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Running
on
Zero
File size: 7,089 Bytes
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import csv
from pathlib import Path
import random
from typing import Literal
from PIL import Image
import torch
from datasets import concatenate_datasets, load_dataset, interleave_datasets
from qwenimage.types import DataRange
from wandml.core.datamodels import SourceDataType
from wandml.core.source import Source
def parse_datarange(dr: DataRange, length: int, return_as: Literal['list', 'range']='list'):
if not isinstance(length, int):
raise ValueError()
left, right = dr
if left is None:
left = 0
if right is None:
right = length
if (isinstance(left, float) or isinstance(right, float)) and (left<1 and right<1):
left = left * length
right = right * length
if return_as=="list":
return list(range(left, right))
elif return_as=="range":
return range(left, right)
else:
raise ValueError()
class StyleSource(Source):
_data_types = [
SourceDataType(name="image", type=Image.Image),
SourceDataType(name="text", type=str),
]
def __init__(self, data_dir, prompt, set_len=None):
data_dir = Path(data_dir)
self.images = list(data_dir.iterdir())
self.prompt = prompt
self.set_len = set_len
def __len__(self):
if self.set_len is not None:
return self.set_len
else:
return len(self.images)
def __getitem__(self, idx):
idx = idx % len(self.images)
im_pil = Image.open(self.images[idx]).convert("RGB")
return im_pil, self.prompt
class StyleSourceWithRandomRef(Source):
_data_types = [
SourceDataType(name="image", type=Image.Image),
SourceDataType(name="text", type=str),
SourceDataType(name="reference", type=Image.Image),
]
def __init__(self, data_dir, prompt, ref_dir, set_len=None):
data_dir = Path(data_dir)
self.images = list(data_dir.iterdir())
self.ref_images = list(Path(ref_dir).iterdir())
self.prompt = prompt
self.set_len = set_len
def __len__(self):
if self.set_len is not None:
return self.set_len
else:
return len(self.images)
def __getitem__(self, idx):
idx = idx % len(self.images)
im_pil = Image.open(self.images[idx]).convert("RGB")
rand_ref = random.choice(self.ref_images)
ref_pil = Image.open(rand_ref).convert("RGB")
return im_pil, self.prompt, ref_pil
class StyleImagetoImageSource(Source):
_data_types = [
SourceDataType(name="text", type=str),
SourceDataType(name="image", type=Image.Image),
SourceDataType(name="reference", type=Image.Image),
]
def __init__(self, csv_path, base_dir, style_title=None, data_range:DataRange|None=None):
self.csv_path = Path(csv_path)
self.base_dir = Path(base_dir)
self.style_title = style_title
self.data = []
with open(self.csv_path, 'r', encoding='utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
if self.style_title is not None and row['style_title'] != self.style_title:
continue
input_image = self.base_dir / row['input_image']
output_image = self.base_dir / row['output_image_path']
self.data.append({
'input_image': input_image,
'output_image': output_image,
'style_title': row['style_title'],
'prompt': row['prompt']
})
if data_range is not None:
indexes = parse_datarange(data_range, len(self.data))
self.data = [self.data[i] for i in indexes]
print(f"{self.__class__} of len{len(self)}")
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
item = self.data[idx]
prompt = item["prompt"]
input_pil = Image.open(item['input_image']).convert("RGB")
output_pil = Image.open(item['output_image']).convert("RGB")
return prompt, output_pil, input_pil
class RegressionSource(Source):
_data_types = [
SourceDataType(name="data", type=dict),
]
def __init__(self, data_dir, gen_steps=50, data_range:DataRange|None=None):
if not isinstance(data_dir, Path):
data_dir = Path(data_dir)
self.data_paths = list(sorted(data_dir.glob("*.pt")))
if data_range is not None:
indexes = parse_datarange(data_range, len(self.data_paths))
self.data_paths = [self.data_paths[i] for i in indexes]
self.gen_steps = gen_steps
self._len = gen_steps * len(self.data_paths)
print(f"{self.__class__} of len{len(self)}")
def __len__(self):
return self._len
def __getitem__(self, idx):
data_idx = idx // self.gen_steps
step_idx = idx % self.gen_steps
out_dict = torch.load(self.data_paths[data_idx])
t = out_dict.pop(f"t_{step_idx}")
latents_start = out_dict.pop(f"latents_{step_idx}_start")
noise_pred = out_dict.pop(f"noise_pred_{step_idx}")
out_dict["t"] = t
out_dict["latents_start"] = latents_start
out_dict["noise_pred"] = noise_pred
return [out_dict,]
class EditingSource(Source):
_data_types = [
SourceDataType(name="text", type=str),
SourceDataType(name="image", type=Image.Image),
SourceDataType(name="reference", type=Image.Image),
]
EDIT_TYPES = [
"color",
"style",
"replace",
"remove",
"add",
"motion change",
"background change",
]
def __init__(self, data_dir:Path, total_per=1, data_range:DataRange|None=None):
data_dir = Path(data_dir)
self.join_ds = self.build_dataset(data_dir, total_per)
if data_range is not None:
indexes = parse_datarange(data_range, len(self.join_ds))
self.join_ds = self.join_ds.select(indexes)
print(f"{self.__class__} of len{len(self)}")
def build_dataset(self, data_dir:Path, total_per:int):
all_edit_datasets = []
for edit_type in self.EDIT_TYPES:
to_concat = []
for ds_n in range(total_per):
ds = load_dataset("parquet", data_files=str(data_dir/f"{edit_type}_{ds_n:05d}.parquet"), split="train")
to_concat.append(ds)
edit_type_concat = concatenate_datasets(to_concat)
all_edit_datasets.append(edit_type_concat)
# consistent ordering for indexing, also allow extension by increasing total_per
join_ds = interleave_datasets(all_edit_datasets)
return join_ds
def __len__(self):
return len(self.join_ds)
def __getitem__(self, idx):
data = self.join_ds[idx]
reference = data["input_img"]
image = data["output_img"]
text = data["instruction"]
return text, image, reference
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