import html import inspect import re import urllib.parse as ul 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, T5EncoderModel, T5Tokenizer from ...loaders import LoraLoaderMixin from ...models import UNet2DConditionModel from ...schedulers import DDPMScheduler from ...utils import ( BACKENDS_MAPPING, PIL_INTERPOLATION, is_bs4_available, is_ftfy_available, logging, replace_example_docstring, ) from ...utils.torch_utils import randn_tensor from ..pipeline_utils import DiffusionPipeline from .pipeline_output import IFPipelineOutput from .safety_checker import IFSafetyChecker from .watermark import IFWatermarker if is_bs4_available(): from bs4 import BeautifulSoup if is_ftfy_available(): import ftfy logger = logging.get_logger(__name__) # pylint: disable=invalid-name # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.resize def resize(images: PIL.Image.Image, img_size: int) -> PIL.Image.Image: w, h = images.size coef = w / h w, h = img_size, img_size if coef >= 1: w = int(round(img_size / 8 * coef) * 8) else: h = int(round(img_size / 8 / coef) * 8) images = images.resize((w, h), resample=PIL_INTERPOLATION["bicubic"], reducing_gap=None) return images EXAMPLE_DOC_STRING = """ Examples: ```py >>> from diffusers import IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline, DiffusionPipeline >>> from diffusers.utils import pt_to_pil >>> import torch >>> from PIL import Image >>> import requests >>> from io import BytesIO >>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" >>> response = requests.get(url) >>> original_image = Image.open(BytesIO(response.content)).convert("RGB") >>> original_image = original_image.resize((768, 512)) >>> pipe = IFImg2ImgPipeline.from_pretrained( ... "DeepFloyd/IF-I-XL-v1.0", ... variant="fp16", ... torch_dtype=torch.float16, ... ) >>> pipe.enable_model_cpu_offload() >>> prompt = "A fantasy landscape in style minecraft" >>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt) >>> image = pipe( ... image=original_image, ... prompt_embeds=prompt_embeds, ... negative_prompt_embeds=negative_embeds, ... output_type="pt", ... ).images >>> # save intermediate image >>> pil_image = pt_to_pil(image) >>> pil_image[0].save("./if_stage_I.png") >>> super_res_1_pipe = IFImg2ImgSuperResolutionPipeline.from_pretrained( ... "DeepFloyd/IF-II-L-v1.0", ... text_encoder=None, ... variant="fp16", ... torch_dtype=torch.float16, ... ) >>> super_res_1_pipe.enable_model_cpu_offload() >>> image = super_res_1_pipe( ... image=image, ... original_image=original_image, ... prompt_embeds=prompt_embeds, ... negative_prompt_embeds=negative_embeds, ... ).images >>> image[0].save("./if_stage_II.png") ``` """ class IFImg2ImgSuperResolutionPipeline(DiffusionPipeline, LoraLoaderMixin): tokenizer: T5Tokenizer text_encoder: T5EncoderModel unet: UNet2DConditionModel scheduler: DDPMScheduler image_noising_scheduler: DDPMScheduler feature_extractor: Optional[CLIPImageProcessor] safety_checker: Optional[IFSafetyChecker] watermarker: Optional[IFWatermarker] bad_punct_regex = re.compile( r"[" + "#®•©™&@·º½¾¿¡§~" + r"\)" + r"\(" + r"\]" + r"\[" + r"\}" + r"\{" + r"\|" + "\\" + r"\/" + r"\*" + r"]{1,}" ) # noqa _optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor"] model_cpu_offload_seq = "text_encoder->unet" _exclude_from_cpu_offload = ["watermarker"] def __init__( self, tokenizer: T5Tokenizer, text_encoder: T5EncoderModel, unet: UNet2DConditionModel, scheduler: DDPMScheduler, image_noising_scheduler: DDPMScheduler, safety_checker: Optional[IFSafetyChecker], feature_extractor: Optional[CLIPImageProcessor], watermarker: Optional[IFWatermarker], 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 IF 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 unet.config.in_channels != 6: logger.warning( "It seems like you have loaded a checkpoint that shall not be used for super resolution from {unet.config._name_or_path} as it accepts {unet.config.in_channels} input channels instead of 6. Please make sure to pass a super resolution checkpoint as the `'unet'`: IFSuperResolutionPipeline.from_pretrained(unet=super_resolution_unet, ...)`." ) self.register_modules( tokenizer=tokenizer, text_encoder=text_encoder, unet=unet, scheduler=scheduler, image_noising_scheduler=image_noising_scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, watermarker=watermarker, ) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing def _text_preprocessing(self, text, clean_caption=False): if clean_caption and not is_bs4_available(): logger.warning(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`")) logger.warning("Setting `clean_caption` to False...") clean_caption = False if clean_caption and not is_ftfy_available(): logger.warning(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`")) logger.warning("Setting `clean_caption` to False...") clean_caption = False if not isinstance(text, (tuple, list)): text = [text] def process(text: str): if clean_caption: text = self._clean_caption(text) text = self._clean_caption(text) else: text = text.lower().strip() return text return [process(t) for t in text] # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption def _clean_caption(self, caption): caption = str(caption) caption = ul.unquote_plus(caption) caption = caption.strip().lower() caption = re.sub("", "person", caption) # urls: caption = re.sub( r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa "", caption, ) # regex for urls caption = re.sub( r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa "", caption, ) # regex for urls # html: caption = BeautifulSoup(caption, features="html.parser").text # @ caption = re.sub(r"@[\w\d]+\b", "", caption) # 31C0—31EF CJK Strokes # 31F0—31FF Katakana Phonetic Extensions # 3200—32FF Enclosed CJK Letters and Months # 3300—33FF CJK Compatibility # 3400—4DBF CJK Unified Ideographs Extension A # 4DC0—4DFF Yijing Hexagram Symbols # 4E00—9FFF CJK Unified Ideographs caption = re.sub(r"[\u31c0-\u31ef]+", "", caption) caption = re.sub(r"[\u31f0-\u31ff]+", "", caption) caption = re.sub(r"[\u3200-\u32ff]+", "", caption) caption = re.sub(r"[\u3300-\u33ff]+", "", caption) caption = re.sub(r"[\u3400-\u4dbf]+", "", caption) caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption) caption = re.sub(r"[\u4e00-\u9fff]+", "", caption) ####################################################### # все виды тире / all types of dash --> "-" caption = re.sub( r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa "-", caption, ) # кавычки к одному стандарту caption = re.sub(r"[`´«»“”¨]", '"', caption) caption = re.sub(r"[‘’]", "'", caption) # " caption = re.sub(r""?", "", caption) # & caption = re.sub(r"&", "", caption) # ip adresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: caption = re.sub(r"\d:\d\d\s+$", "", caption) # \n caption = re.sub(r"\\n", " ", caption) # "#123" caption = re.sub(r"#\d{1,3}\b", "", caption) # "#12345.." caption = re.sub(r"#\d{5,}\b", "", caption) # "123456.." caption = re.sub(r"\b\d{6,}\b", "", caption) # filenames: caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption) # caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT""" caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT""" caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT caption = re.sub(r"\s+\.\s+", r" ", caption) # " . " # this-is-my-cute-cat / this_is_my_cute_cat regex2 = re.compile(r"(?:\-|\_)") if len(re.findall(regex2, caption)) > 3: caption = re.sub(regex2, " ", caption) caption = ftfy.fix_text(caption) caption = html.unescape(html.unescape(caption)) caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640 caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231 caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption) caption = re.sub(r"(free\s)?download(\sfree)?", "", caption) caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption) caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption) caption = re.sub(r"\bpage\s+\d+\b", "", caption) caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a... caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption) caption = re.sub(r"\b\s+\:\s+", r": ", caption) caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption) caption = re.sub(r"\s+", " ", caption) caption.strip() caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption) caption = re.sub(r"^[\'\_,\-\:;]", r"", caption) caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption) caption = re.sub(r"^\.\S+$", "", caption) return caption.strip() @torch.no_grad() # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.encode_prompt def encode_prompt( self, prompt: Union[str, List[str]], do_classifier_free_guidance: bool = True, num_images_per_prompt: int = 1, device: Optional[torch.device] = None, negative_prompt: Optional[Union[str, List[str]]] = None, prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, clean_caption: bool = False, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded do_classifier_free_guidance (`bool`, *optional*, defaults to `True`): whether to use classifier free guidance or not num_images_per_prompt (`int`, *optional*, defaults to 1): number of images that should be generated per prompt device: (`torch.device`, *optional*): torch device to place the resulting embeddings on 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. 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. clean_caption (bool, defaults to `False`): If `True`, the function will preprocess and clean the provided caption before encoding. """ if prompt is not None and negative_prompt is not None: if 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)}." ) if device is None: device = self._execution_device 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] # while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF max_length = 77 if prompt_embeds is None: prompt = self._text_preprocessing(prompt, clean_caption=clean_caption) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=max_length, truncation=True, add_special_tokens=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[:, max_length - 1 : -1]) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {max_length} tokens: {removed_text}" ) attention_mask = text_inputs.attention_mask.to(device) prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] if self.text_encoder is not None: dtype = self.text_encoder.dtype elif self.unet is not None: dtype = self.unet.dtype else: dtype = None prompt_embeds = prompt_embeds.to(dtype=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 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 uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_attention_mask=True, add_special_tokens=True, return_tensors="pt", ) attention_mask = uncond_input.attention_mask.to(device) 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=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 else: negative_prompt_embeds = None return prompt_embeds, negative_prompt_embeds # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker 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, nsfw_detected, watermark_detected = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype=dtype), ) else: nsfw_detected = None watermark_detected = None return image, nsfw_detected, watermark_detected # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.