# Part of the implementation is borrowed and modified from diffusers, publicly available at # https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/controlnet/pipeline_controlnet.py import inspect import os import warnings 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 diffusers.image_processor import VaeImageProcessor from diffusers.loaders import TextualInversionLoaderMixin from diffusers.models import (AutoencoderKL, ControlNetModel, UNet2DConditionModel) from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel from diffusers.pipelines.pipeline_utils import DiffusionPipeline from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput from diffusers.pipelines.stable_diffusion.safety_checker import \ StableDiffusionSafetyChecker from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.utils import (PIL_INTERPOLATION, is_accelerate_available, is_accelerate_version, logging, replace_example_docstring) from diffusers.utils.torch_utils import is_compiled_module, randn_tensor from torchvision.utils import save_image from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from .vaehook import VAEHook logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> # !pip install opencv-python transformers accelerate >>> from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler >>> from diffusers.utils import load_image >>> 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 control net and stable diffusion v1-5 >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16) >>> pipe = StableDiffusionControlNetPipeline.from_pretrained( ... "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16 ... ) >>> # 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] ``` """ class PixelAwareStableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) In addition the pipeline inherits the following loading methods: - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`] Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. controlnet ([`ControlNetModel`] or `List[ControlNetModel]`): Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets as a list, the outputs from each ControlNet are added together to create one combined additional conditioning. 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 details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ _optional_components = ['safety_checker', 'feature_extractor'] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel], scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, 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." ) if isinstance(controlnet, (list, tuple)): controlnet = MultiControlNetModel(controlnet) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, controlnet=controlnet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) 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) def _init_tiled_vae(self, encoder_tile_size=256, decoder_tile_size=256, fast_decoder=False, fast_encoder=False, color_fix=False, vae_to_gpu=True): # save original forward (only once) if not hasattr(self.vae.encoder, 'original_forward'): setattr(self.vae.encoder, 'original_forward', self.vae.encoder.forward) if not hasattr(self.vae.decoder, 'original_forward'): setattr(self.vae.decoder, 'original_forward', self.vae.decoder.forward) encoder = self.vae.encoder decoder = self.vae.decoder self.vae.encoder.forward = VAEHook( encoder, encoder_tile_size, is_decoder=False, fast_decoder=fast_decoder, fast_encoder=fast_encoder, color_fix=color_fix, to_gpu=vae_to_gpu) self.vae.decoder.forward = VAEHook( decoder, decoder_tile_size, is_decoder=True, fast_decoder=fast_decoder, fast_encoder=fast_encoder, color_fix=color_fix, to_gpu=vae_to_gpu) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion. # StableDiffusionPipeline.enable_vae_slicing def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion. # StableDiffusionPipeline.disable_vae_slicing def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion. # StableDiffusionPipeline.enable_vae_tiling def enable_vae_tiling(self): r""" Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful to save a large amount of memory and to allow the processing of larger images. """ self.vae.enable_tiling() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion. # StableDiffusionPipeline.disable_vae_tiling def disable_vae_tiling(self): r""" Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_tiling() def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae, controlnet, and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. Note that offloading happens on a submodule basis. Memory savings are higher than with `enable_model_cpu_offload`, but performance is lower. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError( 'Please install accelerate via `pip install accelerate`') device = torch.device(f'cuda:{gpu_id}') for cpu_offloaded_model in [ self.unet, self.text_encoder, self.vae, self.controlnet ]: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload( self.safety_checker, execution_device=device, offload_buffers=True) def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version( '>=', '0.17.0.dev0'): from accelerate import cpu_offload_with_hook else: raise ImportError( '`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.' ) device = torch.device(f'cuda:{gpu_id}') hook = None for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]: _, hook = cpu_offload_with_hook( cpu_offloaded_model, device, prev_module_hook=hook) if self.safety_checker is not None: # the safety checker can offload the vae again _, hook = cpu_offload_with_hook( self.safety_checker, device, prev_module_hook=hook) # control net