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| 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 | |
| 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, | |
| is_accelerate_available, | |
| is_accelerate_version, | |
| is_bs4_available, | |
| is_ftfy_available, | |
| logging, | |
| randn_tensor, | |
| replace_example_docstring, | |
| ) | |
| from ..pipeline_utils import DiffusionPipeline | |
| from . 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 | |
| EXAMPLE_DOC_STRING = """ | |
| Examples: | |
| ```py | |
| >>> from diffusers import IFPipeline, IFSuperResolutionPipeline, DiffusionPipeline | |
| >>> from diffusers.utils import pt_to_pil | |
| >>> import torch | |
| >>> pipe = IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16) | |
| >>> pipe.enable_model_cpu_offload() | |
| >>> prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says "very deep learning"' | |
| >>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt) | |
| >>> image = pipe(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 = IFSuperResolutionPipeline.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, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds | |
| ... ).images | |
| >>> image[0].save("./if_stage_II.png") | |
| ``` | |
| """ | |
| class IFSuperResolutionPipeline(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"]{1,}" | |
| ) # noqa | |
| _optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor", "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.warn( | |
| "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.enable_sequential_cpu_offload | |
| def enable_sequential_cpu_offload(self, gpu_id=0): | |
| r""" | |
| Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's | |
| models 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. | |
| """ | |
| 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}") | |
| models = [ | |
| self.text_encoder, | |
| self.unet, | |
| ] | |
| for cpu_offloaded_model in models: | |
| if cpu_offloaded_model is not None: | |
| cpu_offload(cpu_offloaded_model, device) | |
| if self.safety_checker is not None: | |
| cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) | |
| # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.enable_model_cpu_offload | |
| 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}") | |
| if self.device.type != "cpu": | |
| self.to("cpu", silence_dtype_warnings=True) | |
| torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) | |
| hook = None | |
| if self.text_encoder is not None: | |
| _, hook = cpu_offload_with_hook(self.text_encoder, device, prev_module_hook=hook) | |
| # Accelerate will move the next model to the device _before_ calling the offload hook of the | |
| # previous model. This will cause both models to be present on the device at the same time. | |
| # IF uses T5 for its text encoder which is really large. We can manually call the offload | |
| # hook for the text encoder to ensure it's moved to the cpu before the unet is moved to | |
| # the GPU. | |
| self.text_encoder_offload_hook = hook | |
| _, hook = cpu_offload_with_hook(self.unet, device, prev_module_hook=hook) | |
| # if the safety checker isn't called, `unet_offload_hook` will have to be called to manually offload the unet | |
| self.unet_offload_hook = hook | |
| if self.safety_checker is not None: | |
| _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) | |
| # We'll offload the last model manually. | |
| self.final_offload_hook = hook | |
| # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.remove_all_hooks | |
| def remove_all_hooks(self): | |
| if is_accelerate_available(): | |
| from accelerate.hooks import remove_hook_from_module | |
| else: | |
| raise ImportError("Please install accelerate via `pip install accelerate`") | |
| for model in [self.text_encoder, self.unet, self.safety_checker]: | |
| if model is not None: | |
| remove_hook_from_module(model, recurse=True) | |
| self.unet_offload_hook = None | |
| self.text_encoder_offload_hook = None | |
| self.final_offload_hook = None | |
| # 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.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`")) | |
| logger.warn("Setting `clean_caption` to False...") | |
| clean_caption = False | |
| if clean_caption and not is_ftfy_available(): | |
| logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`")) | |
| logger.warn("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>", "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 | |
| # @<nickname> | |
| 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() | |
| # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device | |
| def _execution_device(self): | |
| r""" | |
| Returns the device on which the pipeline's models will be executed. After calling | |
| `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module | |
| hooks. | |
| """ | |
| if not hasattr(self.unet, "_hf_hook"): | |
| return self.device | |
| for module in self.unet.modules(): | |
| if ( | |
| hasattr(module, "_hf_hook") | |
| and hasattr(module._hf_hook, "execution_device") | |
| and module._hf_hook.execution_device is not None | |
| ): | |
| return torch.device(module._hf_hook.execution_device) | |
| return self.device | |
| # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.encode_prompt | |
| def encode_prompt( | |
| self, | |
| prompt, | |
