mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
320 lines
12 KiB
Python
Executable File
320 lines
12 KiB
Python
Executable File
#!/usr/bin/env python3
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import argparse
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import shlex
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import atexit
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import os
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import sys
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from PIL import Image,PngImagePlugin
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# readline unavailable on windows systems
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try:
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import readline
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readline_available = True
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except:
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readline_available = False
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debugging = False
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def main():
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''' Initialize command-line parsers and the diffusion model '''
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arg_parser = create_argv_parser()
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opt = arg_parser.parse_args()
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if opt.laion400m:
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# defaults suitable to the older latent diffusion weights
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width = 256
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height = 256
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config = "configs/latent-diffusion/txt2img-1p4B-eval.yaml"
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weights = "models/ldm/text2img-large/model.ckpt"
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else:
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# some defaults suitable for stable diffusion weights
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width = 512
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height = 512
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config = "configs/stable-diffusion/v1-inference.yaml"
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weights = "models/ldm/stable-diffusion-v1/model.ckpt"
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# command line history will be stored in a file called "~/.dream_history"
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if readline_available:
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setup_readline()
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print("* Initializing, be patient...\n")
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sys.path.append('.')
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from pytorch_lightning import logging
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from ldm.simplet2i import T2I
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# creating a simple text2image object with a handful of
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# defaults passed on the command line.
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# additional parameters will be added (or overriden) during
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# the user input loop
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t2i = T2I(width=width,
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height=height,
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batch_size=opt.batch_size,
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outdir=opt.outdir,
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sampler_name=opt.sampler_name,
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weights=weights,
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full_precision=opt.full_precision,
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config=config,
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latent_diffusion_weights=opt.laion400m # this is solely for recreating the prompt
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)
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# make sure the output directory exists
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if not os.path.exists(opt.outdir):
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os.makedirs(opt.outdir)
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# gets rid of annoying messages about random seed
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logging.getLogger("pytorch_lightning").setLevel(logging.ERROR)
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# preload the model
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if not debugging:
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t2i.load_model()
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print("\n* Initialization done! Awaiting your command (-h for help, q to quit)...")
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log_path = os.path.join(opt.outdir,"dream_log.txt")
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with open(log_path,'a') as log:
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cmd_parser = create_cmd_parser()
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main_loop(t2i,cmd_parser,log)
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log.close()
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def main_loop(t2i,parser,log):
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''' prompt/read/execute loop '''
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done = False
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while not done:
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try:
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command = input("dream> ")
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except EOFError:
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done = True
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break
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elements = shlex.split(command)
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if len(elements)==0:
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continue
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if elements[0]=='q': #
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done = True
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break
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if elements[0].startswith('!dream'): # in case a stored prompt still contains the !dream command
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elements.pop(0)
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# rearrange the arguments to mimic how it works in the Dream bot.
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switches = ['']
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switches_started = False
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for el in elements:
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if el[0]=='-' and not switches_started:
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switches_started = True
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if switches_started:
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switches.append(el)
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else:
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switches[0] += el
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switches[0] += ' '
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switches[0] = switches[0][:len(switches[0])-1]
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try:
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opt = parser.parse_args(switches)
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except SystemExit:
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parser.print_help()
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continue
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if len(opt.prompt)==0:
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print("Try again with a prompt!")
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continue
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try:
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if opt.init_img is None:
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results = t2i.txt2img(**vars(opt))
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else:
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results = t2i.img2img(**vars(opt))
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print("Outputs:")
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write_log_message(t2i,opt,results,log)
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except KeyboardInterrupt:
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print('*interrupted*')
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continue
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print("goodbye!")
