mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
786b8878d6
* attention maps saving to /tmp * tidy up diffusers branch backporting of cross attention refactoring * base64-encoding the attention maps image for generationResult * cleanup/refactor conditioning.py * attention maps and tokens being sent to web UI * attention maps: restrict count to actual token count and improve robustness * add argument type hint to image_to_dataURL function Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com> Co-authored-by: damian <git@damianstewart.com> Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
949 lines
34 KiB
Python
949 lines
34 KiB
Python
import os
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import re
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import sys
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import shlex
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import copy
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import warnings
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import time
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import traceback
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import yaml
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from ldm.generate import Generate
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from ldm.invoke.globals import Globals
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from ldm.invoke.prompt_parser import PromptParser
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from ldm.invoke.readline import get_completer, Completer
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from ldm.invoke.args import Args, metadata_dumps, metadata_from_png, dream_cmd_from_png
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from ldm.invoke.pngwriter import PngWriter, retrieve_metadata, write_metadata
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from ldm.invoke.image_util import make_grid
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from ldm.invoke.log import write_log
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from ldm.invoke.concepts_lib import Concepts
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from omegaconf import OmegaConf
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from pathlib import Path
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import pyparsing
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# global used in multiple functions (fix)
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infile = None
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def main():
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"""Initialize command-line parsers and the diffusion model"""
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global infile
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print('* Initializing, be patient...')
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opt = Args()
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args = opt.parse_args()
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if not args:
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sys.exit(-1)
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if args.laion400m:
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print('--laion400m flag has been deprecated. Please use --model laion400m instead.')
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sys.exit(-1)
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if args.weights:
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print('--weights argument has been deprecated. Please edit ./configs/models.yaml, and select the weights using --model instead.')
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sys.exit(-1)
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if args.max_loaded_models is not None:
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if args.max_loaded_models <= 0:
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print('--max_loaded_models must be >= 1; using 1')
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args.max_loaded_models = 1
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# alert - setting globals here
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Globals.root = os.path.expanduser(args.root_dir or os.environ.get('INVOKEAI_ROOT') or os.path.abspath('.'))
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Globals.try_patchmatch = args.patchmatch
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print(f'>> InvokeAI runtime directory is "{Globals.root}"')
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# loading here to avoid long delays on startup
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from ldm.generate import Generate
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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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# Loading Face Restoration and ESRGAN Modules
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gfpgan,codeformer,esrgan = load_face_restoration(opt)
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# normalize the config directory relative to root
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if not os.path.isabs(opt.conf):
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opt.conf = os.path.normpath(os.path.join(Globals.root,opt.conf))
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if opt.embeddings:
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if not os.path.isabs(opt.embedding_path):
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embedding_path = os.path.normpath(os.path.join(Globals.root,opt.embedding_path))
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else:
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embedding_path = opt.embedding_path
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else:
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embedding_path = None
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# load the infile as a list of lines
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if opt.infile:
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try:
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if os.path.isfile(opt.infile):
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infile = open(opt.infile, 'r', encoding='utf-8')
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elif opt.infile == '-': # stdin
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infile = sys.stdin
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else:
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raise FileNotFoundError(f'{opt.infile} not found.')
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except (FileNotFoundError, IOError) as e:
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print(f'{e}. Aborting.')
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sys.exit(-1)
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# creating a Generate object:
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try:
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gen = Generate(
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conf = opt.conf,
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model = opt.model,
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sampler_name = opt.sampler_name,
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embedding_path = embedding_path,
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full_precision = opt.full_precision,
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precision = opt.precision,
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gfpgan=gfpgan,
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codeformer=codeformer,
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esrgan=esrgan,
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free_gpu_mem=opt.free_gpu_mem,
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safety_checker=opt.safety_checker,
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max_loaded_models=opt.max_loaded_models,
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)
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except (FileNotFoundError, TypeError, AssertionError):
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emergency_model_reconfigure()
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sys.exit(-1)
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except (IOError, KeyError) as e:
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print(f'{e}. Aborting.')
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sys.exit(-1)
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if opt.seamless:
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print(">> changed to seamless tiling mode")
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# preload the model
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try:
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gen.load_model()
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except AssertionError:
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emergency_model_reconfigure()
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sys.exit(-1)
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# web server loops forever
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if opt.web or opt.gui:
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invoke_ai_web_server_loop(gen, gfpgan, codeformer, esrgan)
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sys.exit(0)
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if not infile:
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print(
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"\n* Initialization done! Awaiting your command (-h for help, 'q' to quit)"
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)
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try:
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main_loop(gen, opt)
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except KeyboardInterrupt:
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print("\ngoodbye!")
