Delete utils.py

utils.py

@@ -1,182 +0,0 @@
-import gc
-import os
-import random
-import numpy as np
-import json
-import torch
-import uuid
-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
-
-
-@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}.png"   
-    else:
-        filename = str(uuid.uuid4()) + ".png"
-    os.makedirs(output_dir, exist_ok=True)
-    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