hook has be manually offloaded as it alternates with unet cpu_offload_with_hook(self.controlnet, device) # We'll offload the last model manually. self.final_offload_hook = hook @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion. # StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, '_hf_hook'): return self.device for module in self.unet.modules(): if (hasattr(module, '_hf_hook') and hasattr(module._hf_hook, 'execution_device') and module._hf_hook.execution_device is not None): return torch.device(module._hf_hook.execution_device) return self.device # 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, ): 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. """ 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: procecss 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 prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to( dtype=self.text_encoder.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: procecss 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=self.text_encoder.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) # 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 prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_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): warnings.warn( 'The decode_latents method is deprecated and will be removed in a future version. Please' ' use VaeImageProcessor instead', FutureWarning, ) 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, height, width, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, controlnet_conditioning_scale=1.0, ): if height % 8 != 0 or width % 8 != 0: raise ValueError( f'`height` and `width` have to be divisible by 8 but are {height} and {width}.' ) flag = callback_steps is not None and ( not isinstance(callback_steps, int) or callback_steps <= 0) if (callback_steps is None) or flag: raise ValueError( f'`callback_steps` has to be a positive integer but is {callback_steps} of type' f' {type(callback_steps)}.') if prompt is not None and prompt_embeds is not None: raise ValueError( f'Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to' ' only forward one of the two.') elif prompt 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}.') # `prompt` needs more sophisticated handling when there are multiple # conditionings. if isinstance(self.controlnet, MultiControlNetModel): if isinstance(prompt, list): logger.warning( f'You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}' ' prompts. The conditionings will be fixed across the prompts.' ) # Check `image` is_compiled = hasattr( F, 'scaled_dot_product_attention') and isinstance( self.controlnet, torch._dynamo.eval_frame.OptimizedModule) if (isinstance(self.controlnet, ControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, ControlNetModel)): self.check_image(image, prompt, prompt_embeds) elif (isinstance(self.controlnet, MultiControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, MultiControlNetModel)): if not isinstance(image, list): raise TypeError( 'For multiple controlnets: `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): raise ValueError( 'A single batch of multiple conditionings are supported at the moment.' ) elif len(image) != len(self.controlnet.nets): raise ValueError( 'For multiple controlnets: `image` must have the same length as the number of controlnets.' ) for image_ in image: self.check_image(image_, prompt, prompt_embeds) else: assert False # Check `controlnet_conditioning_scale` if (isinstance(self.controlnet, ControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, ControlNetModel)): if not isinstance(controlnet_conditioning_scale, float): raise TypeError( 'For single controlnet: `controlnet_conditioning_scale` must be type `float`.' ) elif (isinstance(self.controlnet, MultiControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, MultiControlNetModel)): if isinstance(controlnet_conditioning_scale, list): if any( isinstance(i, list) for i in controlnet_conditioning_scale): raise ValueError( 'A single batch of multiple conditionings are supported at the moment.' ) elif isinstance( controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len( self.controlnet.nets): raise ValueError( 'For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`,' 'it must have the same length as the number of controlnets' ) else: assert False 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_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) if not image_is_pil and not image_is_tensor and not image_is_pil_list and not image_is_tensor_list: raise TypeError( 'image must be passed and be one of PIL image, torch tensor, list of PIL images,' 'or list of torch tensors') if image_is_pil: image_batch_size = 1 elif image_is_tensor: image_batch_size = image.shape[0] elif image_is_pil_list: image_batch_size = len(image) elif image_is_tensor_list: 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, guess_mode=False, ): if not isinstance(image, torch.Tensor): if isinstance(image, PIL.Image.Image): image = [image] if isinstance(image[0], PIL.Image.Image): images = [] for image_ in image: image_ = image_.convert('RGB') image_ = np.array(image_) image_ = image_[None, :] images.append(image_) image = images image = np.concatenate(image, axis=0) image = np.array(image).astype(np.float32) / 255.0 image = image.transpose(0, 3, 1, 2) image = torch.from_numpy(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, dim=0) 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 and not guess_mode: 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, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f'You