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# TODO: main_loop() has gotten busy. Needs to be refactored.
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def main_loop(gen, opt):
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"""prompt/read/execute loop"""
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global infile
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done = False
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doneAfterInFile = infile is not None
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path_filter = re.compile(r'[<>:"/\\|?*]')
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last_results = list()
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model_config = OmegaConf.load(opt.conf)
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# The readline completer reads history from the .dream_history file located in the
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# output directory specified at the time of script launch. We do not currently support
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# changing the history file midstream when the output directory is changed.
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completer = get_completer(opt, models=list(model_config.keys()))
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set_default_output_dir(opt, completer)
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add_embedding_terms(gen, completer)
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output_cntr = completer.get_current_history_length()+1
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# os.pathconf is not available on Windows
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if hasattr(os, 'pathconf'):
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path_max = os.pathconf(opt.outdir, 'PC_PATH_MAX')
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name_max = os.pathconf(opt.outdir, 'PC_NAME_MAX')
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else:
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path_max = 260
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name_max = 255
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while not done:
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operation = 'generate'
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try:
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command = get_next_command(infile)
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except EOFError:
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done = infile is None or doneAfterInFile
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infile = None
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continue
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# skip empty lines
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if not command.strip():
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continue
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if command.startswith(('#', '//')):
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continue
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if len(command.strip()) == 1 and command.startswith('q'):
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done = True
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break
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if not command.startswith('!history'):
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completer.add_history(command)
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if command.startswith('!'):
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command, operation = do_command(command, gen, opt, completer)
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if operation is None:
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continue
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if opt.parse_cmd(command) is None:
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continue
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if opt.init_img:
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try:
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if not opt.prompt:
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oldargs = metadata_from_png(opt.init_img)
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opt.prompt = oldargs.prompt
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print(f'>> Retrieved old prompt "{opt.prompt}" from {opt.init_img}')
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except (OSError, AttributeError, KeyError):
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pass
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if len(opt.prompt) == 0:
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opt.prompt = ''
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# width and height are set by model if not specified
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if not opt.width:
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opt.width = gen.width
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if not opt.height:
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opt.height = gen.height
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# retrieve previous value of init image if requested
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if opt.init_img is not None and re.match('^-\\d+$', opt.init_img):
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try:
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opt.init_img = last_results[int(opt.init_img)][0]
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print(f'>> Reusing previous image {opt.init_img}')
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except IndexError:
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print(
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f'>> No previous initial image at position {opt.init_img} found')
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opt.init_img = None
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continue
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# the outdir can change with each command, so we adjust it here
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set_default_output_dir(opt,completer)
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# try to relativize pathnames
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for attr in ('init_img','init_mask','init_color'):
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if getattr(opt,attr) and not os.path.exists(getattr(opt,attr)):
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basename = getattr(opt,attr)
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path = os.path.join(opt.outdir,basename)
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setattr(opt,attr,path)
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# retrieve previous value of seed if requested
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# Exception: for postprocess operations negative seed values
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# mean "discard the original seed and generate a new one"
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# (this is a non-obvious hack and needs to be reworked)
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if opt.seed is not None and opt.seed < 0 and operation != 'postprocess':
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try:
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opt.seed = last_results[opt.seed][1]
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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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continue
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if opt.strength is None:
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opt.strength = 0.75 if opt.out_direction is None else 0.83
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if opt.with_variations is not None:
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opt.with_variations = split_variations(opt.with_variations)
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if opt.prompt_as_dir and operation == 'generate':
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# sanitize the prompt to a valid folder name
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subdir = path_filter.sub('_', opt.prompt)[:name_max].rstrip(' .')
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# truncate path to maximum allowed length
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# 39 is the length of '######.##########.##########-##.png', plus two separators and a NUL
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subdir = subdir[:(path_max - 39 - len(os.path.abspath(opt.outdir)))]
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current_outdir = os.path.join(opt.outdir, subdir)
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print('Writing files to directory: "' + current_outdir + '"')
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# make sure the output directory exists
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if not os.path.exists(current_outdir):
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os.makedirs(current_outdir)
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else:
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if not os.path.exists(opt.outdir):
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os.makedirs(opt.outdir)
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current_outdir = opt.outdir
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# Here is where the images are actually generated!
