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