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Running
on
Zero
File size: 8,510 Bytes
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from typing import Optional, Union, List
import os
import random
import yaml
import glob
from PIL import Image
import torch
from torchvision import transforms
from datasets import load_dataset, concatenate_datasets
from ..pipelines.omnigen2.pipeline_omnigen2 import OmniGen2ImageProcessor
class OmniGen2TrainDataset(torch.utils.data.Dataset):
SYSTEM_PROMPT = "You are a helpful assistant that generates high-quality images based on user instructions."
SYSTEM_PROMPT_DROP = "You are a helpful assistant that generates images."
def __init__(
self,
config_path: str,
tokenizer,
use_chat_template: bool,
max_input_pixels: Optional[Union[int, List[int]]] = None,
max_output_pixels: Optional[int] = None,
max_side_length: Optional[int] = None,
img_scale_num: int = 16,
prompt_dropout_prob: float = 0.0,
ref_img_dropout_prob: float = 0.0,
):
self.max_input_pixels = max_input_pixels
self.max_output_pixels = max_output_pixels
self.max_side_length = max_side_length
self.img_scale_num = img_scale_num
self.prompt_dropout_prob = prompt_dropout_prob
self.ref_img_dropout_prob = ref_img_dropout_prob
with open(config_path, "r") as f:
self.config = yaml.load(f, Loader=yaml.FullLoader)
self.use_chat_template = use_chat_template
self.image_processor = OmniGen2ImageProcessor(vae_scale_factor=img_scale_num, do_resize=True)
data = self._collect_annotations(self.config)
self.data = data
self.tokenizer = tokenizer
def _collect_annotations(self, config):
total_samples = 0
total_ratio = 0
json_datasets = []
for data in config['data']:
data_path, data_type = data['path'], data.get("type", "default")
if os.path.isdir(data_path):
jsonl_files = list(glob.glob(os.path.join(data_path, "**/*.jsonl"), recursive=True)) + list(glob.glob(os.path.join(data_path, "**/*.json"), recursive=True))
json_dataset = load_dataset('json', data_files=jsonl_files, cache_dir=None)['train']
else:
data_ext = os.path.splitext(data_path)[-1]
if data_ext in [".json", ".jsonl"]:
json_dataset = load_dataset('json', data_files=data_path, cache_dir=None)['train']
elif data_ext in [".yml", ".yaml"]:
with open(data_path, "r") as f:
sub_config = yaml.load(f, Loader=yaml.FullLoader)
json_dataset = self._collect_annotations(sub_config)
else:
raise NotImplementedError(
f'Unknown data file extension: "{data_ext}". '
f"Currently, .json, .jsonl .yml .yaml are supported. "
"If you are using a supported format, please set the file extension so that the proper parsing "
"routine can be called."
)
total_ratio += data['ratio']
total_samples += len(json_dataset)
json_datasets.append(json_dataset)
for json_dataset in json_datasets:
target_size = int(len(json_dataset) * data['ratio'] / total_ratio) # normalize the ratio
if target_size <= len(json_dataset):
# Random selection without replacement
indices = random.sample(range(len(json_dataset)), target_size)
else:
# Oversample with replacement
indices = random.choices(range(len(json_dataset)), k=target_size)
json_dataset = json_dataset.select(indices)
json_dataset = concatenate_datasets(json_datasets)
return json_dataset
def clean_data_item(self, data_item):
task_type = data_item['task_type']
prefixs = ["The image portrays ", "The image depicts ", "The image captures ", "The image highlights ", "The image shows ", "这张图片展示了"]
if "text_to_image" in task_type or "t2i" in task_type:
if random.random() < 0.5:
for p in prefixs:
if p in data_item['instruction']:
data_item['instruction'] = data_item['instruction'].replace(p, "")
break
return data_item
def apply_chat_template(self, instruction, system_prompt):
if self.use_chat_template:
prompt = [
{
"role": "system",
"content": system_prompt,
},
{"role": "user", "content": instruction},
]
instruction = self.tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=False)
return instruction
def process_item(self, data_item):
assert data_item['instruction'] is not None
data_item = self.clean_data_item(data_item)
drop_prompt = random.random() < self.prompt_dropout_prob
drop_ref_img = drop_prompt and random.random() < self.ref_img_dropout_prob
if drop_prompt:
instruction = self.apply_chat_template("", self.SYSTEM_PROMPT_DROP)
else:
instruction = self.apply_chat_template(data_item['instruction'], self.SYSTEM_PROMPT)
if not drop_ref_img and 'input_images' in data_item and data_item['input_images'] is not None:
input_images_path = data_item['input_images']
input_images = []
max_input_pixels = self.max_input_pixels[len(input_images_path) - 1] if isinstance(self.max_input_pixels, list) else self.max_input_pixels
for input_image_path in input_images_path:
input_image = Image.open(input_image_path).convert("RGB")
input_image = self.image_processor.preprocess(input_image, max_pixels=max_input_pixels, max_side_length=self.max_side_length)
input_images.append(input_image)
else:
input_images_path, input_images = None, None
output_image_path = data_item['output_image']
output_image = Image.open(output_image_path).convert("RGB")
output_image = self.image_processor.preprocess(output_image, max_pixels=self.max_output_pixels, max_side_length=self.max_side_length)
data = {
'task_type': data_item['task_type'],
'instruction': instruction,
'input_images_path': input_images_path,
'input_images': input_images,
'output_image': output_image,
'output_image_path': output_image_path,
}
return data
def __getitem__(self, index):
max_retries = 12
current_index = index
for attempt in range(max_retries):
try:
data_item = self.data[current_index]
return self.process_item(data_item)
except Exception as e:
if attempt == max_retries - 1:
raise e
else:
# Try a different index for the next attempt
current_index = random.randint(0, len(self.data) - 1)
continue
def __len__(self):
return len(self.data)
class OmniGen2Collator():
def __init__(self, tokenizer, max_token_len):
self.tokenizer = tokenizer
self.max_token_len = max_token_len
def __call__(self, batch):
task_type = [data['task_type'] for data in batch]
instruction = [data['instruction'] for data in batch]
input_images_path = [data['input_images_path'] for data in batch]
input_images = [data['input_images'] for data in batch]
output_image = [data['output_image'] for data in batch]
output_image_path = [data['output_image_path'] for data in batch]
text_inputs = self.tokenizer(
instruction,
padding="longest",
max_length=self.max_token_len,
truncation=True,
return_tensors="pt",
)
data = {
"task_type": task_type,
"text_ids": text_inputs.input_ids,
"text_mask": text_inputs.attention_mask,
"input_images": input_images,
"input_images_path": input_images_path,
"output_image": output_image,
"output_image_path": output_image_path,
}
return data
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