Mirrored_Repos/InvokeAI
1
0
Fork 0
mirror of https://github.com/invoke-ai/InvokeAI synced 2024-08-30 20:32:17 +00:00
Code Issues Packages Projects Releases Wiki Activity
0eb07b7488
InvokeAI/ldm/invoke/args.py
Lincoln Stein 799dc6d0df acceptable integration of new prompting system and inpainting 
This was a difficult merge because both PR #1108 and #1243 made
changes to obscure parts of the diffusion code.

- prompt weighting, merging and cross-attention working
  - cross-attention does not work with runwayML inpainting
    model, but weighting and merging are tested and working
- CLI command parsing code rewritten in order to get embedded
  quotes right
- --hires now works with runwayML inpainting
- --embiggen does not work with runwayML and will give an error
- Added an --invert option to invert masks applied to inpainting
- Updated documentation
2022-10-27 01:51:35 -04:00

1030 lines
37 KiB
Python
Raw Blame History

"""Helper class for dealing with image generation arguments.
The Args class parses both the command line (shell) arguments, as well as the
command string passed at the invoke> prompt. It serves as the definitive repository
of all the arguments used by Generate and their default values, and implements the
preliminary metadata standards discussed here:
https://github.com/lstein/stable-diffusion/issues/266
To use:
opt = Args()
# Read in the command line options:
# this returns a namespace object like the underlying argparse library)
# You do not have to use the return value, but you can check it against None
# to detect illegal arguments on the command line.
args = opt.parse_args()
if not args:
print('oops')
sys.exit(-1)
# read in a command passed to the invoke> prompt:
opts = opt.parse_cmd('do androids dream of electric sheep? -H256 -W1024 -n4')
# The Args object acts like a namespace object
print(opt.model)
You can set attributes in the usual way, use vars(), etc.:
opt.model = 'something-else'
do_something(**vars(a))
It is helpful in saving metadata:
# To get a json representation of all the values, allowing
# you to override any values dynamically
j = opt.json(seed=42)
# To get the prompt string with the switches, allowing you
# to override any values dynamically
j = opt.dream_prompt_str(seed=42)
If you want to access the namespace objects from the shell args or the
parsed command directly, you may use the values returned from the
original calls to parse_args() and parse_cmd(), or get them later
using the _arg_switches and _cmd_switches attributes. This can be
useful if both the args and the command contain the same attribute and
you wish to apply logic as to which one to use. For example:
a = Args()
args = a.parse_args()
opts = a.parse_cmd(string)
do_grid = args.grid or opts.grid
To add new attributes, edit the _create_arg_parser() and
_create_dream_cmd_parser() methods.
**Generating and retrieving sd-metadata**
To generate a dict representing RFC266 metadata:
metadata = metadata_dumps(opt,<seeds,model_hash,postprocesser>)
This will generate an RFC266 dictionary that can then be turned into a JSON
and written to the PNG file. The optional seeds, weights, model_hash and
postprocesser arguments are not available to the opt object and so must be
provided externally. See how invoke.py does it.
Note that this function was originally called format_metadata() and a wrapper
is provided that issues a deprecation notice.
To retrieve a (series of) opt objects corresponding to the metadata, do this:
opt_list = metadata_loads(metadata)
The metadata should be pulled out of the PNG image. pngwriter has a method
retrieve_metadata that will do this, or you can do it in one swell foop
with metadata_from_png():
opt_list = metadata_from_png('/path/to/image_file.png')
"""
import argparse
from argparse import Namespace, RawTextHelpFormatter
import pydoc
import json
import hashlib
import os
import re
import shlex
import copy
import base64
import functools
import ldm.invoke.pngwriter
from ldm.invoke.prompt_parser import split_weighted_subprompts
SAMPLER_CHOICES = [
'ddim',
'k_dpm_2_a',
'k_dpm_2',
'k_euler_a',
'k_euler',
'k_heun',
'k_lms',
'plms',
]
PRECISION_CHOICES = [
'auto',
'float32',
'autocast',
'float16',
]
# is there a way to pick this up during git commits?
APP_ID = 'invoke-ai/InvokeAI'
APP_VERSION = 'v2.02'
class ArgFormatter(argparse.RawTextHelpFormatter):
# use defined argument order to display usage
def _format_usage(self, usage, actions, groups, prefix):
if prefix is None:
prefix = 'usage: '
# if usage is specified, use that
if usage is not None:
usage = usage % dict(prog=self._prog)
# if no optionals or positionals are available, usage is just prog
elif usage is None and not actions:
usage = 'invoke>'
elif usage is None:
prog='invoke>'
# build full usage string
action_usage = self._format_actions_usage(actions, groups) # NEW
usage = ' '.join([s for s in [prog, action_usage] if s])
# omit the long line wrapping code
# prefix with 'usage:'
return '%s%s\n\n' % (prefix, usage)
class PagingArgumentParser(argparse.ArgumentParser):
'''
A custom ArgumentParser that uses pydoc to page its output.
