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import random
from pathlib import Path
from typing import Dict, List, Optional, Tuple, Union
from PIL import Image
from torch import zeros_like
from torch.utils.data import Dataset
from torchvision import transforms
import glob
from lora_diffusion.preprocess_files import face_mask_google_mediapipe
OBJECT_TEMPLATE = [
"a photo of a {}",
"a rendering of a {}",
"a cropped photo of the {}",
"the photo of a {}",
"a photo of a clean {}",
"a photo of a dirty {}",
"a dark photo of the {}",
"a photo of my {}",
"a photo of the cool {}",
"a close-up photo of a {}",
"a bright photo of the {}",
"a cropped photo of a {}",
"a photo of the {}",
"a good photo of the {}",
"a photo of one {}",
"a close-up photo of the {}",
"a rendition of the {}",
"a photo of the clean {}",
"a rendition of a {}",
"a photo of a nice {}",
"a good photo of a {}",
"a photo of the nice {}",
"a photo of the small {}",
"a photo of the weird {}",
"a photo of the large {}",
"a photo of a cool {}",
"a photo of a small {}",
]
STYLE_TEMPLATE = [
"a painting in the style of {}",
"a rendering in the style of {}",
"a cropped painting in the style of {}",
"the painting in the style of {}",
"a clean painting in the style of {}",
"a dirty painting in the style of {}",
"a dark painting in the style of {}",
"a picture in the style of {}",
"a cool painting in the style of {}",
"a close-up painting in the style of {}",
"a bright painting in the style of {}",
"a cropped painting in the style of {}",
"a good painting in the style of {}",
"a close-up painting in the style of {}",
"a rendition in the style of {}",
"a nice painting in the style of {}",
"a small painting in the style of {}",
"a weird painting in the style of {}",
"a large painting in the style of {}",
]
NULL_TEMPLATE = ["{}"]
TEMPLATE_MAP = {
"object": OBJECT_TEMPLATE,
"style": STYLE_TEMPLATE,
"null": NULL_TEMPLATE,
}
def _randomset(lis):
ret = []
for i in range(len(lis)):
if random.random() < 0.5:
ret.append(lis[i])
return ret
def _shuffle(lis):
return random.sample(lis, len(lis))
def _get_cutout_holes(
height,
width,
min_holes=8,
max_holes=32,
min_height=16,
max_height=128,
min_width=16,
max_width=128,
):
holes = []
for _n in range(random.randint(min_holes, max_holes)):
hole_height = random.randint(min_height, max_height)
hole_width = random.randint(min_width, max_width)
y1 = random.randint(0, height - hole_height)
x1 = random.randint(0, width - hole_width)
y2 = y1 + hole_height
x2 = x1 + hole_width
holes.append((x1, y1, x2, y2))
return holes
def _generate_random_mask(image):
mask = zeros_like(image[:1])
holes = _get_cutout_holes(mask.shape[1], mask.shape[2])
for (x1, y1, x2, y2) in holes:
mask[:, y1:y2, x1:x2] = 1.0
if random.uniform(0, 1) < 0.25:
mask.fill_(1.0)
masked_image = image * (mask < 0.5)
return mask, masked_image
class PivotalTuningDatasetCapation(Dataset):
"""
A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
It pre-processes the images and the tokenizes prompts.
"""
def __init__(
self,
images,
caption,
tokenizer,
token_map: Optional[dict] = None,
use_template: Optional[str] = None,
size=512,
h_flip=True,
color_jitter=False,
resize=True,
use_mask_captioned_data=False,
use_face_segmentation_condition=False,
train_inpainting=False,
blur_amount: int = 70,
):
self.size = size
self.tokenizer = tokenizer
self.resize = resize
self.train_inpainting = train_inpainting
assert not (
use_mask_captioned_data and use_template
), "Can't use both mask caption data and template."
# Prepare the instance images
# self.instance_images_path = None
self.images = images
self.captions = [caption] * len(images)
self.use_mask = use_face_segmentation_condition or use_mask_captioned_data
self.use_mask_captioned_data = use_mask_captioned_data
self.num_instance_images = len(self.images)
self.token_map = token_map
self.use_template = use_template
if use_template is not None:
self.templates = TEMPLATE_MAP[use_template]
self._length = self.num_instance_images
self.h_flip = h_flip
self.image_transforms = transforms.Compose(
[
transforms.Resize(
size, interpolation=transforms.InterpolationMode.BILINEAR
)
if resize
else transforms.Lambda(lambda x: x),
transforms.ColorJitter(0.1, 0.1)
if color_jitter
else transforms.Lambda(lambda x: x),
transforms.CenterCrop(size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
self.blur_amount = blur_amount
def __len__(self):
return self._length
def __getitem__(self, index):
example = {}
instance_image = self.images[index % self.num_instance_images]
if not instance_image.mode == "RGB":
instance_image = instance_image.convert("RGB")
example["instance_images"] = self.image_transforms(instance_image)
if self.train_inpainting:
(
example["instance_masks"],
example["instance_masked_images"],
) = _generate_random_mask(example["instance_images"])
if self.use_template:
assert self.token_map is not None
input_tok = list(self.token_map.values())[0]
text = random.choice(self.templates).format(input_tok)
else:
text = self.captions[index % self.num_instance_images].strip()
if self.token_map is not None:
for token, value in self.token_map.items():
text = text.replace(token, value)
print(text)
if self.use_mask:
example["mask"] = (
self.image_transforms(
Image.open(self.mask_path[index % self.num_instance_images])
)
* 0.5
+ 1.0
)
if self.h_flip and random.random() > 0.5:
hflip = transforms.RandomHorizontalFlip(p=1)
example["instance_images"] = hflip(example["instance_images"])
if self.use_mask:
example["mask"] = hflip(example["mask"])
example["instance_prompt_ids"] = self.tokenizer(
text,
padding="do_not_pad",
truncation=True,
max_length=self.tokenizer.model_max_length,
).input_ids
return example
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