import inspect from typing import Any, Callable, Dict, List, Optional, Tuple, Union import numpy as np import PIL.Image import torch import torch.nn.functional as F from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback from diffusers.image_processor import PipelineImageInput, VaeImageProcessor from diffusers.loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin from diffusers.models import AutoencoderKL, ImageProjection from diffusers.models.lora import adjust_lora_scale_text_encoder from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.utils import ( USE_PEFT_BACKEND, deprecate, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers, ) from diffusers.utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor 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 model import UNet2DConditionModelEx from huggingface_hub.utils import validate_hf_hub_args logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> # !pip install opencv-python transformers accelerate >>> from diffusers import UniPCMultistepScheduler >>> from diffusers.utils import load_image >>> from model import UNet2DConditionModelEx >>> from pipeline import StableDiffusionControlLoraV3Pipeline >>> import numpy as np >>> import torch >>> import cv2 >>> from PIL import Image >>> # download an image >>> image = load_image( ... "https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png" ... ) >>> image = np.array(image) >>> # get canny image >>> image = cv2.Canny(image, 100, 200) >>> image = image[:, :, None] >>> image = np.concatenate([image, image, image], axis=2) >>> canny_image = Image.fromarray(image) >>> # load stable diffusion v1-5 and control-lora-v3 >>> unet: UNet2DConditionModelEx = UNet2DConditionModelEx.from_pretrained( ... "runwayml/stable-diffusion-v1-5", subfolder="unet", torch_dtype=torch.float16 ... ) >>> unet = unet.add_extra_conditions(["canny"]) >>> pipe = StableDiffusionControlLoraV3Pipeline.from_pretrained( ... "runwayml/stable-diffusion-v1-5", unet=unet, torch_dtype=torch.float16 ... ) >>> # load attention processors >>> pipe.load_lora_weights("HighCWu/sd-control-lora-v3-canny") >>> # speed up diffusion process with faster scheduler and memory optimization >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) >>> # remove following line if xformers is not installed >>> pipe.enable_xformers_memory_efficient_attention() >>> pipe.enable_model_cpu_offload() >>> # generate image >>> generator = torch.manual_seed(0) >>> image = pipe( ... "futuristic-looking woman", num_inference_steps=20, generator=generator, image=canny_image ... ).images[0] ``` """ # 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, sigmas: Optional[List[float]] = 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 override the timestep spacing strategy of the scheduler. If `timesteps` is passed, `num_inference_steps` and `sigmas` must be `None`. sigmas (`List[float]`, *optional*): Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, `num_inference_steps` and `timesteps` 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 and sigmas is not None: raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values") 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) elif sigmas is not None: accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) if not accept_sigmas: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" f" sigmas schedules. Please check whether you are using the correct scheduler." ) scheduler.set_timesteps(sigmas=sigmas, 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 StableDiffusionControlLoraV3Pipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, LoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin, ): r""" Pipeline for text-to-image generation using Stable Diffusion with extra condition guidance. 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 ([`UNet2DConditionModelEx`]): A `UNet2DConditionModelEx` to denoise the encoded image latents with extra conditions. 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: UNet2DConditionModelEx, scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, image_encoder: CLIPVisionModelWithProjection = None, requires_safety_checker: bool = True, ): super().__init__() 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." ) 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, do_convert_rgb=True) 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.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = 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.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = 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.Tensor`, *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.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. 