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def write_log_message(t2i,opt,results,logfile):
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''' logs the name of the output image, its prompt and seed to the terminal, log file, and a Dream text chunk in the PNG metadata '''
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switches = _reconstruct_switches(t2i,opt)
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prompt_str = ' '.join(switches)
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# when multiple images are produced in batch, then we keep track of where each starts
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last_seed = None
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img_num = 1
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batch_size = opt.batch_size or t2i.batch_size
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seenit = {}
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for r in results:
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seed = r[1]
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log_message = (f'{r[0]}: {prompt_str} -S{seed}')
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if batch_size > 1:
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if seed != last_seed:
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img_num = 1
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log_message += f' # (batch image {img_num} of {batch_size})'
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else:
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img_num += 1
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log_message += f' # (batch image {img_num} of {batch_size})'
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last_seed = seed
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print(log_message)
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logfile.write(log_message+"\n")
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logfile.flush()
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if r[0] not in seenit:
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seenit[r[0]] = True
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try:
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_write_prompt_to_png(r[0],f'{prompt_str} -S{seed}')
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except FileNotFoundError:
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print(f"Could not open file '{r[0]}' for reading")
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def _reconstruct_switches(t2i,opt):
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'''Normalize the prompt and switches'''
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switches = list()
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switches.append(f'"{opt.prompt}"')
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switches.append(f'-s{opt.steps or t2i.steps}')
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switches.append(f'-b{opt.batch_size or t2i.batch_size}')
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switches.append(f'-W{opt.width or t2i.width}')
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switches.append(f'-H{opt.height or t2i.height}')
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switches.append(f'-C{opt.cfg_scale or t2i.cfg_scale}')
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if opt.init_img:
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switches.append(f'-I{opt.init_img}')
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if opt.strength and opt.init_img is not None:
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switches.append(f'-f{opt.strength or t2i.strength}')
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if t2i.full_precision:
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switches.append('-F')
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return switches
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def _write_prompt_to_png(path,prompt):
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info = PngImagePlugin.PngInfo()
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info.add_text("Dream",prompt)
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im = Image.open(path)
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im.save(path,"PNG",pnginfo=info)
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def create_argv_parser():
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parser = argparse.ArgumentParser(description="Parse script's command line args")
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parser.add_argument("--laion400m",
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"--latent_diffusion",
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"-l",
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dest='laion400m',
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action='store_true',
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help="fallback to the latent diffusion (laion400m) weights and config")
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parser.add_argument('-n','--iterations',
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type=int,
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default=1,
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help="number of images to generate")
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parser.add_argument('-F','--full_precision',
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dest='full_precision',
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action='store_true',
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help="use slower full precision math for calculations")
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parser.add_argument('-b','--batch_size',
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type=int,
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default=1,
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help="number of images to produce per iteration (faster, but doesn't generate individual seeds")
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parser.add_argument('--sampler','-m',
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dest="sampler_name",
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choices=['plms','ddim', 'klms'],
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default='klms',
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help="which sampler to use (klms) - can only be set on command line")
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parser.add_argument('-o',
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'--outdir',
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type=str,
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default="outputs/img-samples",
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help="directory in which to place generated images and a log of prompts and seeds")
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return parser
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def create_cmd_parser():
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parser = argparse.ArgumentParser(description='Example: dream> a fantastic alien landscape -W1024 -H960 -s100 -n12')
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parser.add_argument('prompt')
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parser.add_argument('-s','--steps',type=int,help="number of steps")
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parser.add_argument('-S','--seed',type=int,help="image seed")
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parser.add_argument('-n','--iterations',type=int,default=1,help="number of samplings to perform")
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parser.add_argument('-b','--batch_size',type=int,default=1,help="number of images to produce per sampling")
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parser.add_argument('-W','--width',type=int,help="image width, multiple of 64")
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parser.add_argument('-H','--height',type=int,help="image height, multiple of 64")
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parser.add_argument('-C','--cfg_scale',default=7.5,type=float,help="prompt configuration scale")
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parser.add_argument('-g','--grid',action='store_true',help="generate a grid")
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parser.add_argument('-i','--individual',action='store_true',help="generate individual files (default)")
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parser.add_argument('-I','--init_img',type=str,help="path to input image (supersedes width and height)")
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parser.add_argument('-f','--strength',default=0.75,type=float,help="strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely")
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return parser
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if readline_available:
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def setup_readline():
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readline.set_completer(Completer(['--steps','-s','--seed','-S','--iterations','-n','--batch_size','-b',
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'--width','-W','--height','-H','--cfg_scale','-C','--grid','-g',
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'--individual','-i','--init_img','-I','--strength','-f']).complete)
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readline.set_completer_delims(" ")
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readline.parse_and_bind('tab: complete')
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load_history()
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def load_history():
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histfile = os.path.join(os.path.expanduser('~'),".dream_history")
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try:
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readline.read_history_file(histfile)
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readline.set_history_length(1000)
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except FileNotFoundError:
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pass
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atexit.register(readline.write_history_file,histfile)
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class Completer():
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def __init__(self,options):
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self.options = sorted(options)
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return
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def complete(self,text,state):
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if text.startswith('-I') or text.startswith('--init_img'):
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return self._image_completions(text,state)
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response = None
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if state == 0:
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# This is the first time for this text, so build a match list.
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if text:
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self.matches = [s
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for s in self.options
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if s and s.startswith(text)]
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else:
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self.matches = self.options[:]
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# Return the state'th item from the match list,
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# if we have that many.
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try:
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response = self.matches[state]
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except IndexError:
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response = None
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return response
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def _image_completions(self,text,state):
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# get the path so far
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if text.startswith('-I'):
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path = text.replace('-I','',1).lstrip()
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elif text.startswith('--init_img='):
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path = text.replace('--init_img=','',1).lstrip()
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matches = list()
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path = os.path.expanduser(path)
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if len(path)==0:
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matches.append(text+'./')
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else:
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dir = os.path.dirname(path)
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dir_list = os.listdir(dir)
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for n in dir_list:
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if n.startswith('.') and len(n)>1:
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continue
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full_path = os.path.join(dir,n)
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if full_path.startswith(path):
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if os.path.isdir(full_path):
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matches.append(os.path.join(os.path.dirname(text),n)+'/')
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elif n.endswith('.png'):
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matches.append(os.path.join(os.path.dirname(text),n))
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try:
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response = matches[state]
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except IndexError:
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response = None
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return response
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if __name__ == "__main__":
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main()
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