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import torch
import os
import base64
import argparse
import argcomplete
from diffusers import FluxPipeline, FluxImg2ImgPipeline
from diffusers.utils import load_image
from typing import Tuple
from pathlib import Path
from PIL 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_img_to_img():
pipeline = FluxImg2ImgPipeline.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
def load_flux():
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
def generate_image(pipeline, prompt, strength=None, prompt_2=None, init_image=None, height=1024, width=1024, guideance_scale=0, num_images_per_prompt=1, num_inference_steps=50):
kwargs = {
"prompt": prompt,
"prompt_2": prompt_2,
"guidance_scale": guideance_scale,
"height":height,
"width": width,
"max_sequence_length": 256,
"num_inference_steps": num_inference_steps,
"num_images_per_prompt": num_images_per_prompt
}
if strength:
kwargs["strength"] = strength
if isinstance(pipeline, FluxImg2ImgPipeline) and init_image is not None:
kwargs["image"] = init_image
images = pipeline(**kwargs).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():
try:
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")
args = parser.parse_args()
logger.info("Choosing model...")
pipeline = load_flux_img_to_img() if args.use_image else load_flux()
if isinstance(pipeline, FluxPipeline):
logger.info("Using text-to-image model")
else:
logger.info("Using image-to-image model")
pipeline.to(torch.float16)
# target_img = STORAGE_DIR / "1517481062292.jpg"
target_img = STORAGE_DIR / "Flux" / "a23aae99-c8f1-4ce5-b91f-0b732774dadd.png"
target_img_path = target_img.resolve(strict=True)
image = Image.open(target_img_path)
init_image = load_image(image).resize((1024, 1024))
width, height = args.size
record_prompt(args.prompt)
logger.info(f"Using prompt: \"{args.prompt}\"")
logger.info("Generating image(s)...")
images = generate_image(
pipeline=pipeline,
init_image=init_image,
prompt=args.prompt,
prompt_2=args.prompt2,
width=width,
height=height,
strength=args.strength,
guideance_scale=args.guideance_scale,
num_images_per_prompt=args.number
)
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)
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")
parser.add_argument("-u", "--use-image", action="store_true", help="use a predefined image")
# parser.add_argument("-b", "--base-image").completer = image_completer
# argcomplete.autocomplete(parser)
if __name__ == "__main__":
main()
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