'''
def print_help(self, file=None):
text = self.format_help()
pydoc.pager(text)
class Args(object):
def __init__(self,arg_parser=None,cmd_parser=None):
'''
Initialize new Args class. It takes two optional arguments, an argparse
parser for switches given on the shell command line, and an argparse
parser for switches given on the invoke> CLI line. If one or both are
missing, it creates appropriate parsers internally.
'''
self._arg_parser = arg_parser or self._create_arg_parser()
self._cmd_parser = cmd_parser or self._create_dream_cmd_parser()
self._arg_switches = self.parse_cmd('') # fill in defaults
self._cmd_switches = self.parse_cmd('') # fill in defaults
def parse_args(self):
'''Parse the shell switches and store.'''
try:
self._arg_switches = self._arg_parser.parse_args()
return self._arg_switches
except:
return None
def parse_cmd(self,cmd_string):
'''Parse a invoke>-style command string '''
# handle the case in which the prompt is enclosed by quotes
if cmd_string.startswith('"'):
a = shlex.split(cmd_string)
prompt = a[0]
switches = shlex.join(a[1:])
else:
# no initial quote, so get everything up to the first thing
# that looks like a switch
match = re.match('^(.+?)\s(--?[a-zA-Z].+)',cmd_string)
if match:
prompt,switches = match.groups()
else:
prompt = cmd_string
switches = ''
try:
self._cmd_switches = self._cmd_parser.parse_args(shlex.split(switches))
setattr(self._cmd_switches,'prompt',prompt)
return self._cmd_switches
except:
return None
def json(self,**kwargs):
return json.dumps(self.to_dict(**kwargs))
def to_dict(self,**kwargs):
a = vars(self)
a.update(kwargs)
return a
# Isn't there a more automated way of doing this?
# Ideally we get the switch strings out of the argparse objects,
# but I don't see a documented API for this.
def dream_prompt_str(self,**kwargs):
"""Normalized dream_prompt."""
a = vars(self)
a.update(kwargs)
switches = list()
prompt = a['prompt']
prompt.replace('"','\\"')
switches.append(prompt)
switches.append(f'-s {a["steps"]}')
switches.append(f'-S {a["seed"]}')
switches.append(f'-W {a["width"]}')
switches.append(f'-H {a["height"]}')
switches.append(f'-C {a["cfg_scale"]}')
if a['perlin'] > 0:
switches.append(f'--perlin {a["perlin"]}')
if a['threshold'] > 0:
switches.append(f'--threshold {a["threshold"]}')
if a['grid']:
switches.append('--grid')
if a['seamless']:
switches.append('--seamless')
if a['hires_fix']:
switches.append('--hires_fix')
# img2img generations have parameters relevant only to them and have special handling
if a['init_img'] and len(a['init_img'])>0:
switches.append(f'-I {a["init_img"]}')
switches.append(f'-A {a["sampler_name"]}')
if a['fit']:
switches.append(f'--fit')
if a['init_mask'] and len(a['init_mask'])>0:
switches.append(f'-M {a["init_mask"]}')
if a['init_color'] and len(a['init_color'])>0:
switches.append(f'--init_color {a["init_color"]}')
if a['strength'] and a['strength']>0:
switches.append(f'-f {a["strength"]}')
if a['inpaint_replace']:
switches.append(f'--inpaint_replace')
if a['text_mask']:
switches.append(f'-tm {" ".join([str(u) for u in a["text_mask"]])}')
else:
switches.append(f'-A {a["sampler_name"]}')
# facetool-specific parameters, only print if running facetool
if a['facetool_strength']:
switches.append(f'-G {a["facetool_strength"]}')
switches.append(f'-ft {a["facetool"]}')
if a["facetool"] == "codeformer":
switches.append(f'-cf {a["codeformer_fidelity"]}')
if a['outcrop']:
switches.append(f'-c {" ".join([str(u) for u in a["outcrop"]])}')
# esrgan-specific parameters
if a['upscale']:
switches.append(f'-U {" ".join([str(u) for u in a["upscale"]])}')
# embiggen parameters
if a['embiggen']:
switches.append(f'--embiggen {" ".join([str(u) for u in a["embiggen"]])}')
if a['embiggen_tiles']:
switches.append(f'--embiggen_tiles {" ".join([str(u) for u in a["embiggen_tiles"]])}')
# outpainting parameters
if a['out_direction']:
switches.append(f'-D {" ".join([str(u) for u in a["out_direction"]])}')
# LS: slight semantic drift which needs addressing in the future:
# 1. Variations come out of the stored metadata as a packed string with the keyword "variations"
# 2. However, they come out of the CLI (and probably web) with the keyword "with_variations" and
# in broken-out form. Variation (1) should be changed to comply with (2)
if a['with_variations'] and len(a['with_variations'])>0:
formatted_variations = ','.join(f'{seed}:{weight}' for seed, weight in (a["with_variations"]))
switches.append(f'-V {formatted_variations}')
if 'variations' in a and len(a['variations'])>0:
switches.append(f'-V {a["variations"]}')
return ' '.join(switches)
def __getattribute__(self,name):
'''
Returns union of command-line arguments and dream_prompt arguments,
with the latter superseding the former.
