mirror of
https://github.com/invoke-ai/InvokeAI
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516 lines
16 KiB
Python
Executable File
516 lines
16 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 os
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import sys
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import copy
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import warnings
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import ldm.dream.readline
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from ldm.dream.pngwriter import PngWriter, PromptFormatter
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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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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(
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width=width,
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height=height,
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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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# load GFPGAN if requested
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if opt.use_gfpgan:
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print('\n* --gfpgan was specified, loading gfpgan...')
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with warnings.catch_warnings():
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warnings.filterwarnings('ignore', category=DeprecationWarning)
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try:
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model_path = os.path.join(
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opt.gfpgan_dir, opt.gfpgan_model_path
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)
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if not os.path.isfile(model_path):
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raise Exception(
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'GFPGAN model not found at path ' + model_path
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)
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sys.path.append(os.path.abspath(opt.gfpgan_dir))
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from gfpgan import GFPGANer
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bg_upsampler = load_gfpgan_bg_upsampler(
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opt.gfpgan_bg_upsampler, opt.gfpgan_bg_tile
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)
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t2i.gfpgan = GFPGANer(
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model_path=model_path,
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upscale=opt.gfpgan_upscale,
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arch='clean',
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channel_multiplier=2,
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bg_upsampler=bg_upsampler,
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)
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except Exception:
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import traceback
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print('Error loading GFPGAN:', file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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print(
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"\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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)
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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, opt.outdir, 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, outdir, parser, log, infile):
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"""prompt/read/execute loop"""
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done = False
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last_seeds = []
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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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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 {outdir}')
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continue
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if elements[0].startswith(
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'!dream'
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): # 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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if opt.seed is not None and opt.seed < 0: # retrieve previous value!
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try:
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opt.seed = last_seeds[opt.seed]
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print(f'reusing previous seed {opt.seed}')
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except IndexError:
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print(f'No previous seed at position {opt.seed} found')
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opt.seed = None
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normalized_prompt = PromptFormatter(t2i, opt).normalize_prompt()
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individual_images = not opt.grid
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try:
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file_writer = PngWriter(outdir, normalized_prompt, opt.batch_size)
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callback = file_writer.write_image if individual_images else None
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image_list = t2i.prompt2image(image_callback=callback, **vars(opt))
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results = (
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file_writer.files_written if individual_images else image_list
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)
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if opt.grid and len(results) > 0:
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grid_img = file_writer.make_grid([r[0] for r in results])
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filename = file_writer.unique_filename(results[0][1])
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seeds = [a[1] for a in results]
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results = [[filename, seeds]]
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metadata_prompt = f'{normalized_prompt} -S{results[0][1]}'
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file_writer.save_image_and_prompt_to_png(
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grid_img, metadata_prompt, filename
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)
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last_seeds = [r[1] for r in results]
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except AssertionError as e:
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print(e)
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continue
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except OSError 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, normalized_prompt, results, log)
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print('goodbye!')
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def load_gfpgan_bg_upsampler(bg_upsampler, bg_tile=400):
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import torch
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if bg_upsampler == 'realesrgan':
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if not torch.cuda.is_available(): # CPU
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import warnings
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warnings.warn(
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'The unoptimized RealESRGAN is slow on CPU. We do not use it. '
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'If you really want to use it, please modify the corresponding codes.'
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)
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bg_upsampler = None
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else:
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from basicsr.archs.rrdbnet_arch import RRDBNet
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from realesrgan import RealESRGANer
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model = RRDBNet(
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num_in_ch=3,
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num_out_ch=3,
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num_feat=64,
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num_block=23,
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num_grow_ch=32,
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scale=2,
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)
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bg_upsampler = RealESRGANer(
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scale=2,
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model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
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model=model,
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tile=bg_tile,
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tile_pad=10,
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pre_pad=0,
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half=True,
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) # need to set False in CPU mode
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else:
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bg_upsampler = None
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return bg_upsampler
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# variant generation is going to be superseded by a generalized
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# "prompt-morph" functionality
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# def generate_variants(t2i,outdir,opt,previous_gens):
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# variants = []
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# print(f"Generating {opt.variants} variant(s)...")