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last_results = []
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try:
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file_writer = PngWriter(current_outdir)
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results = [] # list of filename, prompt pairs
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grid_images = dict() # seed -> Image, only used if `opt.grid`
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prior_variations = opt.with_variations or []
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prefix = file_writer.unique_prefix()
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step_callback = make_step_callback(gen, opt, prefix) if opt.save_intermediates > 0 else None
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def image_writer(image, seed, upscaled=False, first_seed=None, use_prefix=None, prompt_in=None, attention_maps_image=None):
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# note the seed is the seed of the current image
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# the first_seed is the original seed that noise is added to
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# when the -v switch is used to generate variations
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nonlocal prior_variations
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nonlocal prefix
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path = None
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if opt.grid:
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grid_images[seed] = image
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elif operation == 'mask':
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filename = f'{prefix}.{use_prefix}.{seed}.png'
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tm = opt.text_mask[0]
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th = opt.text_mask[1] if len(opt.text_mask)>1 else 0.5
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formatted_dream_prompt = f'!mask {opt.input_file_path} -tm {tm} {th}'
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path = file_writer.save_image_and_prompt_to_png(
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image = image,
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dream_prompt = formatted_dream_prompt,
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metadata = {},
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name = filename,
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compress_level = opt.png_compression,
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)
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results.append([path, formatted_dream_prompt])
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else:
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if use_prefix is not None:
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prefix = use_prefix
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postprocessed = upscaled if upscaled else operation=='postprocess'
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opt.prompt = gen.concept_lib().replace_triggers_with_concepts(opt.prompt or prompt_in) # to avoid the problem of non-unique concept triggers
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filename, formatted_dream_prompt = prepare_image_metadata(
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opt,
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prefix,
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seed,
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operation,
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prior_variations,
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postprocessed,
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first_seed
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)
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path = file_writer.save_image_and_prompt_to_png(
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image = image,
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dream_prompt = formatted_dream_prompt,
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metadata = metadata_dumps(
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opt,
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seeds = [seed if opt.variation_amount==0 and len(prior_variations)==0 else first_seed],
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model_hash = gen.model_hash,
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),
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name = filename,
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compress_level = opt.png_compression,
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)
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# update rfc metadata
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if operation == 'postprocess':
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tool = re.match('postprocess:(\w+)',opt.last_operation).groups()[0]
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add_postprocessing_to_metadata(
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opt,
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opt.input_file_path,
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filename,
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tool,
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formatted_dream_prompt,
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)
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if (not postprocessed) or opt.save_original:
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# only append to results if we didn't overwrite an earlier output
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results.append([path, formatted_dream_prompt])
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# so that the seed autocompletes (on linux|mac when -S or --seed specified
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if completer and operation == 'generate':
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completer.add_seed(seed)
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completer.add_seed(first_seed)
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last_results.append([path, seed])
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if operation == 'generate':
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catch_ctrl_c = infile is None # if running interactively, we catch keyboard interrupts
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opt.last_operation='generate'
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try:
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gen.prompt2image(
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image_callback=image_writer,
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step_callback=step_callback,
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catch_interrupts=catch_ctrl_c,
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**vars(opt)
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)
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except (PromptParser.ParsingException, pyparsing.ParseException) as e:
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print('** An error occurred while processing your prompt **')
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print(f'** {str(e)} **')
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elif operation == 'postprocess':
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print(f'>> fixing {opt.prompt}')
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opt.last_operation = do_postprocess(gen,opt,image_writer)
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elif operation == 'mask':
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print(f'>> generating masks from {opt.prompt}')
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do_textmask(gen, opt, image_writer)
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if opt.grid and len(grid_images) > 0:
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grid_img = make_grid(list(grid_images.values()))
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grid_seeds = list(grid_images.keys())
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first_seed = last_results[0][1]
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filename = f'{prefix}.{first_seed}.png'
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formatted_dream_prompt = opt.dream_prompt_str(seed=first_seed,grid=True,iterations=len(grid_images))
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formatted_dream_prompt += f' # {grid_seeds}'
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metadata = metadata_dumps(
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opt,
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seeds = grid_seeds,
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model_hash = gen.model_hash
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)
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path = file_writer.save_image_and_prompt_to_png(
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image = grid_img,
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dream_prompt = formatted_dream_prompt,
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metadata = metadata,
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name = filename
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)
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results = [[path, formatted_dream_prompt]]
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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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log_path = os.path.join(current_outdir, 'invoke_log')
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output_cntr = write_log(results, log_path ,('txt', 'md'), output_cntr)
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print()
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print('goodbye!')