have passed a list of generators of length {len(generator)}, but requested an effective batch' f' size of {batch_size}. Make sure the batch size matches the length of the generators.' ) if 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 def _default_height_width(self, height, width, image): # NOTE: It is possible that a list of images have different # dimensions for each image, so just checking the first image # is not _exactly_ correct, but it is simple. while isinstance(image, list): image = image[0] if height is None: if isinstance(image, PIL.Image.Image): height = image.height elif isinstance(image, torch.Tensor): height = image.shape[2] height = (height // 8) * 8 # round down to nearest multiple of 8 if width is None: if isinstance(image, PIL.Image.Image): width = image.width elif isinstance(image, torch.Tensor): width = image.shape[3] width = (width // 8) * 8 # round down to nearest multiple of 8 return height, width # override DiffusionPipeline def save_pretrained( self, save_directory: Union[str, os.PathLike], safe_serialization: bool = False, variant: Optional[str] = None, ): if isinstance(self.controlnet, ControlNetModel): super().save_pretrained(save_directory, safe_serialization, variant) else: raise NotImplementedError( 'Currently, the `save_pretrained()` is not implemented for Multi-ControlNet.' ) def _gaussian_weights(self, tile_width, tile_height, nbatches): """Generates a gaussian mask of weights for tile contributions""" from numpy import pi, exp, sqrt import numpy as np latent_width = tile_width latent_height = tile_height var = 0.01 midpoint = (latent_width - 1) / 2 x_probs = [] for x in range(latent_width): tmp = -(x - midpoint) * (x - midpoint) / ( latent_width * latent_width) / (2 * var) tmp = exp(tmp) / sqrt(2 * pi * var) x_probs.append(tmp) midpoint = latent_height / 2 y_probs = [] for y in range(latent_height): tmp = -(y - midpoint) * (y - midpoint) / ( latent_height * latent_height) / (2 * var) tmp = exp(tmp) / sqrt(2 * pi * var) y_probs.append(tmp) weights = np.outer(y_probs, x_probs) return torch.tile( torch.tensor(weights, device=self.device), (nbatches, self.unet.config.in_channels, 1, 1)) @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, image: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]] = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, 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.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = 'pil', return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, fg_mask: Optional[torch.FloatTensor] = None, conditioning_scale_fg: Union[float, List[float]] = 1.0, conditioning_scale_bg: Union[float, List[float]] = 1.0, guess_mode: bool = False, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[List[torch.FloatTensor]]`, or `List[List[PIL.Image.Image]]`): The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `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 according to them. If multiple ControlNets are specified in init, images must be passed as a list such that each element of the list can be correctly batched for input to a single controlnet. 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. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. 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. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0): The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added to the residual in the original unet. If multiple ControlNets are specified in init, you can set the corresponding scale as a list. guess_mode (`bool`, *optional*, defaults to `False`): In this mode, the ControlNet encoder will try best to recognize the content of the input image even if you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended. Examples: Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 0. Default height and width to unet height, width = self._default_height_width(height, width, image) # 1. Check inputs. Raise error if not correct # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 controlnet = self.controlnet._orig_mod if is_compiled_module( self.controlnet) else self.controlnet guess_mode = guess_mode # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 4. Prepare 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=controlnet.dtype, do_classifier_free_guidance=do_classifier_free_guidance, guess_mode=guess_mode, ) # 5. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.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, ) # 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) # 8. Denoising loop num_warmup_steps = len( timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat( [latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input( latent_model_input, t) # controlnet(s) inference if guess_mode and do_classifier_free_guidance: # Infer ControlNet only for the conditional batch. controlnet_latent_model_input = latents controlnet_prompt_embeds = prompt_embeds.chunk(2)[1] else: controlnet_latent_model_input = latent_model_input controlnet_prompt_embeds = prompt_embeds _, _, h, w = latent_model_input.size() tile_size, tile_overlap = 120, 32 if h < tile_size and w < tile_size: if image is not None: rgbs, down_block_res_samples, mid_block_res_sample = self.controlnet( controlnet_latent_model_input, t, encoder_hidden_states=controlnet_prompt_embeds, controlnet_cond=image, fg_mask=fg_mask, conditioning_scale_fg=conditioning_scale_fg, conditioning_scale_bg=conditioning_scale_bg, guess_mode=guess_mode, return_dict=False, ) else: down_block_res_samples, mid_block_res_sample = [ None ] * 10, [None] * 10 if guess_mode and