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4d012e7
1
Parent(s):
c490d71
Update utils.py
Browse files
utils.py
CHANGED
@@ -3,19 +3,12 @@ import os
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import random
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import numpy as np
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import json
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import torch
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import uuid
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from PIL import Image, PngImagePlugin
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from datetime import datetime
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from dataclasses import dataclass
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from typing import Callable, Dict, Optional, Tuple
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DDIMScheduler,
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DPMSolverMultistepScheduler,
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DPMSolverSinglestepScheduler,
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EulerAncestralDiscreteScheduler,
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EulerDiscreteScheduler,
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)
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MAX_SEED = np.iinfo(np.int32).max
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@@ -32,14 +25,6 @@ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
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return seed
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def seed_everything(seed: int) -> torch.Generator:
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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np.random.seed(seed)
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generator = torch.Generator()
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generator.manual_seed(seed)
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return generator
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def parse_aspect_ratio(aspect_ratio: str) -> Optional[Tuple[int, int]]:
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if aspect_ratio == "Custom":
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@@ -58,24 +43,6 @@ def aspect_ratio_handler(
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return width, height
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def get_scheduler(scheduler_config: Dict, name: str) -> Optional[Callable]:
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scheduler_factory_map = {
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"DPM++ 2M Karras": lambda: DPMSolverMultistepScheduler.from_config(
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scheduler_config, use_karras_sigmas=True
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),
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"DPM++ SDE Karras": lambda: DPMSolverSinglestepScheduler.from_config(
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scheduler_config, use_karras_sigmas=True
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),
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"DPM++ 2M SDE Karras": lambda: DPMSolverMultistepScheduler.from_config(
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scheduler_config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++"
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),
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"Euler": lambda: EulerDiscreteScheduler.from_config(scheduler_config),
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"Euler a": lambda: EulerAncestralDiscreteScheduler.from_config(
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scheduler_config
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),
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"DDIM": lambda: DDIMScheduler.from_config(scheduler_config),
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}
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return scheduler_factory_map.get(name, lambda: None)()
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def free_memory() -> None:
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@@ -107,25 +74,6 @@ def preprocess_prompt(
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return formatted_positive, combined_negative
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def common_upscale(
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samples: torch.Tensor,
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width: int,
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height: int,
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upscale_method: str,
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) -> torch.Tensor:
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return torch.nn.functional.interpolate(
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samples, size=(height, width), mode=upscale_method
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)
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def upscale(
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samples: torch.Tensor, upscale_method: str, scale_by: float
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) -> torch.Tensor:
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width = round(samples.shape[3] * scale_by)
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height = round(samples.shape[2] * scale_by)
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return common_upscale(samples, width, height, upscale_method)
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def get_random_line_from_file(file_path: str) -> str:
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with open(file_path, "r") as file:
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import random
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import numpy as np
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import json
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import uuid
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from PIL import Image, PngImagePlugin
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from datetime import datetime
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from dataclasses import dataclass
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from typing import Callable, Dict, Optional, Tuple
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MAX_SEED = np.iinfo(np.int32).max
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return seed
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def parse_aspect_ratio(aspect_ratio: str) -> Optional[Tuple[int, int]]:
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if aspect_ratio == "Custom":
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return width, height
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def free_memory() -> None:
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return formatted_positive, combined_negative
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def get_random_line_from_file(file_path: str) -> str:
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with open(file_path, "r") as file:
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