied_layers_index = pag_applied_layers_index # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # 3. Encode input prompt lora_scale = ( self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None ) prompt_embeds, negative_prompt_embeds = self.encode_prompt( prompt, device, num_images_per_prompt, self.do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, lora_scale=lora_scale, clip_skip=self.clip_skip, ) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes # cfg if self.do_classifier_free_guidance and not self.do_adversarial_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) # pag elif not self.do_classifier_free_guidance and self.do_adversarial_guidance: prompt_embeds = torch.cat([prompt_embeds, prompt_embeds]) # both elif self.do_classifier_free_guidance and self.do_adversarial_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds, prompt_embeds]) if ip_adapter_image is not None or ip_adapter_image_embeds is not None: image_embeds = self.prepare_ip_adapter_image_embeds( ip_adapter_image, ip_adapter_image_embeds, device, batch_size * num_images_per_prompt ) # 4. Preprocess image image = self.image_processor.preprocess(image) # 5.set timesteps timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps) timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device) latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) # 6. Prepare latent variables latents = self.prepare_latents( image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator, ) # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 7.1 Add image embeds for IP-Adapter added_cond_kwargs = ( {"image_embeds": image_embeds} if (ip_adapter_image is not None or ip_adapter_image_embeds is not None) else None ) # 7.2 Optionally get Guidance Scale Embedding timestep_cond = None if self.unet.config.time_cond_proj_dim is not None: guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) timestep_cond = self.get_guidance_scale_embedding( guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim ).to(device=device, dtype=latents.dtype) # 8. Denoising loop if self.do_adversarial_guidance: down_layers = [] mid_layers = [] up_layers = [] for name, module in self.unet.named_modules(): if "attn1" in name and "to" not in name: layer_type = name.split(".")[0].split("_")[0] if layer_type == "down": down_layers.append(module) elif layer_type == "mid": mid_layers.append(module) elif layer_type == "up": up_layers.append(module) else: raise ValueError(f"Invalid layer type: {layer_type}") num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order self._num_timesteps = len(timesteps) with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): if self.interrupt: continue # cfg if self.do_classifier_free_guidance and not self.do_adversarial_guidance: latent_model_input = torch.cat([latents] * 2) # pag elif not self.do_classifier_free_guidance and self.do_adversarial_guidance: latent_model_input = torch.cat([latents] * 2) # both elif self.do_classifier_free_guidance and self.do_adversarial_guidance: latent_model_input = torch.cat([latents] * 3) # no else: latent_model_input = latents # change attention layer in UNet if use PAG if self.do_adversarial_guidance: if self.do_classifier_free_guidance: replace_processor = PAGCFGIdentitySelfAttnProcessor() else: replace_processor = PAGIdentitySelfAttnProcessor() drop_layers = self.pag_applied_layers_index for drop_layer in drop_layers: try: if drop_layer[0] == "d": down_layers[int(drop_layer[1])].processor = replace_processor elif drop_layer[0] == "m": mid_layers[int(drop_layer[1])].processor = replace_processor elif drop_layer[0] == "u": up_layers[int(drop_layer[1])].processor = replace_processor else: raise ValueError(f"Invalid layer type: {drop_layer[0]}") except IndexError: raise ValueError( f"Invalid layer index: {drop_layer}. Available layers: {len(down_layers)} down layers, {len(mid_layers)} mid layers, {len(up_layers)} up layers." ) latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, timestep_cond=timestep_cond, cross_attention_kwargs=self.cross_attention_kwargs, added_cond_kwargs=added_cond_kwargs, return_dict=False, )[0] # perform guidance # cfg if self.do_classifier_free_guidance and not self.do_adversarial_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) delta = noise_pred_text - noise_pred_uncond noise_pred = noise_pred_uncond + self.guidance_scale * delta # pag elif not self.do_classifier_free_guidance and self.do_adversarial_guidance: noise_pred_original, noise_pred_perturb = noise_pred.chunk(2) signal_scale = self.pag_scale if self.do_pag_adaptive_scaling: signal_scale = self.pag_scale - self.pag_adaptive_scaling * (1000 - t) if signal_scale < 0: signal_scale = 0 noise_pred = noise_pred_original + signal_scale * (noise_pred_original - noise_pred_perturb) # both elif self.do_classifier_free_guidance and self.do_adversarial_guidance: noise_pred_uncond, noise_pred_text, noise_pred_text_perturb = noise_pred.chunk(3) signal_scale = self.pag_scale if self.do_pag_adaptive_scaling: signal_scale = self.pag_scale - self.pag_adaptive_scaling * (1000 - t) if signal_scale < 0: signal_scale = 0 noise_pred = ( noise_pred_text + (self.guidance_scale - 1.0) * (noise_pred_text - noise_pred_uncond) + signal_scale * (noise_pred_text - noise_pred_text_perturb) ) if self.do_classifier_free_guidance and self.guidance_rescale > 0.0: # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] if 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) # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: step_idx = i // getattr(self.scheduler, "order", 1) callback(step_idx, t, latents) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[ 0 ] image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) # Offload all models self.maybe_free_model_hooks() if not return_dict: return (image, has_nsfw_concept) # change attention layer in UNet if use PAG if self.do_adversarial_guidance: drop_layers = self.pag_applied_layers_index for drop_layer in drop_layers: try: if drop_layer[0] == "d": down_layers[int(drop_layer[1])].processor = AttnProcessor2_0() elif drop_layer[0] == "m": mid_layers[int(drop_layer[1])].processor = AttnProcessor2_0() elif drop_layer[0] == "u": up_layers[int(drop_layer[1])].processor = AttnProcessor2_0() else: raise ValueError(f"Invalid layer type: {drop_layer[0]}") except IndexError: raise ValueError( f"Invalid layer index: {drop_layer}. Available layers: {len(down_layers)} down layers, {len(mid_layers)} mid layers, {len(up_layers)} up layers." ) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)