# Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/ import inspect from typing import Any, Callable, Dict, List, Optional, Union import numpy as np import PIL.Image import torch import torch.nn.functional as F from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from diffusers import AutoencoderKL, ControlNetModel, UNet2DConditionModel, logging from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.utils import ( PIL_INTERPOLATION, replace_example_docstring, ) from diffusers.utils.torch_utils import randn_tensor 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 stable_diffusion_controlnet_inpaint_img2img import StableDiffusionControlNetInpaintImg2ImgPipeline >>> from transformers import AutoImageProcessor, UperNetForSemanticSegmentation >>> from diffusers import ControlNetModel, UniPCMultistepScheduler >>> from diffusers.utils import load_image >>> def ade_palette(): return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50], [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255], [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7], [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82], [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3], [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255], [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220], [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224], [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255], [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7], [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153], [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255], [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0], [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255], [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255], [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255], [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0], [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0], [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255], [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255], [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20], [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255], [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255], [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255], [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0], [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0], [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255], [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112], [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160], [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163], [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0], [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0], [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255], [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204], [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255], [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255], [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194], [102, 255, 0], [92, 0, 255]] >>> image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small") >>> image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small") >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16) >>> pipe = StableDiffusionControlNetInpaintImg2ImgPipeline.from_pretrained( "stable-diffusion-v1-5/stable-diffusion-inpainting", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 ) >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) >>> pipe.enable_xformers_memory_efficient_attention() >>> pipe.enable_model_cpu_offload() >>> def image_to_seg(image): pixel_values = image_processor(image, return_tensors="pt").pixel_values with torch.no_grad(): outputs = image_segmentor(pixel_values) seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3 palette = np.array(ade_palette()) for label, color in enumerate(palette): color_seg[seg == label, :] = color color_seg = color_seg.astype(np.uint8) seg_image = Image.fromarray(color_seg) return seg_image >>> image = load_image( "https://github.com/CompVis/latent-diffusion/raw/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" ) >>> mask_image = load_image( "https://github.com/CompVis/latent-diffusion/raw/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" ) >>> controlnet_conditioning_image = image_to_seg(image) >>> image = pipe( "Face of a yellow cat, high resolution, sitting on a park bench", image, mask_image, controlnet_conditioning_image, num_inference_steps=20, ).images[0] >>> image.save("out.png") ``` """ 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_mask_image(mask_image): if isinstance(mask_image, torch.Tensor): if mask_image.ndim == 2: # Batch and add channel dim for single mask mask_image = mask_image.unsqueeze(0).unsqueeze(0) elif mask_image.ndim == 3 and mask_image.shape[0] == 1: # Single mask, the 0'th dimension is considered to be # the existing batch size of 1 mask_image = mask_image.unsqueeze(0) elif mask_image.ndim == 3 and mask_image.shape[0] != 1: # Batch of mask, the 0'th dimension is considered to be # the batching dimension mask_image = mask_image.unsqueeze(1) # Binarize mask mask_image[mask_image < 0.5] = 0 mask_image[mask_image >= 0.5] = 1 else: # preprocess mask if isinstance(mask_image, (PIL.Image.Image, np.ndarray)): mask_image = [mask_image] if isinstance(mask_image, list) and isinstance(mask_image[0], PIL.Image.Image): mask_image = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask_image], axis=0) mask_image = mask_image.astype(np.float32) / 255.0 elif isinstance(mask_image, list) and isinstance(mask_image[0], np.ndarray): mask_image = np.concatenate([m[None, None, :] for