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# TO DO: remove repetitive code and the awkward command.replace() trope
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# Just do a simple parse of the command!
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def do_command(command:str, gen, opt:Args, completer) -> tuple:
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global infile
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operation = 'generate' # default operation, alternative is 'postprocess'
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if command.startswith('!dream'): # in case a stored prompt still contains the !dream command
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command = command.replace('!dream ','',1)
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elif command.startswith('!fix'):
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command = command.replace('!fix ','',1)
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operation = 'postprocess'
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elif command.startswith('!mask'):
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command = command.replace('!mask ','',1)
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operation = 'mask'
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elif command.startswith('!switch'):
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model_name = command.replace('!switch ','',1)
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gen.set_model(model_name)
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add_embedding_terms(gen, completer)
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completer.add_history(command)
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operation = None
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elif command.startswith('!models'):
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gen.model_cache.print_models()
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completer.add_history(command)
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operation = None
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elif command.startswith('!import'):
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path = shlex.split(command)
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if len(path) < 2:
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print('** please provide a path to a .ckpt or .vae model file')
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elif not os.path.exists(path[1]):
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print(f'** {path[1]}: file not found')
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else:
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add_weights_to_config(path[1], gen, opt, completer)
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completer.add_history(command)
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operation = None
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elif command.startswith('!edit'):
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path = shlex.split(command)
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if len(path) < 2:
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print('** please provide the name of a model')
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else:
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edit_config(path[1], gen, opt, completer)
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completer.add_history(command)
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operation = None
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elif command.startswith('!del'):
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path = shlex.split(command)
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if len(path) < 2:
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print('** please provide the name of a model')
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else:
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del_config(path[1], gen, opt, completer)
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completer.add_history(command)
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operation = None
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elif command.startswith('!fetch'):
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file_path = command.replace('!fetch','',1).strip()
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retrieve_dream_command(opt,file_path,completer)
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completer.add_history(command)
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operation = None
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elif command.startswith('!replay'):
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file_path = command.replace('!replay','',1).strip()
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if infile is None and os.path.isfile(file_path):
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infile = open(file_path, 'r', encoding='utf-8')
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completer.add_history(command)
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operation = None
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elif command.startswith('!history'):
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completer.show_history()
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operation = None
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elif command.startswith('!search'):
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search_str = command.replace('!search','',1).strip()
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completer.show_history(search_str)
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operation = None
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elif command.startswith('!clear'):
|
|
completer.clear_history()
|
|
operation = None
|
|
|
|
elif re.match('^!(\d+)',command):
|
|
command_no = re.match('^!(\d+)',command).groups()[0]
|
|
command = completer.get_line(int(command_no))
|
|
completer.set_line(command)
|
|
operation = None
|
|
|
|
else: # not a recognized command, so give the --help text
|
|
command = '-h'
|
|
return command, operation
|
|
|
|
def set_default_output_dir(opt:Args, completer:Completer):
|
|
'''
|
|
If opt.outdir is relative, we add the root directory to it
|
|
normalize the outdir relative to root and make sure it exists.
