# Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/ import inspect from typing import Any, Callable, Dict, List, Optional, Tuple, Union import numpy as np import PIL.Image import torch from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from diffusers.loaders import TextualInversionLoaderMixin # from diffusers.loaders import FromCkptMixin, LoraLoaderMixin, TextualInversionLoaderMixin from diffusers import AutoencoderKL, ControlNetModel, DiffusionPipeline, UNet2DConditionModel, logging from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.utils import ( PIL_INTERPOLATION, is_accelerate_available, is_accelerate_version, randn_tensor, replace_example_docstring, ) logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import numpy as np >>> import torch >>> from PIL import Image >>> from diffusers import ControlNetModel, UniPCMultistepScheduler >>> from diffusers.utils import load_image >>> input_image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png") >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16) >>> pipe_controlnet = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 ) >>> pipe_controlnet.scheduler = UniPCMultistepScheduler.from_config(pipe_controlnet.scheduler.config) >>> pipe_controlnet.enable_xformers_memory_efficient_attention() >>> pipe_controlnet.enable_model_cpu_offload() # using image with edges for our canny controlnet >>> control_image = load_image( "https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/vermeer_canny_edged.png") >>> result_img = pipe_controlnet(controlnet_conditioning_image=control_image, image=input_image, prompt="an android robot, cyberpank, digitl art masterpiece", num_inference_steps=20).images[0] >>> result_img.show() ``` """ def prepare_image(image): if isinstance(image, torch.Tensor): # Batch single image if image.ndim == 3: image = image.unsqueeze(0) image = image.to(dtype=torch.float32) else: # preprocess image if isinstance(image, (PIL.Image.Image, np.ndarray)): image = [image] if isinstance(image, list) and isinstance(image[0], PIL.Image.Image): image = [np.array(i.convert("RGB"))[None, :] for i in image] image = np.concatenate(image, axis=0) elif isinstance(image, list) and isinstance(image[0], np.ndarray): image = np.concatenate([i[None, :] for i in image], axis=0) image = image.transpose(0, 3, 1, 2) image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0 return image def prepare_controlnet_conditioning_image( controlnet_conditioning_image, width, height, batch_size, num_images_per_prompt, device, dtype, do_classifier_free_guidance, ): if not isinstance(controlnet_conditioning_image, torch.Tensor): if isinstance(controlnet_conditioning_image, PIL.Image.Image): controlnet_conditioning_image = [controlnet_conditioning_image] if isinstance(controlnet_conditioning_image[0], PIL.Image.Image): controlnet_conditioning_image = [ np.array(i.resize((width, height), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in controlnet_conditioning_image ] controlnet_conditioning_image = np.concatenate(controlnet_conditioning_image, axis=0) controlnet_conditioning_image = np.array(controlnet_conditioning_image).astype(np.float32) / 255.0 controlnet_conditioning_image = controlnet_conditioning_image.transpose(0, 3, 1, 2) controlnet_conditioning_image = torch.from_numpy(controlnet_conditioning_image) elif isinstance(controlnet_conditioning_image[0], torch.Tensor): controlnet_conditioning_image = torch.cat(controlnet_conditioning_image, dim=0) image_batch_size = controlnet_conditioning_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 controlnet_conditioning_image = controlnet_conditioning_image.repeat_interleave(repeat_by, dim=0) controlnet_conditioning_image = controlnet_conditioning_image.to(device=device, dtype=dtype) if do_classifier_free_guidance: controlnet_conditioning_image = torch.cat([controlnet_conditioning_image] * 2) return controlnet_conditioning_image class StableDiffusionControlNetImg2ImgPipeline(DiffusionPipeline, TextualInversionLoaderMixin): """ Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/ """ _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.register_to_config(requires_safety_checker=requires_safety_checker) def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() 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 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 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 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 def run_safety_checker(self, image, device, dtype): if self.safety_checker is not None: safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) else: has_nsfw_concept = None return image, has_nsfw_concept def decode_latents(self, latents): latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents).sample 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 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_controlnet_conditioning_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) else: raise ValueError("controlnet condition image is not valid") 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] else: raise ValueError("prompt or prompt_embeds are not valid") 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 check_inputs( self, prompt, image, controlnet_conditioning_image, height, width, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, strength=None, controlnet_guidance_start=None, controlnet_guidance_end=None, controlnet_conditioning_scale=None, ): 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}.") if (callback_steps is None) or ( 