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 self.text_encoder is not None: 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, image, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ip_adapter_image=None, ip_adapter_image_embeds=None, extra_condition_scale=1.0, control_guidance_start=0.0, control_guidance_end=1.0, callback_on_step_end_tensor_inputs=None, ): 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}." ) # Check `image` unet: UNet2DConditionModelEx = self.unet._orig_mod if is_compiled_module(self.unet) else self.unet num_extra_conditions = len(unet.extra_condition_names) if num_extra_conditions == 1: self.check_image(image, prompt, prompt_embeds) elif num_extra_conditions > 1: if not isinstance(image, list): raise TypeError("For multiple extra conditions: `image` must be type `list`") # When `image` is a nested list: # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]]) elif any(isinstance(i, list) for i in image): transposed_image = [list(t) for t in zip(*image)] if len(transposed_image) != num_extra_conditions: raise ValueError( f"For multiple extra conditions: if you pass`image` as a list of list, each sublist must have the same length as the number of extra conditions, but the sublists in `image` got {len(transposed_image)} images and {num_extra_conditions} extra conditions." ) for image_ in transposed_image: self.check_image(image_, prompt, prompt_embeds) elif len(image) != num_extra_conditions: raise ValueError( f"For multiple extra conditions: `image` must have the same length as the number of extra conditions, but got {len(image)} images and {num_extra_conditions} extra conditions." ) else: for image_ in image: self.check_image(image_, prompt, prompt_embeds) else: assert False # Check `extra_condition_scale` if num_extra_conditions == 1: if not isinstance(extra_condition_scale, float): raise TypeError("For single extra condition: `extra_condition_scale` must be type `float`.") elif num_extra_conditions >= 1: if isinstance(extra_condition_scale, list): if any(isinstance(i, list) for i in extra_condition_scale): raise ValueError( "A single batch of varying conditioning scale settings (e.g. [[1.0, 0.5], [0.2, 0.8]]) is not supported at the moment. " "The conditioning scale must be fixed across the batch." ) elif isinstance(extra_condition_scale, list) and len(extra_condition_scale) != num_extra_conditions: raise ValueError( "For multiple extra conditions: When `extra_condition_scale` is specified as `list`, it must have" " the same length as the number of extra conditions" ) else: assert False if not isinstance(control_guidance_start, (tuple, list)): control_guidance_start = [control_guidance_start] if not isinstance(control_guidance_end, (tuple, list)): control_guidance_end = [control_guidance_end] if len(control_guidance_start) != len(control_guidance_end): raise ValueError( f"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list." ) if num_extra_conditions > 1: if len(control_guidance_start) != num_extra_conditions: raise ValueError( f"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {num_extra_conditions} extra conditions available. Make sure to provide {num_extra_conditions}." ) for start, end in zip(control_guidance_start, control_guidance_end): if start >= end: raise ValueError( f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}." ) if start < 0.0: raise ValueError(f"control guidance start: {start} can't be smaller than 0.") if end > 1.0: raise ValueError(f"control guidance end: {end} can't be larger than 1.0.") 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 check_image(self, image, prompt, prompt_embeds): image_is_pil = isinstance(image, PIL.Image.Image) image_is_tensor = isinstance(image, torch.Tensor) image_is_np = isinstance(image, np.ndarray) image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image) image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor) image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray) if ( not image_is_pil and not image_is_tensor and not image_is_np and not image_is_pil_list and not image_is_tensor_list and not image_is_np_list ): raise TypeError( f"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}" ) if image_is_pil: image_batch_size = 1 else: image_batch_size = len(image) if prompt is not None and isinstance(prompt, str): prompt_batch_size = 1 elif prompt is not None and isinstance(prompt, list): prompt_batch_size = len(prompt) elif prompt_embeds is not None: prompt_batch_size = prompt_embeds.shape[0] if image_batch_size != 1 and image_batch_size != prompt_batch_size: raise ValueError( f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}" ) def prepare_image( self, image, width, height, batch_size, num_images_per_prompt, device, dtype, do_classifier_free_guidance=False, ): image = self.image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32) image_batch_size = image.shape[0] if image_batch_size == 1: repeat_by = batch_size else: # image batch size is the same as prompt batch size repeat_by = num_images_per_prompt image = image.repeat_interleave(repeat_by, dim=0) image = image.to(device=device, dtype=dtype) if do_classifier_free_guidance: image = torch.cat([image] * 2) return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = ( batch_size, num_channels_latents, int(height) // self.vae_scale_factor, int(width) // self.vae_scale_factor, ) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents # 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 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 @classmethod @validate_hf_hub_args def lora_state_dict( cls, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs, ): # Override to add support for different LoRA alphas state_dict, network_alphas = super(StableDiffusionControlLoraV3Pipeline, cls).lora_state_dict( pretrained_model_name_or_path_or_dict, **kwargs ) if network_alphas is None: network_alphas = {} for k, v in state_dict.items(): if ".lora_A." in k: network_alphas[".".join(k.split(".lora_A.")