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, original_image, batch_size, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): 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}." ) # image if isinstance(image, list): check_image_type = image[0] else: check_image_type = image if ( not isinstance(check_image_type, torch.Tensor) and not isinstance(check_image_type, PIL.Image.Image) and not isinstance(check_image_type, np.ndarray) ): raise ValueError( "`image` has to be of type `torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is" f" {type(check_image_type)}" ) if isinstance(image, list): image_batch_size = len(image) elif isinstance(image, torch.Tensor): image_batch_size = image.shape[0] elif isinstance(image, PIL.Image.Image): image_batch_size = 1 elif isinstance(image, np.ndarray): image_batch_size = image.shape[0] else: assert False if batch_size != image_batch_size: raise ValueError(f"image batch size: {image_batch_size} must be same as prompt batch size {batch_size}") # original_image if isinstance(original_image, list): check_image_type = original_image[0] else: check_image_type = original_image if ( not isinstance(check_image_type, torch.Tensor) and not isinstance(check_image_type, PIL.Image.Image) and not isinstance(check_image_type, np.ndarray) ): raise ValueError( "`original_image` has to be of type `torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is" f" {type(check_image_type)}" ) if isinstance(original_image, list): image_batch_size = len(original_image) elif isinstance(original_image, torch.Tensor): image_batch_size = original_image.shape[0] elif isinstance(original_image, PIL.Image.Image): image_batch_size = 1 elif isinstance(original_image, np.ndarray): image_batch_size = original_image.shape[0] else: assert False if batch_size != image_batch_size: raise ValueError( f"original_image batch size: {image_batch_size} must be same as prompt batch size {batch_size}" ) # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.preprocess_image with preprocess_image -> preprocess_original_image def preprocess_original_image(self, image: PIL.Image.Image) -> torch.Tensor: if not isinstance(image, list): image = [image] def numpy_to_pt(images): if images.ndim == 3: images = images[..., None] images = torch.from_numpy(images.transpose(0, 3, 1, 2)) return images if isinstance(image[0], PIL.Image.Image): new_image = [] for image_ in image: image_ = image_.convert("RGB") image_ = resize(image_, self.unet.config.sample_size) image_ = np.array(image_) image_ = image_.astype(np.float32) image_ = image_ / 127.5 - 1 new_image.append(image_) image = new_image image = np.stack(image, axis=0) # to np image = numpy_to_pt(image) # to pt elif isinstance(image[0], np.ndarray): image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0) image = numpy_to_pt(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0) return image # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_superresolution.IFSuperResolutionPipeline.preprocess_image def preprocess_image(self, image: PIL.Image.Image, num_images_per_prompt, device) -> torch.Tensor: if not isinstance(image, torch.Tensor) and not isinstance(image, list): image = [image] if isinstance(image[0], PIL.Image.Image): image = [np.array(i).astype(np.float32) / 127.5 - 1.0 for i in image] image = np.stack(image, axis=0) # to np image = torch.from_numpy(image.transpose(0, 3, 1, 2)) elif isinstance(image[0], np.ndarray): image = np.stack(image, axis=0) # to np if image.ndim == 5: image = image[0] image = torch.from_numpy(image.transpose(0, 3, 1, 2)) elif isinstance(image, list) and isinstance(image[0], torch.Tensor): dims = image[0].ndim if dims == 3: image = torch.stack(image, dim=0) elif dims == 4: image = torch.concat(image, dim=0) else: raise ValueError(f"Image must have 3 or 4 dimensions, instead got {dims}") image = image.to(device=device, dtype=self.unet.dtype) image = image.repeat_interleave(num_images_per_prompt, dim=0) return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps def get_timesteps(self, num_inference_steps, strength): # get the original timestep using init_timestep init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0) timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :] if hasattr(self.scheduler, "set_begin_index"): self.scheduler.set_begin_index(t_start * self.scheduler.order) return timesteps, num_inference_steps - t_start # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.prepare_intermediate_images def prepare_intermediate_images( self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None ): _, channels, height, width = image.shape batch_size = batch_size * num_images_per_prompt shape = (batch_size, channels, height, width) 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." ) noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) image = image.repeat_interleave(num_images_per_prompt, dim=0) image = self.scheduler.add_noise(image, noise, timestep) return image @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, image: Union[PIL.Image.Image, np.ndarray, torch.Tensor], original_image: Union[ PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray] ] = None, strength: float = 0.8, prompt: Union[str, List[str]] = None, num_inference_steps: int = 50, timesteps: List[int] = None, guidance_scale: float = 4.0, 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, prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, output_type: Optional[str] = "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, noise_level: int = 250, clean_caption: bool = True, ): """ Function invoked when calling the pipeline for generation. Args: image (`torch.Tensor` or `PIL.Image.Image`): `Image`, or tensor representing an image batch, that will be used as the starting point for the process. original_image (`torch.Tensor` or `PIL.Image.Image`): The original image that `image` was varied from. strength (`float`, *optional*, defaults to 0.8): 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`. 