utils.py

import gc
import os
import random
import numpy as np
import json
import torch
import uuid
from PIL import Image
from datetime import datetime
from dataclasses import dataclass
from typing import Callable, Dict, Optional, Tuple
from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
)

MAX_SEED = np.iinfo(np.int32).max


@dataclass
class StyleConfig:
    prompt: str
    negative_prompt: str


def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed


def seed_everything(seed: int) -> torch.Generator:
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    np.random.seed(seed)
    generator = torch.Generator()
    generator.manual_seed(seed)
    return generator


def parse_aspect_ratio(aspect_ratio: str) -> Optional[Tuple[int, int]]:
    if aspect_ratio == "Custom":
        return None
    width, height = aspect_ratio.split(" x ")
    return int(width), int(height)


def aspect_ratio_handler(
    aspect_ratio: str, custom_width: int, custom_height: int
) -> Tuple[int, int]:
    if aspect_ratio == "Custom":
        return custom_width, custom_height
    else:
        width, height = parse_aspect_ratio(aspect_ratio)
        return width, height


def get_scheduler(scheduler_config: Dict, name: str) -> Optional[Callable]:
    scheduler_factory_map = {
        "DPM++ 2M Karras": lambda: DPMSolverMultistepScheduler.from_config(
            scheduler_config, use_karras_sigmas=True
        ),
        "DPM++ SDE Karras": lambda: DPMSolverSinglestepScheduler.from_config(
            scheduler_config, use_karras_sigmas=True
        ),
        "DPM++ 2M SDE Karras": lambda: DPMSolverMultistepScheduler.from_config(
            scheduler_config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++"
        ),
        "Euler": lambda: EulerDiscreteScheduler.from_config(scheduler_config),
        "Euler a": lambda: EulerAncestralDiscreteScheduler.from_config(
            scheduler_config
        ),
        "DDIM": lambda: DDIMScheduler.from_config(scheduler_config),
    }
    return scheduler_factory_map.get(name, lambda: None)()


def free_memory() -> None:
    torch.cuda.empty_cache()
    gc.collect()


def preprocess_prompt(
    style_dict,
    style_name: str,
    positive: str,
    negative: str = "",
    add_style: bool = True,
) -> Tuple[str, str]:
    p, n = style_dict.get(style_name, style_dict["(None)"])

    if add_style and positive.strip():
        formatted_positive = p.format(prompt=positive)
    else:
        formatted_positive = positive

    combined_negative = n
    if negative.strip():
        if combined_negative:
            combined_negative += ", " + negative
        else:
            combined_negative = negative

    return formatted_positive, combined_negative


def common_upscale(
    samples: torch.Tensor,
    width: int,
    height: int,
    upscale_method: str,
) -> torch.Tensor:
    return torch.nn.functional.interpolate(
        samples, size=(height, width), mode=upscale_method
    )


def upscale(
    samples: torch.Tensor, upscale_method: str, scale_by: float
) -> torch.Tensor:
    width = round(samples.shape[3] * scale_by)
    height = round(samples.shape[2] * scale_by)
    return common_upscale(samples, width, height, upscale_method)


def load_wildcard_files(wildcard_dir: str) -> Dict[str, str]:
    wildcard_files = {}
    for file in os.listdir(wildcard_dir):
        if file.endswith(".txt"):
            key = f"__{file.split('.')[0]}__"  # Create a key like __character__
            wildcard_files[key] = os.path.join(wildcard_dir, file)
    return wildcard_files


def get_random_line_from_file(file_path: str) -> str:
    with open(file_path, "r") as file:
        lines = file.readlines()
        if not lines:
            return ""
        return random.choice(lines).strip()


def add_wildcard(prompt: str, wildcard_files: Dict[str, str]) -> str:
    for key, file_path in wildcard_files.items():
        if key in prompt:
            wildcard_line = get_random_line_from_file(file_path)
            prompt = prompt.replace(key, wildcard_line)
    return prompt


def preprocess_image_dimensions(width, height):
    if width % 8 != 0:
        width = width - (width % 8)
    if height % 8 != 0:
        height = height - (height % 8)
    return width, height


def save_image(image, metadata, output_dir, is_colab):
    if is_colab:
        current_time = datetime.now().strftime("%Y%m%d_%H%M%S")
        filename = f"image_{current_time}.jpg"   
    else:
        filename = str(uuid.uuid4()) + ".jpg"
    os.makedirs(output_dir, exist_ok=True)
    filepath = os.path.join(output_dir, filename)
    
    # Lưu metadata dưới dạng tệp văn bản đi kèm
    metadata_str = json.dumps(metadata)
    metadata_filepath = os.path.join(output_dir, f"{filename}.json")
    with open(metadata_filepath, 'w') as f:
        f.write(metadata_str)
    
    # Lưu hình ảnh dưới định dạng JPEG
    image.save(filepath, "JPEG")
    return filepath


def is_google_colab():
    try:
        import google.colab
        return True
    except:
        return False