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 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 controlnet condition image if isinstance(self.controlnet, ControlNetModel): self.check_controlnet_conditioning_image(controlnet_conditioning_image, prompt, prompt_embeds) elif isinstance(self.controlnet, MultiControlNetModel): if not isinstance(controlnet_conditioning_image, list): raise TypeError("For multiple controlnets: `image` must be type `list`") if len(controlnet_conditioning_image) != len(self.controlnet.nets): raise ValueError( "For multiple controlnets: `image` must have the same length as the number of controlnets." ) for image_ in controlnet_conditioning_image: self.check_controlnet_conditioning_image(image_, prompt, prompt_embeds) else: assert False # Check `controlnet_conditioning_scale` if isinstance(self.controlnet, 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): if 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 if isinstance(image, torch.Tensor): if image.ndim != 3 and image.ndim != 4: raise ValueError("`image` must have 3 or 4 dimensions") if image.ndim == 3: image_batch_size = 1 image_channels, image_height, image_width = image.shape elif image.ndim == 4: image_batch_size, image_channels, image_height, image_width = image.shape else: assert False if image_channels != 3: raise ValueError("`image` must have 3 channels") if image.min() < -1 or image.max() > 1: raise ValueError("`image` should be in range [-1, 1]") if self.vae.config.latent_channels != self.unet.config.in_channels: raise ValueError( f"The config of `pipeline.unet` expects {self.unet.config.in_channels} but received" f" latent channels: {self.vae.config.latent_channels}," f" Please verify the config of `pipeline.unet` and the `pipeline.vae`" ) if strength < 0 or strength > 1: raise ValueError(f"The value of `strength` should in [0.0, 1.0] but is {strength}") if controlnet_guidance_start < 0 or controlnet_guidance_start > 1: raise ValueError( f"The value of `controlnet_guidance_start` should in [0.0, 1.0] but is {controlnet_guidance_start}" ) if controlnet_guidance_end < 0 or controlnet_guidance_end > 1: raise ValueError( f"The value of `controlnet_guidance_end` should in [0.0, 1.0] but is {controlnet_guidance_end}" ) if controlnet_guidance_start > controlnet_guidance_end: raise ValueError( "The value of `controlnet_guidance_start` should be less than `controlnet_guidance_end`, but got" f" `controlnet_guidance_start` {controlnet_guidance_start} >= `controlnet_guidance_end` {controlnet_guidance_end}" ) def get_timesteps(self, num_inference_steps, strength, device): # get the original timestep using init_timestep init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0) timesteps = self.scheduler.timesteps[t_start:] return timesteps, num_inference_steps - t_start # def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None): # if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)): # raise ValueError( # f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}" # ) # image = image.to(device=device, dtype=dtype) # batch_size = batch_size * num_images_per_prompt # if 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 isinstance(generator, list): # init_latents = [ # self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size) # ] # init_latents = torch.cat(init_latents, dim=0) # else: # init_latents = self.vae.encode(image).latent_dist.sample(generator) # init_latents = self.vae.config.scaling_factor * init_latents # if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0: # raise ValueError( # f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts." # ) # else: # init_latents = torch.cat([init_latents], dim=0) # shape = init_latents.shape # noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) # # get latents # init_latents = self.scheduler.add_noise(init_latents, noise, timestep) # latents = init_latents # return latents def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None): if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)): raise ValueError( f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}" ) if isinstance(image, list): image_tensors = [] for img in image: img_tensor = prepare_image(img) img_tensor = img_tensor.to(device=device, dtype=dtype) image_tensors.append(img_tensor) image = torch.stack(image_tensors, dim=0) else: image = prepare_image(image) image = image.to(device=device, dtype=dtype) batch_size = batch_size * num_images_per_prompt 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 isinstance(generator, list): init_latents = [ self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(image.shape[0]) ] init_latents = torch.cat(init_latents, dim=0) else: init_latents = self.vae.encode(image).latent_dist.sample(generator) init_latents = self.vae.config.scaling_factor * init_latents shape = init_latents.shape noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) # get latents init_latents = self.scheduler.add_noise(init_latents, noise, timestep) latents = init_latents return latents def _default_height_width(self, height, width, image): if 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[3] 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[2] width = (width // 8) * 8 # round down to nearest multiple of 8 return height, width @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, image: Union[torch.Tensor, PIL.Image.Image] = None, controlnet_conditioning_image: Union[ torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image] ] = None, strength: float = 0.8, 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, controlnet_conditioning_scale: Union[float, List[float]] = 1.0, controlnet_guidance_start: float = 0.0, controlnet_guidance_end: float = 1.0, ): 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.Tensor` or `PIL.Image.Image`): `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will be masked out with `mask_image` and repainted according to `prompt`. controlnet_conditioning_image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]` or `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 control image is automatically resized to fit the output image. strength (`float`, *optional*): Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. 