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. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. timesteps (`List[int]`, *optional*): Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps` timesteps are used. Must be in descending order. guidance_scale (`float`, *optional*, defaults to 4.0): 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. 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.IFPipelineOutput`] 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). noise_level (`int`, *optional*, defaults to 250): The amount of noise to add to the upscaled image. Must be in the range `[0, 1000)` clean_caption (`bool`, *optional*, defaults to `True`): Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to be installed. If the dependencies are not installed, the embeddings will be created from the raw prompt. Examples: Returns: [`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.IFPipelineOutput`] 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) or watermarked content, according to the `safety_checker`. """ # 1. Check inputs. Raise error if not correct 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] self.check_inputs( prompt, image, original_image, batch_size, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, ) # 2. Define call parameters # 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 device = self._execution_device # 3. Encode input prompt prompt_embeds, negative_prompt_embeds = self.encode_prompt( prompt, do_classifier_free_guidance, num_images_per_prompt=num_images_per_prompt, device=device, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, clean_caption=clean_caption, ) if do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) dtype = prompt_embeds.dtype # 4. Prepare timesteps if timesteps is not None: self.scheduler.set_timesteps(timesteps=timesteps, device=device) timesteps = self.scheduler.timesteps num_inference_steps = len(timesteps) else: self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength) # 5. prepare original image original_image = self.preprocess_original_image(original_image) original_image = original_image.to(device=device, dtype=dtype) # 6. Prepare intermediate images noise_timestep = timesteps[0:1] noise_timestep = noise_timestep.repeat(batch_size * num_images_per_prompt) intermediate_images = self.prepare_intermediate_images( original_image, noise_timestep, batch_size, num_images_per_prompt, dtype, device, generator, ) # 7. Prepare upscaled image and noise level _, _, height, width = original_image.shape image = self.preprocess_image(image, num_images_per_prompt, device) upscaled = F.interpolate(image, (height, width), mode="bilinear", align_corners=True) noise_level = torch.tensor([noise_level] * upscaled.shape[0], device=upscaled.device) noise = randn_tensor(upscaled.shape, generator=generator, device=upscaled.device, dtype=upscaled.dtype) upscaled = self.image_noising_scheduler.add_noise(upscaled, noise, timesteps=noise_level) if do_classifier_free_guidance: noise_level = torch.cat([noise_level] * 2) # 8. 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) # HACK: see comment in `enable_model_cpu_offload` if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None: self.text_encoder_offload_hook.offload() # 9. 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): model_input = torch.cat([intermediate_images, upscaled], dim=1) model_input = torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input model_input = self.scheduler.scale_model_input(model_input, t) # predict the noise residual noise_pred = self.unet( model_input, t, encoder_hidden_states=prompt_embeds, class_labels=noise_level, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1] // 2, dim=1) noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1] // 2, dim=1) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) noise_pred = torch.cat([noise_pred, predicted_variance], dim=1) if self.scheduler.config.variance_type not in ["learned", "learned_range"]: noise_pred, _ = noise_pred.split(intermediate_images.shape[1], dim=1) # compute the previous noisy sample x_t -> x_t-1 intermediate_images = self.scheduler.step( noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False )[0] # 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, intermediate_images) image = intermediate_images if output_type == "pil": # 10. Post-processing image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).float().numpy() # 11. Run safety checker image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype) # 12. Convert to PIL image = self.numpy_to_pil(image) # 13. Apply watermark if self.watermarker is not None: self.watermarker.apply_watermark(image, self.unet.config.sample_size) elif output_type == "pt": nsfw_detected = None watermark_detected = None else: # 10. Post-processing image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).float().numpy() # 11. Run safety checker image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype) # Offload all models self.maybe_free_model_hooks() if not return_dict: return (image, nsfw_detected, watermark_detected) return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected)