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import torch
from torch.utils.data import Dataset
import torchvision.transforms as T
import os
import random
import numpy as np
from PIL import Image, ImageDraw
from datasets import load_dataset
from .trainer import OminiModel, get_config, train
from ..pipeline.flux_omini import Condition, convert_to_condition, generate
class ImageConditionDataset(Dataset):
def __init__(
self,
base_dataset,
condition_size=(512, 512),
target_size=(512, 512),
condition_type: str = "canny",
drop_text_prob: float = 0.1,
drop_image_prob: float = 0.1,
return_pil_image: bool = False,
position_scale=1.0,
):
self.base_dataset = base_dataset
self.condition_size = condition_size
self.target_size = target_size
self.condition_type = condition_type
self.drop_text_prob = drop_text_prob
self.drop_image_prob = drop_image_prob
self.return_pil_image = return_pil_image
self.position_scale = position_scale
self.to_tensor = T.ToTensor()
def __len__(self):
return len(self.base_dataset)
def __get_condition__(self, image, condition_type):
condition_size = self.condition_size
position_delta = np.array([0, 0])
if condition_type in ["canny", "coloring", "deblurring", "depth"]:
image, kwargs = image.resize(condition_size), {}
if condition_type == "deblurring":
blur_radius = random.randint(1, 10)
kwargs["blur_radius"] = blur_radius
condition_img = convert_to_condition(condition_type, image, **kwargs)
elif condition_type == "depth_pred":
depth_img = convert_to_condition("depth", image)
condition_img = image.resize(condition_size)
image = depth_img.resize(condition_size)
elif condition_type == "fill":
condition_img = image.resize(condition_size).convert("RGB")
w, h = image.size
x1, x2 = sorted([random.randint(0, w), random.randint(0, w)])
y1, y2 = sorted([random.randint(0, h), random.randint(0, h)])
mask = Image.new("L", image.size, 0)
draw = ImageDraw.Draw(mask)
draw.rectangle([x1, y1, x2, y2], fill=255)
if random.random() > 0.5:
mask = Image.eval(mask, lambda a: 255 - a)
condition_img = Image.composite(
image, Image.new("RGB", image.size, (0, 0, 0)), mask
)
elif condition_type == "sr":
condition_img = image.resize(condition_size)
position_delta = np.array([0, -condition_size[0] // 16])
else:
raise ValueError(f"Condition type {condition_type} is not implemented.")
return condition_img, position_delta
def __getitem__(self, idx):
image = self.base_dataset[idx]["jpg"]
image = image.resize(self.target_size).convert("RGB")
description = self.base_dataset[idx]["json"]["prompt"]
condition_size = self.condition_size
position_scale = self.position_scale
condition_img, position_delta = self.__get_condition__(
image, self.condition_type
)
# Randomly drop text or image (for training)
drop_text = random.random() < self.drop_text_prob
drop_image = random.random() < self.drop_image_prob
if drop_text:
description = ""
if drop_image:
condition_img = Image.new("RGB", condition_size, (0, 0, 0))
return {
"image": self.to_tensor(image),
"condition_0": self.to_tensor(condition_img),
"condition_type_0": self.condition_type,
"position_delta_0": position_delta,
"description": description,
**({"pil_image": [image, condition_img]} if self.return_pil_image else {}),
**({"position_scale_0": position_scale} if position_scale != 1.0 else {}),
}
@torch.no_grad()
def test_function(model, save_path, file_name):
condition_size = model.training_config["dataset"]["condition_size"]
target_size = model.training_config["dataset"]["target_size"]
position_delta = model.training_config["dataset"].get("position_delta", [0, 0])
position_scale = model.training_config["dataset"].get("position_scale", 1.0)
adapter = model.adapter_names[2]
condition_type = model.training_config["condition_type"]
test_list = []
if condition_type in ["canny", "coloring", "deblurring", "depth"]:
image = Image.open("assets/vase_hq.jpg")
image = image.resize(condition_size)
condition_img = convert_to_condition(condition_type, image, 5)
condition = Condition(condition_img, adapter, position_delta, position_scale)
test_list.append((condition, "A beautiful vase on a table."))
elif condition_type == "depth_pred":
image = Image.open("assets/vase_hq.jpg")
image = image.resize(condition_size)
condition = Condition(image, adapter, position_delta, position_scale)
test_list.append((condition, "A beautiful vase on a table."))
elif condition_type == "fill":
condition_img = (
Image.open("./assets/vase_hq.jpg").resize(condition_size).convert("RGB")
)
mask = Image.new("L", condition_img.size, 0)
draw = ImageDraw.Draw(mask)
a = condition_img.size[0] // 4
b = a * 3
draw.rectangle([a, a, b, b], fill=255)
condition_img = Image.composite(
condition_img, Image.new("RGB", condition_img.size, (0, 0, 0)), mask
)
condition = Condition(condition, adapter, position_delta, position_scale)
test_list.append((condition, "A beautiful vase on a table."))
elif condition_type == "super_resolution":
image = Image.open("assets/vase_hq.jpg")
image = image.resize(condition_size)
condition = Condition(image, adapter, position_delta, position_scale)
test_list.append((condition, "A beautiful vase on a table."))
else:
raise NotImplementedError
os.makedirs(save_path, exist_ok=True)
for i, (condition, prompt) in enumerate(test_list):
generator = torch.Generator(device=model.device)
generator.manual_seed(42)
res = generate(
model.flux_pipe,
prompt=prompt,
conditions=[condition],
height=target_size[1],
width=target_size[0],
generator=generator,
model_config=model.model_config,
kv_cache=model.model_config.get("independent_condition", False),
)
file_path = os.path.join(save_path, f"{file_name}_{condition_type}_{i}.jpg")
res.images[0].save(file_path)
def main():
# Initialize
config = get_config()
training_config = config["train"]
torch.cuda.set_device(int(os.environ.get("LOCAL_RANK", 0)))
# Load dataset text-to-image-2M
dataset = load_dataset(
"webdataset",
data_files={"train": training_config["dataset"]["urls"]},
split="train",
cache_dir="cache/t2i2m",
num_proc=32,
)
# Initialize custom dataset
dataset = ImageConditionDataset(
dataset,
condition_size=training_config["dataset"]["condition_size"],
target_size=training_config["dataset"]["target_size"],
condition_type=training_config["condition_type"],
drop_text_prob=training_config["dataset"]["drop_text_prob"],
drop_image_prob=training_config["dataset"]["drop_image_prob"],
position_scale=training_config["dataset"].get("position_scale", 1.0),
)
# Initialize model
trainable_model = OminiModel(
flux_pipe_id=config["flux_path"],
lora_config=training_config["lora_config"],
device=f"cuda",
dtype=getattr(torch, config["dtype"]),
optimizer_config=training_config["optimizer"],
model_config=config.get("model", {}),
gradient_checkpointing=training_config.get("gradient_checkpointing", False),
)
train(dataset, trainable_model, config, test_function)
if __name__ == "__main__":
main()
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