Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,136 Bytes
e0b1273 ab4e488 e0b1273 ab4e488 e0b1273 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 |
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
|