|
|
'''
|
|
if not os.path.isabs(opt.outdir):
|
|
opt.outdir=os.path.normpath(os.path.join(Globals.root,opt.outdir))
|
|
if not os.path.exists(opt.outdir):
|
|
os.makedirs(opt.outdir)
|
|
completer.set_default_dir(opt.outdir)
|
|
|
|
|
|
def add_weights_to_config(model_path:str, gen, opt, completer):
|
|
print(f'>> Model import in process. Please enter the values needed to configure this model:')
|
|
print()
|
|
|
|
new_config = {}
|
|
new_config['weights'] = model_path
|
|
|
|
done = False
|
|
while not done:
|
|
model_name = input('Short name for this model: ')
|
|
if not re.match('^[\w._-]+$',model_name):
|
|
print('** model name must contain only words, digits and the characters [._-] **')
|
|
else:
|
|
done = True
|
|
new_config['description'] = input('Description of this model: ')
|
|
|
|
completer.complete_extensions(('.yaml','.yml'))
|
|
completer.linebuffer = 'configs/stable-diffusion/v1-inference.yaml'
|
|
|
|
done = False
|
|
while not done:
|
|
new_config['config'] = input('Configuration file for this model: ')
|
|
done = os.path.exists(new_config['config'])
|
|
|
|
done = False
|
|
completer.complete_extensions(('.vae.pt','.vae','.ckpt'))
|
|
while not done:
|
|
vae = input('VAE autoencoder file for this model [None]: ')
|
|
if os.path.exists(vae):
|
|
new_config['vae'] = vae
|
|
done = True
|
|
else:
|
|
done = len(vae)==0
|
|
|
|
completer.complete_extensions(None)
|
|
|
|
for field in ('width','height'):
|
|
done = False
|
|
while not done:
|
|
try:
|
|
completer.linebuffer = '512'
|
|
value = int(input(f'Default image {field}: '))
|
|
assert value >= 64 and value <= 2048
|
|
new_config[field] = value
|
|
done = True
|
|
except:
|
|
print('** Please enter a valid integer between 64 and 2048')
|
|
|
|
make_default = input('Make this the default model? [n] ') in ('y','Y')
|
|
|
|
if write_config_file(opt.conf, gen, model_name, new_config, make_default=make_default):
|
|
completer.add_model(model_name)
|
|
|
|
def del_config(model_name:str, gen, opt, completer):
|
|
current_model = gen.model_name
|
|
if model_name == current_model:
|
|
print("** Can't delete active model. !switch to another model first. **")
|
|
return
|
|
gen.model_cache.del_model(model_name)
|
|
gen.model_cache.commit(opt.conf)
|
|
print(f'** {model_name} deleted')
|
|
completer.del_model(model_name)
|
|
|
|
def edit_config(model_name:str, gen, opt, completer):
|
|
config = gen.model_cache.config
|
|
|
|
if model_name not in config:
|
|
print(f'** Unknown model {model_name}')
|
|
return
|
|
|
|
print(f'\n>> Editing model {model_name} from configuration file {opt.conf}')
|
|
|
|
conf = config[model_name]
|
|
new_config = {}
|
|
completer.complete_extensions(('.yaml','.yml','.ckpt','.vae.pt'))
|
|
for field in ('description', 'weights', 'vae', 'config', 'width','height'):
|
|
completer.linebuffer = str(conf[field]) if field in conf else ''
|
|
new_value = input(f'{field}: ')
|
|
new_config[field] = int(new_value) if field in ('width','height') else new_value
|
|
make_default = input('Make this the default model? [n] ') in ('y','Y')
|
|
completer.complete_extensions(None)
|
|
write_config_file(opt.conf, gen, model_name, new_config, clobber=True, make_default=make_default)
|
|
|
|
def write_config_file(conf_path, gen, model_name, new_config, clobber=False, make_default=False):
|
|
current_model = gen.model_name
|
|
|
|
op = 'modify' if clobber else 'import'
|
|
print('\n>> New configuration:')
|
|
if make_default:
|
|
new_config['default'] = True
|
|
print(yaml.dump({model_name:new_config}))
|
|
if input(f'OK to {op} [n]? ') not in ('y','Y'):
|
|
return False
|
|
|
|
try:
|
|
print('>> Verifying that new model loads...')