| do_classifier_free_guidance=True, | |
| num_images_per_prompt=1, | |
| device=None, | |
| negative_prompt=None, | |
| prompt_embeds: Optional[torch.FloatTensor] = None, | |
| negative_prompt_embeds: Optional[torch.FloatTensor] = 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 | |
| device: (`torch.device`, *optional*): | |
| torch device to place the resulting embeddings on | |
| num_images_per_prompt (`int`, *optional*, defaults to 1): | |
| number of images that should be generated per prompt | |
| do_classifier_free_guidance (`bool`, *optional*, defaults to `True`): | |
| 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. 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 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 | |
| if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None: | |
| self.unet_offload_hook.offload() | |
| 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, | |
| batch_size, | |
| noise_level, | |
| 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}." | |
| ) | |
| if noise_level < 0 or noise_level >= self.image_noising_scheduler.config.num_train_timesteps: | |
| raise ValueError( | |
| f"`noise_level`: {noise_level} must be a valid timestep in `self.noising_scheduler`, [0, {self.image_noising_scheduler.config.num_train_timesteps})" | |
| ) | |
| 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.FloatTensor`, `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}") | |
| # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_intermediate_images | |
| def prepare_intermediate_images(self, batch_size, num_channels, height, width, dtype, device, generator): | |
| shape = (batch_size, num_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." | |
| ) | |
| intermediate_images = randn_tensor(shape, generator=generator, device=device, dtype=dtype) | |
| # scale the initial noise by the standard deviation required by the scheduler | |
| intermediate_images = intermediate_images * self.scheduler.init_noise_sigma | |
| return intermediate_images | |
| def preprocess_image(self, image, num_images_per_prompt, device): | |
| 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 | |
| def __call__( | |
| self, | |
| prompt: Union[str, List[str]] = None, | |
| height: int = None, | |
| width: int = None, | |
| image: Union[PIL.Image.Image, np.ndarray, torch.FloatTensor] = 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.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, | |
| noise_level: int = 250, | |
| clean_caption: bool = True, | |
| ): | |
| """ | |
| 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. | |
| height (`int`, *optional*, defaults to self.unet.config.sample_size): | |
| The height in pixels of the generated image. | |
| width (`int`, *optional*, defaults to self.unet.config.sample_size): | |
| The width in pixels of the generated image. | |
| image (`PIL.Image.Image`, `np.ndarray`, `torch.FloatTensor`): | |
| The image to be upscaled. | |
| 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 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. | |
| 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.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.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). | |
| 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, | |
| batch_size, | |
| noise_level, | |
| callback_steps, | |
| negative_prompt, | |
| prompt_embeds, | |
| negative_prompt_embeds, | |
| ) | |
| # 2. Define call parameters | |
| height = height or self.unet.config.sample_size | |
| width = width or self.unet.config.sample_size | |
| 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 | |
| # 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]) | |
| # 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 | |
| # 5. Prepare intermediate images | |
| num_channels = self.unet.config.in_channels // 2 | |
| intermediate_images = self.prepare_intermediate_images( | |
| batch_size * num_images_per_prompt, | |
| num_channels, | |
| height, | |
| width, | |
| prompt_embeds.dtype, | |
| device, | |
| generator, | |
| ) | |
| # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline | |
| extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) | |
| # 7. Prepare upscaled image and noise level | |
| 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) | |
| # 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() | |
| # 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): | |
| 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": | |
| # 9. Post-processing | |
| image = (image / 2 + 0.5).clamp(0, 1) | |
| image = image.cpu().permute(0, 2, 3, 1).float().numpy() | |
| # 10. Run safety checker | |
| image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype) | |
| # 11. Convert to PIL | |
| image = self.numpy_to_pil(image) | |
| # 12. 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 | |
| if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None: | |
| self.unet_offload_hook.offload() | |
| else: | |
| # 9. Post-processing | |
| image = (image / 2 + 0.5).clamp(0, 1) | |
| image = image.cpu().permute(0, 2, 3, 1).float().numpy() | |
| # 10. Run safety checker | |
| image, nsfw_detected, watermark_detected = 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, nsfw_detected, watermark_detected) | |
| return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected) | |