Create utils.py

utils.py

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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