|
|
gen.model_cache.add_model(model_name, new_config, clobber)
|
|
assert gen.set_model(model_name) is not None, 'model failed to load'
|
|
except AssertionError as e:
|
|
print(f'** aborting **')
|
|
gen.model_cache.del_model(model_name)
|
|
return False
|
|
|
|
if make_default:
|
|
print('making this default')
|
|
gen.model_cache.set_default_model(model_name)
|
|
|
|
gen.model_cache.commit(conf_path)
|
|
|
|
do_switch = input(f'Keep model loaded? [y]')
|
|
if len(do_switch)==0 or do_switch[0] in ('y','Y'):
|
|
pass
|
|
else:
|
|
gen.set_model(current_model)
|
|
return True
|
|
|
|
def do_textmask(gen, opt, callback):
|
|
image_path = opt.prompt
|
|
if not os.path.exists(image_path):
|
|
image_path = os.path.join(opt.outdir,image_path)
|
|
assert os.path.exists(image_path), '** "{opt.prompt}" not found. Please enter the name of an existing image file to mask **'
|
|
assert opt.text_mask is not None and len(opt.text_mask) >= 1, '** Please provide a text mask with -tm **'
|
|
opt.input_file_path = image_path
|
|
tm = opt.text_mask[0]
|
|
threshold = float(opt.text_mask[1]) if len(opt.text_mask) > 1 else 0.5
|
|
gen.apply_textmask(
|
|
image_path = image_path,
|
|
prompt = tm,
|
|
threshold = threshold,
|
|
callback = callback,
|
|
)
|
|
|
|
def do_postprocess (gen, opt, callback):
|
|
file_path = opt.prompt # treat the prompt as the file pathname
|
|
if opt.new_prompt is not None:
|
|
opt.prompt = opt.new_prompt
|
|
else:
|
|
opt.prompt = None
|
|
|
|
if os.path.dirname(file_path) == '': #basename given
|
|
file_path = os.path.join(opt.outdir,file_path)
|
|
|
|
opt.input_file_path = file_path
|
|
|
|
tool=None
|
|
if opt.facetool_strength > 0:
|
|
tool = opt.facetool
|
|
elif opt.embiggen:
|
|
tool = 'embiggen'
|
|
elif opt.upscale:
|
|
tool = 'upscale'
|
|
elif opt.out_direction:
|
|
tool = 'outpaint'
|
|
elif opt.outcrop:
|
|
tool = 'outcrop'
|
|
opt.save_original = True # do not overwrite old image!
|
|
opt.last_operation = f'postprocess:{tool}'
|
|
try:
|
|
gen.apply_postprocessor(
|
|
image_path = file_path,
|
|
tool = tool,
|
|
facetool_strength = opt.facetool_strength,
|
|
codeformer_fidelity = opt.codeformer_fidelity,
|
|
save_original = opt.save_original,
|
|
upscale = opt.upscale,
|
|
out_direction = opt.out_direction,
|
|
outcrop = opt.outcrop,
|
|
callback = callback,
|
|
opt = opt,
|
|
)
|
|
except OSError:
|
|
print(traceback.format_exc(), file=sys.stderr)
|
|
print(f'** {file_path}: file could not be read')
|
|
return
|
|
except (KeyError, AttributeError):
|
|
print(traceback.format_exc(), file=sys.stderr)
|
|
return
|
|
return opt.last_operation
|
|
|
|
def add_postprocessing_to_metadata(opt,original_file,new_file,tool,command):
|
|
original_file = original_file if os.path.exists(original_file) else os.path.join(opt.outdir,original_file)
|
|
new_file = new_file if os.path.exists(new_file) else os.path.join(opt.outdir,new_file)
|
|
try:
|
|
meta = retrieve_metadata(original_file)['sd-metadata']
|
|
except AttributeError:
|
|
try:
|
|
meta = retrieve_metadata(new_file)['sd-metadata']
|
|
except AttributeError:
|
|
meta = {}
|
|
|
|
if 'image' not in meta:
|
|
meta = metadata_dumps(opt,seeds=[opt.seed])['image']
|
|
meta['image'] = {}
|
|
img_data = meta.get('image')
|
|
pp = img_data.get('postprocessing',[]) or []
|
|
pp.append(
|
|
{
|
|
'tool':tool,
|
|
'dream_command':command,
|
|