m in mask_image], axis=0) mask_image[mask_image < 0.5] = 0 mask_image[mask_image >= 0.5] = 1 mask_image = torch.from_numpy(mask_image) return mask_image def prepare_controlnet_conditioning_image( controlnet_conditioning_image, width, height, batch_size, num_images_per_prompt, device, dtype ): 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) return controlnet_conditioning_image class StableDiffusionControlNetInpaintImg2ImgPipeline(DiffusionPipeline, StableDiffusionMixin): """ 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: ControlNetModel, 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." ) 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) if getattr(self, "vae", None) else 8 self.register_to_config(requires_safety_checker=requires_safety_checker) 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, ): 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. """ 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: 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 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://huggingface.co/papers/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, image, mask_image, controlnet_conditioning_image, height, width, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, strength=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}." ) controlnet_cond_image_is_pil = isinstance(controlnet_conditioning_image, PIL.Image.Image) controlnet_cond_image_is_tensor = isinstance(controlnet_conditioning_image, torch.Tensor) controlnet_cond_image_is_pil_list = isinstance(controlnet_conditioning_image, list) and isinstance( controlnet_conditioning_image[0], PIL.Image.Image ) controlnet_cond_image_is_tensor_list = isinstance(controlnet_conditioning_image, list) and isinstance( controlnet_conditioning_image[0], torch.Tensor ) if ( not controlnet_cond_image_is_pil and not controlnet_cond_image_is_tensor and not controlnet_cond_image_is_pil_list and not controlnet_cond_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 controlnet_cond_image_is_pil: controlnet_cond_image_batch_size = 1 elif controlnet_cond_image_is_tensor: controlnet_cond_image_batch_size = controlnet_conditioning_image.shape[0] elif controlnet_cond_image_is_pil_list: controlnet_cond_image_batch_size = len(controlnet_conditioning_image) elif controlnet_cond_image_is_tensor_list: controlnet_cond_image_batch_size = len(controlnet_conditioning_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 controlnet_cond_image_batch_size != 1 and controlnet_cond_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: {controlnet_cond_image_batch_size}, prompt batch size: {prompt_batch_size}" ) if isinstance(image, torch.Tensor) and not isinstance(mask_image, torch.Tensor): raise TypeError("if `image` is a tensor, `mask_image` must also be a tensor") if isinstance(image, PIL.Image.Image) and not isinstance(mask_image, PIL.Image.Image): raise TypeError("if `image` is a PIL image, `mask_image` must also be a PIL image") if isinstance(image, torch.Tensor): if image.ndim != 3 and image.ndim != 4: raise ValueError("`image` must have 3 or 4 dimensions") if mask_image.ndim != 2 and mask_image.ndim != 3 and mask_image.ndim != 4: raise ValueError("`mask_image` must have 2, 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 if mask_image.ndim == 2: mask_image_batch_size = 1 mask_image_channels = 1 mask_image_height, mask_image_width = mask_image.shape elif mask_image.ndim == 3: mask_image_channels = 1 mask_image_batch_size, mask_image_height, mask_image_width = mask_image.shape elif mask_image.ndim == 4: mask_image_batch_size, mask_image_channels, mask_image_height, mask_image_width = mask_image.shape if image_channels != 3: raise ValueError("`image` must have 3 channels") if mask_image_channels != 1: raise ValueError("`mask_image` must have 1 channel") if image_batch_size != mask_image_batch_size: raise ValueError("`image` and `mask_image` mush have the same batch sizes") if image_height != mask_image_height or image_width != mask_image_width: raise ValueError("`image` and `mask_image` must have the same height and width dimensions") if image.min() < -1 or image.max() > 1: raise ValueError("`image` should be in range [-1, 1]") if mask_image.min() < 0 or mask_image.max() > 1: raise ValueError("`mask_image` should be in range [0, 1]") else: mask_image_channels = 1 image_channels = 3 single_image_latent_channels = self.vae.config.latent_channels total_latent_channels = single_image_latent_channels * 2 + mask_image_channels if total_latent_channels != self.unet.config.in_channels: raise ValueError( f"The config of `pipeline.unet` expects {self.unet.config.in_channels} but received" f" non inpainting latent channels: {single_image_latent_channels}," f" mask channels: {mask_image_channels}, and masked image channels: {single_image_latent_channels}." f" Please verify the config of `pipeline.unet` and the `mask_image` and `image` inputs." ) if strength < 0 or strength > 1: raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") 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_mask_latents(self, mask_image, batch_size, height, width, dtype, device, do_classifier_free_guidance): # resize the mask to latents shape as we concatenate the mask to the latents # we do that before converting to dtype to avoid breaking in case we're using cpu_offload # and half precision mask_image = F.interpolate(mask_image, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)) mask_image = mask_image.to(device=device, dtype=dtype) # duplicate mask for each generation per prompt, using mps friendly method if mask_image.shape[0] < batch_size: if not batch_size % mask_image.shape[0] == 0: raise