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import os
import argparse
from dataclasses import dataclass, asdict
from typing import Tuple, Optional
from pathlib import Path
from PIL.Image import Image
import uuid
import logging
STORAGE_DIR: Path = Path.home() / "Pictures"
STORAGE_DIR.mkdir(parents=True, exist_ok=True)
logger = logging.getLogger("run_flux")
def image_completer(prefix, parsed_args, **kwargs):
image_dir = STORAGE_DIR / "Flux"
return [
filename for filename in os.listdir(image_dir)
if filename.startswith(prefix) and os.path.isfile(os.path.join(image_dir, filename))
]
def record_prompt(prompt, filename="prompts.txt"):
try:
with open(filename, "r") as file:
existing_prompts = set(line.strip() for line in file)
except FileNotFoundError:
existing_prompts = set()
if prompt not in existing_prompts:
with open(filename, "a") as file:
file.write(prompt + "\n")
logger.info(f"Recording new prompt: \"{prompt}\"")
else:
logger.info(f"Prompt already exists in the file: \"{prompt}\"")
def load_flux():
import torch
from diffusers import FluxPipeline
pipeline = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
pipeline.enable_model_cpu_offload()
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()
return pipeline
@dataclass(frozen=True)
class GenerateImageConfig:
prompt: str
prompt_2: Optional[str] = None
init_image: Optional[Image] = None
strength: int = 0.0
guidance_scale: float = 0.0
height: int = 1024
width: int = 1024
num_images_per_prompt: int = 1
num_inference_steps: int = 50
def to_dict(self):
return {k: v for k, v in asdict(self).items() if v is not None}
def generate_image(pipeline, config: GenerateImageConfig):
images = pipeline(**config.to_dict()).images
return images
def generate_random_string(length=16) -> str:
return str(uuid.uuid4())
def parse_dimensions(dim_str: str) -> Tuple[int, int]:
try:
width, height = map(int, dim_str.split(':'))
return width, height
except ValueError:
raise argparse.ArgumentError('Dimensions must be in format width:height')
def main():
logging.basicConfig(filename="flux.log", level=logging.INFO, format='%(asctime)s - %(levelname)s -> %(message)s',
datefmt="%m/%d/%Y %I:%M:%S %p")
logger.info("Parsing arguments")
parser = argparse.ArgumentParser(description="Generate some A.I. images", epilog="All done!")
parser.add_argument("-n", "--number", type=int, default=1, help="the number of images you want to generate")
parser.add_argument("-o", "--output", type=str, default="image", help="the name of the output image")
parser.add_argument("-p", "--prompt", type=str, required=True, help="the prompt")
parser.add_argument("-p2", "--prompt2", type=str, help="A second prompt")
parser.add_argument("-gs", "--guideance-scale", type=float, default=0)
parser.add_argument("--strength", type=float)
parser.add_argument("--size", type=parse_dimensions, default="1024:1024", help="the size of the output images")
args = parser.parse_args()
try:
import torch
from diffusers.utils import load_image
logger.info("Choosing model...")
pipeline = load_flux()
pipeline.to(torch.float16)
width, height = args.size
record_prompt(args.prompt)
logger.info(f"Using prompt: \"{args.prompt}\"")
logger.info("Generating image(s)...")
config = GenerateImageConfig(
prompt=args.prompt,
prompt_2=args.prompt2 if args.prompt2 else None,
width=width,
height=height,
strength=args.strength,
guidance_scale=args.guideance_scale,
num_images_per_prompt=args.number
)
images = generate_image(
pipeline=pipeline,
config=config
)
for image in images:
filename = generate_random_string()
filepath = STORAGE_DIR / "Flux" / f"{filename}.png"
logger.info(f"Saving {filepath}...")
image.save(filepath)
logger.info("Finished")
except FileNotFoundError:
print("\n Target image doesn't exist. Exiting...")
exit(0)
except KeyboardInterrupt:
print('\nExiting early...')
exit(0)
except Exception as e:
print(f"An error occured: {e}")
exit(1)
if __name__ == "__main__":
main()
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