}
|
|
)
|
|
meta['image']['postprocessing'] = pp
|
|
write_metadata(new_file,meta)
|
|
|
|
def prepare_image_metadata(
|
|
opt,
|
|
prefix,
|
|
seed,
|
|
operation='generate',
|
|
prior_variations=[],
|
|
postprocessed=False,
|
|
first_seed=None
|
|
):
|
|
|
|
if postprocessed and opt.save_original:
|
|
filename = choose_postprocess_name(opt,prefix,seed)
|
|
else:
|
|
wildcards = dict(opt.__dict__)
|
|
wildcards['prefix'] = prefix
|
|
wildcards['seed'] = seed
|
|
try:
|
|
filename = opt.fnformat.format(**wildcards)
|
|
except KeyError as e:
|
|
print(f'** The filename format contains an unknown key \'{e.args[0]}\'. Will use \'{{prefix}}.{{seed}}.png\' instead')
|
|
filename = f'{prefix}.{seed}.png'
|
|
except IndexError as e:
|
|
print(f'** The filename format is broken or complete. Will use \'{{prefix}}.{{seed}}.png\' instead')
|
|
filename = f'{prefix}.{seed}.png'
|
|
|
|
if opt.variation_amount > 0:
|
|
first_seed = first_seed or seed
|
|
this_variation = [[seed, opt.variation_amount]]
|
|
opt.with_variations = prior_variations + this_variation
|
|
formatted_dream_prompt = opt.dream_prompt_str(seed=first_seed)
|
|
elif len(prior_variations) > 0:
|
|
formatted_dream_prompt = opt.dream_prompt_str(seed=first_seed)
|
|
elif operation == 'postprocess':
|
|
formatted_dream_prompt = '!fix '+opt.dream_prompt_str(seed=seed,prompt=opt.input_file_path)
|
|
else:
|
|
formatted_dream_prompt = opt.dream_prompt_str(seed=seed)
|
|
return filename,formatted_dream_prompt
|
|
|
|
def choose_postprocess_name(opt,prefix,seed) -> str:
|
|
match = re.search('postprocess:(\w+)',opt.last_operation)
|
|
if match:
|
|
modifier = match.group(1) # will look like "gfpgan", "upscale", "outpaint" or "embiggen"
|
|
else:
|
|
modifier = 'postprocessed'
|
|
|
|
counter = 0
|
|
filename = None
|
|
available = False
|
|
while not available:
|
|
if counter == 0:
|
|
filename = f'{prefix}.{seed}.{modifier}.png'
|
|
else:
|
|
filename = f'{prefix}.{seed}.{modifier}-{counter:02d}.png'
|
|
available = not os.path.exists(os.path.join(opt.outdir,filename))
|
|
counter += 1
|
|
return filename
|
|
|
|
def get_next_command(infile=None) -> str: # command string
|
|
if infile is None:
|
|
command = input('invoke> ')
|
|
else:
|
|
command = infile.readline()
|
|
if not command:
|
|
raise EOFError
|
|
else:
|
|
command = command.strip()
|
|
if len(command)>0:
|
|
print(f'#{command}')
|
|
return command
|
|
|
|
def invoke_ai_web_server_loop(gen: Generate, gfpgan, codeformer, esrgan):
|
|
print('\n* --web was specified, starting web server...')
|
|
from backend.invoke_ai_web_server import InvokeAIWebServer
|
|
# Change working directory to the stable-diffusion directory
|
|
os.chdir(
|
|
os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
|
|
)
|
|
|
|
invoke_ai_web_server = InvokeAIWebServer(generate=gen, gfpgan=gfpgan, codeformer=codeformer, esrgan=esrgan)
|
|
|
|
try:
|
|
invoke_ai_web_server.run()
|
|
except KeyboardInterrupt:
|
|
pass
|
|
|
|
def add_embedding_terms(gen,completer):
|
|
'''
|
|
Called after setting the model, updates the autocompleter with
|
|
any terms loaded by the embedding manager.
|
|
'''
|
|
completer.add_embedding_terms(gen.model.embedding_manager.list_terms())
|
|
|
|
def split_variations(variations_string) -> list:
|
|
# shotgun parsing, woo
|
|
parts = []
|
|
broken = False # python doesn't have labeled loops...