[0].split(".") + ["alpha"])] = v.shape[0] return state_dict, network_alphas def load_lora_weights( self, pretrained_model_name_or_path_or_dict: Union[ Union[str, Dict[str, torch.Tensor]], List[Union[str, Dict[str, torch.Tensor]]] ], adapter_name=None, **kwargs ): unet: UNet2DConditionModelEx = self.unet._orig_mod if is_compiled_module(self.unet) else self.unet num_condition_names = len(unet.extra_condition_names) in_channels = unet.config.in_channels kwargs["weight_name"] = kwargs.pop("weight_name", "pytorch_lora_weights.safetensors") if adapter_name is not None and adapter_name not in unet.extra_condition_names: unet._hf_peft_config_loaded = True super().load_lora_weights(pretrained_model_name_or_path_or_dict, adapter_name, **kwargs) unet.set_adapter(adapter_name) return if not isinstance(pretrained_model_name_or_path_or_dict, list): pretrained_model_name_or_path_or_dict = [pretrained_model_name_or_path_or_dict] * num_condition_names pretrained_model_name_or_path_or_dict_list = pretrained_model_name_or_path_or_dict assert len(pretrained_model_name_or_path_or_dict) == len(unet.extra_condition_names) adapter_name_ori = adapter_name for i, (pretrained_model_name_or_path_or_dict, adapter_name) in enumerate(zip( pretrained_model_name_or_path_or_dict_list, unet.extra_condition_names )): _kwargs = {**kwargs} subfolder = _kwargs.pop("subfolder", None) if isinstance(subfolder, list): subfolder = subfolder[i] if not isinstance(pretrained_model_name_or_path_or_dict, dict): pretrained_model_name_or_path_or_dict, _ = self.lora_state_dict( pretrained_model_name_or_path_or_dict, subfolder=subfolder, **_kwargs ) if adapter_name_ori is not None: # only load lora of the input adapter name, then break the loop i = unet.extra_condition_names.index(adapter_name_ori) adapter_name = adapter_name_ori unet_conv_in_lora_A_name, old_weight = ([ (k, v) for k, v in pretrained_model_name_or_path_or_dict.items() if "unet." in k and ".conv_in." in k and ".lora_A." in k ] + [(None, None)])[0] if unet_conv_in_lora_A_name is not None: in_weight = old_weight[:,:in_channels] cond_weight = old_weight[:,in_channels:] zero_weight = torch.zeros_like(in_weight) new_weight = torch.cat( [in_weight] + [zero_weight] * i + [cond_weight] + [zero_weight] * (num_condition_names - i - 1), dim=1 ) pretrained_model_name_or_path_or_dict[unet_conv_in_lora_A_name] = new_weight super().load_lora_weights(pretrained_model_name_or_path_or_dict, adapter_name, **_kwargs) if adapter_name_ori is not None: break unet.activate_extra_condition_adapters() @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, image: PipelineImageInput = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, timesteps: List[int] = None, sigmas: List[float] = None, guidance_scale: float = 7.5, 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.Tensor] = None, prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, ip_adapter_image: Optional[PipelineImageInput] = None, ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None, output_type: Optional[str] = "pil", return_dict: bool = True, cross_attention_kwargs: Optional[Dict[str, Any]] = None, extra_condition_scale: Union[float, List[float]] = 1.0, control_guidance_start: Union[float, List[float]] = 0.0, control_guidance_end: Union[float, List[float]] = 1.0, clip_skip: Optional[int] = None, callback_on_step_end: Optional[ Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] ] = 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.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,: `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`): The extra input condition to provide guidance to the `unet` for generation after encoded by `vae`. If the type is specified as `torch.Tensor`, its `vae` latent representation is passed to UNet. `PIL.Image.Image` can also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height and/or width are passed, `image` is resized accordingly. If multiple extra conditions are specified in `unet`, images must be passed as a list such that each element of the list can be correctly batched for input to `unet`. When `prompt` is a list, and if a list of images is passed for `unet`, each will be paired with each prompt in the `prompt` list. This also applies to multiple extra conditions, where a list of image lists can be passed to batch for each prompt and each extra condition. height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): The height in pixels of the generated image. width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): The width in pixels of the generated 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. sigmas (`List[float]`, *optional*): Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. 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.Tensor`, *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.Tensor`, *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.Tensor`, *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.Tensor]`, *optional*): Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. 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. callback (`Callable`, *optional*): A function that calls every `callback_steps` steps during inference. The function is 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 is called. If not specified, the callback is called at every step. 