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`, *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. controlnet_guidance_start ('float', *optional*, defaults to 0.0): The percentage of total steps the controlnet starts applying. Must be between 0 and 1. controlnet_guidance_end ('float', *optional*, defaults to 1.0): The percentage of total steps the controlnet ends applying. Must be between 0 and 1. Must be greater than `controlnet_guidance_start`. 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, controlnet_conditioning_image) # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, image, controlnet_conditioning_image, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, strength, controlnet_guidance_start, controlnet_guidance_end, controlnet_conditioning_scale, ) # 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 if isinstance(self.controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float): controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(self.controlnet.nets) # 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, and controlnet_conditioning_image # image = prepare_image(image) images = [prepare_image(img) for img in image] # condition image(s) if isinstance(self.controlnet, ControlNetModel): controlnet_conditioning_image = prepare_controlnet_conditioning_image( controlnet_conditioning_image=controlnet_conditioning_image, width=width, height=height, batch_size=batch_size * num_images_per_prompt, num_images_per_prompt=num_images_per_prompt, device=device, dtype=self.controlnet.dtype, do_classifier_free_guidance=do_classifier_free_guidance, ) elif isinstance(self.controlnet, MultiControlNetModel): controlnet_conditioning_images = [] for image_ in controlnet_conditioning_image: image_ = prepare_controlnet_conditioning_image( controlnet_conditioning_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=self.controlnet.dtype, do_classifier_free_guidance=do_classifier_free_guidance, ) controlnet_conditioning_images.append(image_) controlnet_conditioning_image = controlnet_conditioning_images else: assert False # 5. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device) latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) # 6. Prepare latent variables # latents = self.prepare_latents( # image, # latent_timestep, # batch_size, # num_images_per_prompt, # prompt_embeds.dtype, # device, # generator, # ) latents = [self.prepare_latents( img, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator, ) for img in images] latents = torch.cat(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 = torch.cat([latents] * 2) if do_classifier_free_guidance else latents.clone() latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # compute the percentage of total steps we are at current_sampling_percent = i / len(timesteps) # if ( # current_sampling_percent < controlnet_guidance_start # or current_sampling_percent > controlnet_guidance_end # ): # # do not apply the controlnet # down_block_res_samples = None # mid_block_res_sample = None # else: # # apply the controlnet # down_block_res_samples, mid_block_res_sample = self.controlnet( # latent_model_input, # t, # encoder_hidden_states=prompt_embeds, # controlnet_cond=controlnet_conditioning_image, # conditioning_scale=controlnet_conditioning_scale, # return_dict=False, # ) if ( current_sampling_percent < controlnet_guidance_start or current_sampling_percent > controlnet_guidance_end ): # do not apply the controlnet down_block_res_samples = None mid_block_res_sample = None else: down_block_res_samples = [] mid_block_res_samples = [] for i in range(batch_size): # apply the controlnet down_block_res_sample, mid_block_res_sample = self.controlnet( latent_model_input[i * num_images_per_prompt:(i + 1) * num_images_per_prompt], t, encoder_hidden_states=prompt_embeds, controlnet_cond=controlnet_conditioning_image[i], conditioning_scale=controlnet_conditioning_scale, return_dict=False, ) down_block_res_samples.append(down_block_res_sample) mid_block_res_samples.append(mid_block_res_sample) down_block_res_samples = torch.cat(down_block_res_samples, dim=0) mid_block_res_sample = torch.cat(mid_block_res_samples, dim=0) # 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, ).sample # 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).prev_sample # 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: 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() if output_type == "latent": image = latents has_nsfw_concept = None elif output_type == "pil": # 8. Post-processing image = self.decode_latents(latents) # 9. Run safety checker image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) # 10. Convert to PIL image = self.numpy_to_pil(image) else: # 8. Post-processing image = self.decode_latents(latents) # 9. Run safety checker image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) # 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)