do_classifier_free_guidance: # Inferred ControlNet only for the conditional batch. # To apply the output of ControlNet to both the unconditional and conditional batches, # add 0 to the unconditional batch to keep it unchanged. down_block_res_samples = [ torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples ] mid_block_res_sample = torch.cat([ torch.zeros_like(mid_block_res_sample), mid_block_res_sample ]) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, down_block_additional_residuals=down_block_res_samples, mid_block_additional_residual=mid_block_res_sample, return_dict=False, )[0] else: tile_size = min(tile_size, min(h, w)) tile_weights = self._gaussian_weights( tile_size, tile_size, 1).to(latent_model_input.device) grid_rows = 0 cur_x = 0 while cur_x < latent_model_input.size(-1): cur_x = max( grid_rows * tile_size - tile_overlap * grid_rows, 0) + tile_size grid_rows += 1 grid_cols = 0 cur_y = 0 while cur_y < latent_model_input.size(-2): cur_y = max( grid_cols * tile_size - tile_overlap * grid_cols, 0) + tile_size grid_cols += 1 input_list = [] cond_list = [] img_list = [] fg_mask_list = [] noise_preds = [] for row in range(grid_rows): for col in range(grid_cols): if col < grid_cols - 1 or row < grid_rows - 1: # extract tile from input image ofs_x = max( row * tile_size - tile_overlap * row, 0) ofs_y = max( col * tile_size - tile_overlap * col, 0) # input tile area on total image if row == grid_rows - 1: ofs_x = w - tile_size if col == grid_cols - 1: ofs_y = h - tile_size input_start_x = ofs_x input_end_x = ofs_x + tile_size input_start_y = ofs_y input_end_y = ofs_y + tile_size # input tile dimensions input_tile = latent_model_input[:, :, input_start_y: input_end_y, input_start_x: input_end_x] input_list.append(input_tile) cond_tile = controlnet_latent_model_input[:, :, input_start_y: input_end_y, input_start_x: input_end_x] cond_list.append(cond_tile) img_tile = image[:, :, input_start_y * 8:input_end_y * 8, input_start_x * 8:input_end_x * 8] img_list.append(img_tile) if fg_mask is not None: fg_mask_tile = fg_mask[:, :, input_start_y * 8:input_end_y * 8, input_start_x * 8:input_end_x * 8] fg_mask_list.append(fg_mask_tile) if len(input_list ) == batch_size or col == grid_cols - 1: input_list_t = torch.cat(input_list, dim=0) cond_list_t = torch.cat(cond_list, dim=0) img_list_t = torch.cat(img_list, dim=0) if fg_mask is not None: fg_mask_list_t = torch.cat( fg_mask_list, dim=0) else: fg_mask_list_t = None _, down_block_res_samples, mid_block_res_sample = self.controlnet( cond_list_t, t, encoder_hidden_states= controlnet_prompt_embeds, controlnet_cond=img_list_t, fg_mask=fg_mask_list_t, conditioning_scale_fg=conditioning_scale_fg, conditioning_scale_bg=conditioning_scale_bg, guess_mode=guess_mode, return_dict=False, ) if guess_mode and do_classifier_free_guidance: # Inferred ControlNet only for the conditional batch. # To apply the output of ControlNet to the unconditional/conditional batches, # add 0 to the unconditional batch to keep it unchanged. down_block_res_samples = [ torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples ] mid_block_res_sample = torch.cat([ torch.zeros_like(mid_block_res_sample), mid_block_res_sample ]) # predict the noise residual model_out = self.unet( input_list_t, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs= cross_attention_kwargs, down_block_additional_residuals= down_block_res_samples, mid_block_additional_residual= mid_block_res_sample, return_dict=False, )[0] input_list = [] cond_list = [] img_list = [] fg_mask_list = [] noise_preds.append(model_out) # Stitch noise predictions for all tiles noise_pred = torch.zeros( latent_model_input.shape, device=latent_model_input.device) contributors = torch.zeros( latent_model_input.shape, device=latent_model_input.device) # Add each tile contribution to overall latents for row in range(grid_rows): for col in range(grid_cols): if col < grid_cols - 1 or row < grid_rows - 1: # extract tile from input image ofs_x = max( row * tile_size - tile_overlap * row, 0) ofs_y = max( col * tile_size - tile_overlap * col, 0) # input tile area on total image if row == grid_rows - 1: ofs_x = w - tile_size if col == grid_cols - 1: ofs_y = h - tile_size input_start_x = ofs_x input_end_x = ofs_x + tile_size input_start_y = ofs_y input_end_y = ofs_y + tile_size noise_pred[:, :, input_start_y:input_end_y, input_start_x: input_end_x] += noise_preds[ row * grid_cols + col] * tile_weights contributors[:, :, input_start_y:input_end_y, input_start_x: input_end_x] += tile_weights # Average overlapping areas with more than 1 contributor noise_pred /= contributors # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * ( noise_pred_text - noise_pred_uncond) # 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] # call the callback, if provided flag = ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0) if i == len(timesteps) - 1 or flag: progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) # If we do sequential model offloading, let's offload unet and controlnet # manually for max memory savings if hasattr( self, 'final_offload_hook') and self.final_offload_hook is not None: self.unet.to('cpu') self.controlnet.to('cpu') torch.cuda.empty_cache() has_nsfw_concept = None if not output_type == 'latent': image = self.vae.decode( latents / self.vae.config.scaling_factor, return_dict=False)[0] 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 last model to CPU if hasattr( self, 'final_offload_hook') and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput( images=image, nsfw_content_detected=has_nsfw_concept)