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). extra_condition_scale (`float` or `List[float]`, *optional*, defaults to 1.0): The control lora scale of `unet`. If multiple extra conditions are specified in `unet`, you can set the corresponding scale as a list. control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0): The percentage of total steps at which the extra condtion starts applying. control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0): The percentage of total steps at which the extra condtion stops applying. 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`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*): A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of each denoising step during the inference. 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`", ) if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs unet: UNet2DConditionModelEx = self.unet._orig_mod if is_compiled_module(self.unet) else self.unet num_extra_conditions = len(unet.extra_condition_names) # align format for control guidance if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list): control_guidance_start = len(control_guidance_end) * [control_guidance_start] elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list): control_guidance_end = len(control_guidance_start) * [control_guidance_end] elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list): mult = num_extra_conditions control_guidance_start, control_guidance_end = ( mult * [control_guidance_start], mult * [control_guidance_end], ) # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, image, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, ip_adapter_image, ip_adapter_image_embeds, extra_condition_scale, control_guidance_start, control_guidance_end, callback_on_step_end_tensor_inputs, ) self._guidance_scale = guidance_scale self._clip_skip = clip_skip self._cross_attention_kwargs = cross_attention_kwargs # 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 if num_extra_conditions > 1 and isinstance(extra_condition_scale, float): extra_condition_scale = [extra_condition_scale] * num_extra_conditions # 3. Encode input prompt text_encoder_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=text_encoder_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 if self.do_classifier_free_guidance: prompt_embeds = torch.cat([negative_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, self.do_classifier_free_guidance, ) # 4. Prepare image if num_extra_conditions == 1: image = self.prepare_image( image=image, width=width, height=height, batch_size=batch_size * num_images_per_prompt, num_images_per_prompt=num_images_per_prompt, device=device, dtype=unet.dtype, do_classifier_free_guidance=self.do_classifier_free_guidance, ) height, width = image.shape[-2:] image = ( self.vae.encode(image.to(dtype=unet.dtype)).latent_dist.mode() * self.vae.config.scaling_factor ) elif num_extra_conditions >= 1: images = [] # Nested lists as extra condition if isinstance(image[0], list): # Transpose the nested image list image = [list(t) for t in zip(*image)] for image_ in image: image_ = self.prepare_image( image=image_, width=width, height=height, batch_size=batch_size * num_images_per_prompt, num_images_per_prompt=num_images_per_prompt, device=device, dtype=unet.dtype, do_classifier_free_guidance=self.do_classifier_free_guidance, ) images.append(image_) image = images height, width = image[0].shape[-2:] image = [ self.vae.encode(image.to(dtype=unet.dtype)).latent_dist.mode() * self.vae.config.scaling_factor for image in images ] else: assert False # 5. Prepare timesteps timesteps, num_inference_steps = retrieve_timesteps( self.scheduler, num_inference_steps, device, timesteps, sigmas ) self._num_timesteps = len(timesteps) # 6. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # 6.5 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) # 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 Create tensor stating which extra_conditions to keep extra_condition_keep = [] for i in range(len(timesteps)): keeps = [ 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e) for s, e in zip(control_guidance_start, control_guidance_end) ] extra_condition_keep.append(keeps[0] if num_extra_conditions == 1 else keeps) # 8. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order is_unet_compiled = is_compiled_module(self.unet) is_torch_higher_equal_2_1 = is_torch_version(">=", "2.1") with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # Relevant thread: # https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428 if is_unet_compiled and is_torch_higher_equal_2_1: torch._inductor.cudagraph_mark_step_begin() # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) if isinstance(extra_condition_keep[i], list): cond_scale = [c * s for c, s in zip(extra_condition_scale, extra_condition_keep[i])] else: extra_cond_scale = extra_condition_scale if isinstance(extra_cond_scale, list): extra_cond_scale = extra_cond_scale[0] cond_scale = extra_cond_scale * extra_condition_keep[i] self.unet.set_extra_condition_scale(cond_scale) # 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, extra_conditions=image, return_dict=False, )[0] # perform guidance if self.do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) # 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) self.unet.set_extra_condition_scale(1.0) # If we do sequential model offloading, let's offload unet # manually for max memory savings if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.unet.to("cpu") torch.cuda.empty_cache() 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) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)