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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.Image | |
import torch | |
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_accelerate_available, | |
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 | |
logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
if is_bs4_available(): | |
from bs4 import BeautifulSoup | |
if is_ftfy_available(): | |
import ftfy | |
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 IFImg2ImgPipeline(DiffusionPipeline, LoraLoaderMixin): | |
tokenizer: T5Tokenizer | |
text_encoder: T5EncoderModel | |
unet: UNet2DConditionModel | |
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", "watermarker"] | |
model_cpu_offload_seq = "text_encoder->unet" | |
def __init__( | |
self, | |
tokenizer: T5Tokenizer, | |
text_encoder: T5EncoderModel, | |
unet: UNet2DConditionModel, | |
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." | |
) | |
self.register_modules( | |
tokenizer=tokenizer, | |
text_encoder=text_encoder, | |
unet=unet, | |
scheduler=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.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 | |
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.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 | |
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.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. | |
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 | |
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, | |
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 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._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() | |
def preprocess_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.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 | |
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:] | |
return timesteps, num_inference_steps - t_start | |
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 | |
def __call__( | |
self, | |
prompt: Union[str, List[str]] = None, | |
image: Union[ | |
PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray] | |
] = None, | |
strength: float = 0.7, | |
num_inference_steps: int = 80, | |
timesteps: List[int] = None, | |
guidance_scale: float = 10.0, | |
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, | |
clean_caption: bool = True, | |
cross_attention_kwargs: Optional[Dict[str, Any]] = None, | |
): | |
""" | |
Function invoked when calling the pipeline for generation. | |
Args: | |
prompt (`str` or `List[str]`, *optional*): | |
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. | |
instead. | |
image (`torch.FloatTensor` or `PIL.Image.Image`): | |
`Image`, or tensor representing an image batch, that will be used as the starting point for the | |
process. | |
strength (`float`, *optional*, defaults to 0.7): | |
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`. | |
num_inference_steps (`int`, *optional*, defaults to 80): | |
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 10.0): | |
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.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. | |
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. | |
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). | |
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, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds | |
) | |
# 2. Define call parameters | |
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]) | |
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 intermediate images | |
image = self.preprocess_image(image) | |
image = image.to(device=device, dtype=dtype) | |
noise_timestep = timesteps[0:1] | |
noise_timestep = noise_timestep.repeat(batch_size * num_images_per_prompt) | |
intermediate_images = self.prepare_intermediate_images( | |
image, noise_timestep, batch_size, num_images_per_prompt, 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) | |
# 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() | |
# 7. 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] * 2) if do_classifier_free_guidance else intermediate_images | |
) | |
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, | |
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], dim=1) | |
noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1], 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(model_input.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": | |
# 8. Post-processing | |
image = (image / 2 + 0.5).clamp(0, 1) | |
image = image.cpu().permute(0, 2, 3, 1).float().numpy() | |
# 9. Run safety checker | |
image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype) | |
# 10. Convert to PIL | |
image = self.numpy_to_pil(image) | |
# 11. 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: | |
# 8. Post-processing | |
image = (image / 2 + 0.5).clamp(0, 1) | |
image = image.cpu().permute(0, 2, 3, 1).float().numpy() | |
# 9. 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) | |