ValueError( "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to" f" a total batch size of {batch_size}, but {mask_image.shape[0]} masks were passed. Make sure the number" " of masks that you pass is divisible by the total requested batch size." ) mask_image = mask_image.repeat(batch_size // mask_image.shape[0], 1, 1, 1) mask_image = torch.cat([mask_image] * 2) if do_classifier_free_guidance else mask_image mask_image_latents = mask_image return mask_image_latents def prepare_masked_image_latents( self, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance ): masked_image = masked_image.to(device=device, dtype=dtype) # encode the mask image into latents space so we can concatenate it to the latents if isinstance(generator, list): masked_image_latents = [ self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i]) for i in range(batch_size) ] masked_image_latents = torch.cat(masked_image_latents, dim=0) else: masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator) masked_image_latents = self.vae.config.scaling_factor * masked_image_latents # duplicate masked_image_latents for each generation per prompt, using mps friendly method if masked_image_latents.shape[0] < batch_size: if not batch_size % masked_image_latents.shape[0] == 0: raise ValueError( "The passed images and the required batch size don't match. Images are supposed to be duplicated" f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed." " Make sure the number of images that you pass is divisible by the total requested batch size." ) masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1) masked_image_latents = ( torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents ) # aligning device to prevent device errors when concating it with the latent model input masked_image_latents = masked_image_latents.to(device=device, dtype=dtype) return masked_image_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, mask_image: Union[torch.Tensor, PIL.Image.Image] = None, controlnet_conditioning_image: Union[ torch.Tensor, PIL.Image.Image, List[torch.Tensor], 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.Tensor] = None, prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, output_type: str | None = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.Tensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, controlnet_conditioning_scale: 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`. mask_image (`torch.Tensor` or `PIL.Image.Image`): `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`. controlnet_conditioning_image (`torch.Tensor`, `PIL.Image.Image`, `List[torch.Tensor]` 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.Tensor`, 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://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://huggingface.co/papers/2205.11487). 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://huggingface.co/papers/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.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 will be 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, *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. 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.Tensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). 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. 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, mask_image, controlnet_conditioning_image, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, strength, ) # 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://huggingface.co/papers/2205.11487 . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 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 mask, image, and controlnet_conditioning_image image = prepare_image(image) mask_image = prepare_mask_image(mask_image) controlnet_conditioning_image = prepare_controlnet_conditioning_image( controlnet_conditioning_image, width, height, batch_size * num_images_per_prompt, num_images_per_prompt, device, self.controlnet.dtype, ) masked_image = image * (mask_image < 0.5) # 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 if latents is None: latents = self.prepare_latents( image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator, ) mask_image_latents = self.prepare_mask_latents( mask_image, batch_size * num_images_per_prompt, height, width, prompt_embeds.dtype, device, do_classifier_free_guidance, ) masked_image_latents = self.prepare_masked_image_latents( masked_image, batch_size * num_images_per_prompt, height, width, prompt_embeds.dtype, device, generator, do_classifier_free_guidance, ) if do_classifier_free_guidance: controlnet_conditioning_image = torch.cat([controlnet_conditioning_image] * 2) # 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 non_inpainting_latent_model_input = ( torch.cat([latents] * 2) if do_classifier_free_guidance else latents ) non_inpainting_latent_model_input = self.scheduler.scale_model_input( non_inpainting_latent_model_input, t ) inpainting_latent_model_input = torch.cat( [non_inpainting_latent_model_input, mask_image_latents, masked_image_latents], dim=1 ) down_block_res_samples, mid_block_res_sample = self.controlnet( non_inpainting_latent_model_input, t, encoder_hidden_states=prompt_embeds, controlnet_cond=controlnet_conditioning_image, return_dict=False, ) down_block_res_samples = [ down_block_res_sample * controlnet_conditioning_scale for down_block_res_sample in down_block_res_samples ] mid_block_res_sample *= controlnet_conditioning_scale # predict the noise residual noise_pred = self.unet( inpainting_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: step_idx = i // getattr(self.scheduler, "order", 1) callback(step_idx, 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)