|
|
for part in variations_string.split(','):
|
|
seed_and_weight = part.split(':')
|
|
if len(seed_and_weight) != 2:
|
|
print(f'** Could not parse with_variation part "{part}"')
|
|
broken = True
|
|
break
|
|
try:
|
|
seed = int(seed_and_weight[0])
|
|
weight = float(seed_and_weight[1])
|
|
except ValueError:
|
|
print(f'** Could not parse with_variation part "{part}"')
|
|
broken = True
|
|
break
|
|
parts.append([seed, weight])
|
|
if broken:
|
|
return None
|
|
elif len(parts) == 0:
|
|
return None
|
|
else:
|
|
return parts
|
|
|
|
def load_face_restoration(opt):
|
|
try:
|
|
gfpgan, codeformer, esrgan = None, None, None
|
|
if opt.restore or opt.esrgan:
|
|
from ldm.invoke.restoration import Restoration
|
|
restoration = Restoration()
|
|
if opt.restore:
|
|
gfpgan, codeformer = restoration.load_face_restore_models(opt.gfpgan_model_path)
|
|
else:
|
|
print('>> Face restoration disabled')
|
|
if opt.esrgan:
|
|
esrgan = restoration.load_esrgan(opt.esrgan_bg_tile)
|
|
else:
|
|
print('>> Upscaling disabled')
|
|
else:
|
|
print('>> Face restoration and upscaling disabled')
|
|
except (ModuleNotFoundError, ImportError):
|
|
print(traceback.format_exc(), file=sys.stderr)
|
|
print('>> You may need to install the ESRGAN and/or GFPGAN modules')
|
|
return gfpgan,codeformer,esrgan
|
|
|
|
def make_step_callback(gen, opt, prefix):
|
|
destination = os.path.join(opt.outdir,'intermediates',prefix)
|
|
os.makedirs(destination,exist_ok=True)
|
|
print(f'>> Intermediate images will be written into {destination}')
|
|
def callback(img, step):
|
|
if step % opt.save_intermediates == 0 or step == opt.steps-1:
|
|
filename = os.path.join(destination,f'{step:04}.png')
|
|
image = gen.sample_to_image(img)
|
|
image.save(filename,'PNG')
|
|
return callback
|
|
|
|
def retrieve_dream_command(opt,command,completer):
|
|
'''
|
|
Given a full or partial path to a previously-generated image file,
|
|
will retrieve and format the dream command used to generate the image,
|
|
and pop it into the readline buffer (linux, Mac), or print out a comment
|
|
for cut-and-paste (windows)
|
|
|
|
Given a wildcard path to a folder with image png files,
|
|
will retrieve and format the dream command used to generate the images,
|
|
and save them to a file commands.txt for further processing
|
|
'''
|
|
if len(command) == 0:
|
|
return
|
|
|
|
tokens = command.split()
|
|
dir,basename = os.path.split(tokens[0])
|
|
if len(dir) == 0:
|
|
path = os.path.join(opt.outdir,basename)
|
|
else:
|
|
path = tokens[0]
|
|
|
|
if len(tokens) > 1:
|
|
return write_commands(opt, path, tokens[1])
|
|
|
|
cmd = ''
|
|
try:
|
|
cmd = dream_cmd_from_png(path)
|
|
except OSError:
|
|
print(f'## {tokens[0]}: file could not be read')
|
|
except (KeyError, AttributeError, IndexError):
|
|
print(f'## {tokens[0]}: file has no metadata')
|
|
except:
|
|
print(f'## {tokens[0]}: file could not be processed')
|
|
if len(cmd)>0:
|
|
completer.set_line(cmd)
|
|
|
|
def write_commands(opt, file_path:str, outfilepath:str):
|
|
dir,basename = os.path.split(file_path)
|
|
try:
|
|
paths = sorted(list(Path(dir).glob(basename)))
|
|
except ValueError:
|
|
print(f'## "{basename}": unacceptable pattern')
|
|
return
|
|
|
|
commands = []
|
|
cmd = None
|
|
for path in paths:
|
|
try:
|
|
cmd = dream_cmd_from_png(path)
|
|
except (KeyError, AttributeError, IndexError):
|
|
print(f'## {path}: file has no metadata')
|
|
except:
|
|
print(f'## {path}: file could not be processed')
|
|
if cmd:
|
|
commands.append(f'# {path}')
|
|
commands.append(cmd)
|
|
if len(commands)>0:
|
|
dir,basename = os.path.split(outfilepath)
|
|
if len(dir)==0:
|
|
outfilepath = os.path.join(opt.outdir,basename)
|
|
with open(outfilepath, 'w', encoding='utf-8') as f:
|
|
f.write('\n'.join(commands))
|
|
print(f'>> File {outfilepath} with commands created')
|
|
|
|
def emergency_model_reconfigure():
|
|
print()
|
|
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
|
|
print(' You appear to have a missing or misconfigured model file(s). ')
|
|
print(' The script will now exit and run configure_invokeai.py to help fix the problem.')
|
|
print(' After reconfiguration is done, please relaunch invoke.py. ')
|
|
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
|
|
print('configure_invokeai is launching....\n')
|
|
|
|
sys.argv = ['configure_invokeai','--interactive']
|
|
import configure_invokeai
|
|
configure_invokeai.main()
|
|
|