mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
313 lines
12 KiB
Python
Executable File
313 lines
12 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
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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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import copy
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from PIL import Image,PngImagePlugin
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from ldm.dream_util import Completer,PngWriter
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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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# these two lines prevent a horrible warning message from appearing
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# when the frozen CLIP tokenizer is imported
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import transformers
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transformers.logging.set_verbosity_error()
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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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embedding_path=opt.embedding_path,
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device=opt.device
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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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infile = None
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try:
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if opt.infile is not None:
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infile = open(opt.infile,'r')
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except FileNotFoundError as e:
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print(e)
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exit(-1)
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# preload the model
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t2i.load_model()
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print("\n* Initialization done! Awaiting your command (-h for help, 'q' to quit, 'cd' to change output dir, 'pwd' to print output dir)...")
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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,infile)
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log.close()
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if infile:
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infile.close()
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def main_loop(t2i,parser,log,infile):
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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 = infile.readline() if infile else input("dream> ")
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except EOFError:
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done = True
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break
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if infile and len(command)==0:
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done = True
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break
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if command.startswith(('#','//')):
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continue
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# before splitting, escape single quotes so as not to mess
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# up the parser
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command = command.replace("'","\\'")
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try:
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elements = shlex.split(command)
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except ValueError as e:
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print(str(e))
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continue
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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]=='cd' and len(elements)>1:
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if os.path.exists(elements[1]):
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print(f"setting image output directory to {elements[1]}")
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opt.outdir=elements[1]
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else:
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print(f"directory {elements[1]} does not exist")
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continue
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if elements[0]=='pwd':
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print(f"current output directory is {opt.outdir}")
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continue
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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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file_writer = PngWriter(opt)
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opt.callback = file_writer(write_image)
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run_generator(**vars(opt))
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results = file_writer.files_written
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except AssertionError as e:
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print(e)
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continue
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print("Outputs:")
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write_log_message(t2i,opt,results,log)
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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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seeds = [a[1] for a in results]
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if batch_size > 1:
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seeds = f"(seeds for each batch row: {seeds})"
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else:
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seeds = f"(seeds for individual images: {seeds})"
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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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if opt.grid:
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_write_prompt_to_png(r[0],f'{prompt_str} -g -S{seed} {seeds}')
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else:
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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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switches.append(f'-m{t2i.sampler_name}')
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if opt.variants:
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switches.append(f'-v{opt.variants}')
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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("--from_file",
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dest='infile',
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type=str,
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help="if specified, load prompts from this file")
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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=['ddim', 'k_dpm_2_a', 'k_dpm_2', 'k_euler_a', 'k_euler', 'k_heun', 'k_lms', 'plms'],
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default='k_lms',
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help="which sampler to use (k_lms) - can only be set on command line")
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parser.add_argument('--outdir',
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'-o',
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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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parser.add_argument('--embedding_path',
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type=str,
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help="Path to a pre-trained embedding manager checkpoint - can only be set on command line")
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parser.add_argument('--device',
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'-d',
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type=str,
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default="cuda",
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help="device to run stable diffusion on. defaults to cuda `torch.cuda.current_device()` if avalible")
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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 (slower, but will provide seeds for individual images)")
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parser.add_argument('-b','--batch_size',type=int,default=1,help="number of images to produce per sampling (will not provide seeds for individual images!)")
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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 for img2img mode (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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parser.add_argument('-v','--variants',type=int,help="in img2img mode, the first generated image will get passed back to img2img to generate the requested number of variants")
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parser.add_argument('-x','--skip_normalize',action='store_true',help="skip subprompt weight normalization")
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return parser
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if __name__ == "__main__":
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main()
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