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# newopt = copy.deepcopy(opt)
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# newopt.iterations = 1
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# newopt.variants = None
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# for r in previous_gens:
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# newopt.init_img = r[0]
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# prompt = PromptFormatter(t2i,newopt).normalize_prompt()
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# print(f"] generating variant for {newopt.init_img}")
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# for j in range(0,opt.variants):
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# try:
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# file_writer = PngWriter(outdir,prompt,newopt.batch_size)
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# callback = file_writer.write_image
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# t2i.prompt2image(image_callback=callback,**vars(newopt))
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# results = file_writer.files_written
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# variants.append([prompt,results])
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# except AssertionError as e:
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# print(e)
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# continue
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# print(f'{opt.variants} variants generated')
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# return variants
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def write_log_message(t2i, prompt, 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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last_seed = None
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img_num = 1
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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} -S{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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def create_argv_parser():
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parser = argparse.ArgumentParser(
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description="Parse script's command line args"
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)
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parser.add_argument(
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'--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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)
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parser.add_argument(
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'--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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)
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parser.add_argument(
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'-n',
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'--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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)
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parser.add_argument(
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'-F',
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'--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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)
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parser.add_argument(
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'--sampler',
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'-m',
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dest='sampler_name',
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choices=[
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'ddim',
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'k_dpm_2_a',
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'k_dpm_2',
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'k_euler_a',
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'k_euler',
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'k_heun',
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'k_lms',
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'plms',
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],
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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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)
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parser.add_argument(
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'--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 (outputs/img-samples',
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)
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parser.add_argument(
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'--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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)
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parser.add_argument(
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'--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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)
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# GFPGAN related args
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parser.add_argument(
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'--gfpgan',
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dest='use_gfpgan',
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action='store_true',
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help='load gfpgan for use in the dreambot. Note: Enabling GFPGAN will require more GPU memory',
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)
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parser.add_argument(
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'--gfpgan_upscale',
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type=int,
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default=2,
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help='The final upsampling scale of the image. Default: 2. Only used if --gfpgan is specified',
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)
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parser.add_argument(
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'--gfpgan_bg_upsampler',
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type=str,
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default='realesrgan',
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help='Background upsampler. Default: realesrgan. Options: realesrgan, none. Only used if --gfpgan is specified',
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)
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parser.add_argument(
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'--gfpgan_bg_tile',
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type=int,
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default=400,
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help='Tile size for background sampler, 0 for no tile during testing. Default: 400. Only used if --gfpgan is specified',
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)
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parser.add_argument(
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'--gfpgan_model_path',
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type=str,
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default='experiments/pretrained_models/GFPGANv1.3.pth',
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help='indicates the path to the GFPGAN model, relative to --gfpgan_dir. Only used if --gfpgan is specified',
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)
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parser.add_argument(
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'--gfpgan_dir',
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type=str,
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default='../GFPGAN',
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help='indicates the directory containing the GFPGAN code. Only used if --gfpgan is specified',
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)
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return parser
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def create_cmd_parser():
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parser = argparse.ArgumentParser(
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description='Example: dream> a fantastic alien landscape -W1024 -H960 -s100 -n12'
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)
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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(
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'-S',
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'--seed',
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type=int,
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help='image seed; a +ve integer, or use -1 for the previous seed, -2 for the one before that, etc',
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)
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parser.add_argument(
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'-n',
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'--iterations',
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type=int,
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default=1,
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help='number of samplings to perform (slower, but will provide seeds for individual images)',
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)
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parser.add_argument(
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'-b',
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'--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 sampling (will not provide seeds for individual images!)',
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)
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parser.add_argument(
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'-W', '--width', type=int, help='image width, multiple of 64'
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)
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parser.add_argument(
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'-H', '--height', type=int, help='image height, multiple of 64'
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)
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parser.add_argument(
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'-C',
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'--cfg_scale',
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default=7.5,
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type=float,
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help='prompt configuration scale',
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)
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parser.add_argument(
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'-g', '--grid', action='store_true', help='generate a grid'
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)
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parser.add_argument(
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'-i',
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'--individual',
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action='store_true',
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help='generate individual files (default)',
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)
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parser.add_argument(
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'-I',
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'--init_img',
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type=str,
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help='path to input image for img2img mode (supersedes width and height)',
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)
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parser.add_argument(
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'-f',
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'--strength',
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default=0.75,
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type=float,
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help='strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely',
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)
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parser.add_argument(
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'-G',
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'--gfpgan_strength',
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default=None,
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type=float,
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help='The strength at which to apply the GFPGAN model to the result, in order to improve faces.',
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)
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# variants is going to be superseded by a generalized "prompt-morph" function
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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(
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'-x',
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'--skip_normalize',
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action='store_true',
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help='skip subprompt weight normalization',
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)
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return parser
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if __name__ == '__main__':
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main()
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