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import gc
import os
import random
import numpy as np
import json
import torch
from PIL import Image, PngImagePlugin
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
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 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 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):
current_time = datetime.now().strftime("%Y%m%d_%H%M%S")
os.makedirs(output_dir, exist_ok=True)
filename = f"image_{current_time}.png"
filepath = os.path.join(output_dir, filename)
metadata_str = json.dumps(metadata)
info = PngImagePlugin.PngInfo()
info.add_text("metadata", metadata_str)
image.save(filepath, "PNG", pnginfo=info)
return filepath
def is_google_colab():
try:
import google.colab
return True
except:
return False |