|
|
|
''' |
|
Copyright 2022 The International Digital Economy Academy (IDEA). CCNL team. All rights reserved. |
|
Licensed under the Apache License, Version 2.0 (the "License"); |
|
you may not use this file except in compliance with the License. |
|
You may obtain a copy of the License at |
|
|
|
http://www.apache.org/licenses/LICENSE-2.0 |
|
|
|
Unless required by applicable law or agreed to in writing, software |
|
distributed under the License is distributed on an "AS IS" BASIS, |
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
|
@File : dreambooth_datasets.py |
|
@Time : 2022/11/10 00:20 |
|
@Author : Gan Ruyi |
|
@Version : 1.0 |
|
@Contact : ganruyi@idea.edu.cn |
|
@License : (C)Copyright 2022-2023, CCNL-IDEA |
|
''' |
|
from torch.utils.data import Dataset |
|
from torchvision import transforms |
|
from PIL import Image |
|
from pathlib import Path |
|
|
|
|
|
def add_data_args(parent_args): |
|
parser = parent_args.add_argument_group('taiyi stable diffusion data args') |
|
parser.add_argument( |
|
"--instance_data_dir", |
|
type=str, |
|
default=None, |
|
required=True, |
|
help="A folder containing the training data of instance images.", |
|
) |
|
parser.add_argument( |
|
"--class_data_dir", |
|
type=str, |
|
default=None, |
|
required=False, |
|
help="A folder containing the training data of class images.", |
|
) |
|
parser.add_argument( |
|
"--instance_prompt", |
|
type=str, |
|
default=None, |
|
help="The prompt with identifier specifying the instance", |
|
) |
|
parser.add_argument( |
|
"--class_prompt", |
|
type=str, |
|
default=None, |
|
help="The prompt to specify images in the same class as provided instance images.", |
|
) |
|
parser.add_argument( |
|
"--with_prior_preservation", |
|
default=False, |
|
action="store_true", |
|
help="Flag to add prior preservation loss.", |
|
) |
|
parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.") |
|
parser.add_argument( |
|
"--num_class_images", |
|
type=int, |
|
default=100, |
|
help=( |
|
"Minimal class images for prior preservation loss. If not have enough images, additional images will be" |
|
" sampled with class_prompt." |
|
), |
|
) |
|
parser.add_argument( |
|
"--resolution", type=int, default=512, |
|
help=( |
|
"The resolution for input images, all the images in the train/validation dataset will be resized to this" |
|
" resolution" |
|
), |
|
) |
|
parser.add_argument( |
|
"--center_crop", action="store_true", default=False, |
|
help="Whether to center crop images before resizing to resolution" |
|
) |
|
parser.add_argument( |
|
"--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images." |
|
) |
|
return parent_args |
|
|
|
|
|
class DreamBoothDataset(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, |
|
instance_data_dir, |
|
instance_prompt, |
|
tokenizer, |
|
class_data_dir=None, |
|
class_prompt=None, |
|
size=512, |
|
center_crop=False, |
|
): |
|
self.size = size |
|
self.center_crop = center_crop |
|
self.tokenizer = tokenizer |
|
|
|
self.instance_data_dir = Path(instance_data_dir) |
|
if not self.instance_data_dir.exists(): |
|
raise ValueError("Instance images root doesn't exists.") |
|
|
|
self.instance_images_path = list(Path(instance_data_dir).iterdir()) |
|
print(self.instance_images_path) |
|
self.num_instance_images = len(self.instance_images_path) |
|
self.instance_prompt = instance_prompt |
|
self._length = self.num_instance_images |
|
|
|
if class_data_dir is not None: |
|
self.class_data_dir = Path(class_data_dir) |
|
self.class_data_dir.mkdir(parents=True, exist_ok=True) |
|
self.class_images_path = list(self.class_data_dir.iterdir()) |
|
self.num_class_images = len(self.class_images_path) |
|
self._length = max(self.num_class_images, self.num_instance_images) |
|
self.class_prompt = class_prompt |
|
else: |
|
self.class_data_dir = None |
|
|
|
self.image_transforms = transforms.Compose( |
|
[ |
|
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR), |
|
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size), |
|
transforms.ToTensor(), |
|
transforms.Normalize([0.5], [0.5]), |
|
] |
|
) |
|
|
|
def __len__(self): |
|
return self._length |
|
|
|
def __getitem__(self, index): |
|
example = {} |
|
instance_image = Image.open(self.instance_images_path[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) |
|
example["instance_prompt_ids"] = self.tokenizer( |
|
self.instance_prompt, |
|
padding="do_not_pad", |
|
truncation=True, |
|
max_length=64, |
|
|
|
).input_ids |
|
|
|
if self.class_data_dir: |
|
class_image = Image.open(self.class_images_path[index % self.num_class_images]) |
|
if not class_image.mode == "RGB": |
|
class_image = class_image.convert("RGB") |
|
example["class_images"] = self.image_transforms(class_image) |
|
example["class_prompt_ids"] = self.tokenizer( |
|
self.class_prompt, |
|
padding="do_not_pad", |
|
truncation=True, |
|
|
|
max_length=64, |
|
).input_ids |
|
|
|
return example |
|
|
|
|
|
class PromptDataset(Dataset): |
|
"A simple dataset to prepare the prompts to generate class images on multiple GPUs." |
|
|
|
def __init__(self, prompt, num_samples): |
|
self.prompt = prompt |
|
self.num_samples = num_samples |
|
|
|
def __len__(self): |
|
return self.num_samples |
|
|
|
def __getitem__(self, index): |
|
example = {} |
|
example["prompt"] = self.prompt |
|
example["index"] = index |
|
return example |
|
|