'''
cmd_switches = None
arg_switches = None
try:
cmd_switches = object.__getattribute__(self,'_cmd_switches')
arg_switches = object.__getattribute__(self,'_arg_switches')
except AttributeError:
pass
if cmd_switches and arg_switches and name=='__dict__':
return self._merge_dict(
arg_switches.__dict__,
cmd_switches.__dict__,
)
try:
return object.__getattribute__(self,name)
except AttributeError:
pass
if not hasattr(cmd_switches,name) and not hasattr(arg_switches,name):
raise AttributeError
value_arg,value_cmd = (None,None)
try:
value_cmd = getattr(cmd_switches,name)
except AttributeError:
pass
try:
value_arg = getattr(arg_switches,name)
except AttributeError:
pass
# here is where we can pick and choose which to use
# default behavior is to choose the dream_command value over
# the arg value. For example, the --grid and --individual options are a little
# funny because of their push/pull relationship. This is how to handle it.
if name=='grid':
if cmd_switches.individual:
return False
else:
return value_cmd or value_arg
return value_cmd if value_cmd is not None else value_arg
def __setattr__(self,name,value):
if name.startswith('_'):
object.__setattr__(self,name,value)
else:
self._cmd_switches.__dict__[name] = value
def _merge_dict(self,dict1,dict2):
new_dict = {}
for k in set(list(dict1.keys())+list(dict2.keys())):
value1 = dict1.get(k,None)
value2 = dict2.get(k,None)
new_dict[k] = value2 if value2 is not None else value1
return new_dict
def _create_arg_parser(self):
'''
This defines all the arguments used on the command line when you launch
the CLI or web backend.
'''
parser = argparse.ArgumentParser(
description=
"""
Generate images using Stable Diffusion.
Use --web to launch the web interface.
Use --from_file to load prompts from a file path or standard input ("-").
Otherwise you will be dropped into an interactive command prompt (type -h for help.)
Other command-line arguments are defaults that can usually be overridden
prompt the command prompt.
""",
)
model_group = parser.add_argument_group('Model selection')
file_group = parser.add_argument_group('Input/output')
web_server_group = parser.add_argument_group('Web server')
render_group = parser.add_argument_group('Rendering')
postprocessing_group = parser.add_argument_group('Postprocessing')
deprecated_group = parser.add_argument_group('Deprecated options')
deprecated_group.add_argument('--laion400m')
deprecated_group.add_argument('--weights') # deprecated
model_group.add_argument(
'--config',
'-c',
'-config',
dest='conf',
default='./configs/models.yaml',
help='Path to configuration file for alternate models.',
)
model_group.add_argument(
'--model',
help='Indicates which diffusion model to load (defaults to "default" stanza in configs/models.yaml)',
)
model_group.add_argument(
'--png_compression','-z',
type=int,
default=6,
choices=range(0,9),
dest='png_compression',
help='level of PNG compression, from 0 (none) to 9 (maximum). Default is 6.'
)
model_group.add_argument(
'--sampler',
'-A',
'-m',
dest='sampler_name',
type=str,
choices=SAMPLER_CHOICES,
metavar='SAMPLER_NAME',
help=f'Switch to a different sampler. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
default='k_lms',
)
model_group.add_argument(
'-F',
'--full_precision',
dest='full_precision',
action='store_true',
help='Deprecated way to set --precision=float32',
)
model_group.add_argument(
'--free_gpu_mem',
dest='free_gpu_mem',
action='store_true',
help='Force free gpu memory before final decoding',
)
model_group.add_argument(
'--precision',
dest='precision',
type=str,
choices=PRECISION_CHOICES,
metavar='PRECISION',
help=f'Set model precision. Defaults to auto selected based on device. Options: {", ".join(PRECISION_CHOICES)}',
default='auto',
)
model_group.add_argument(
'--safety_checker',
action='store_true',
help='Check for and blur potentially NSFW images',
)
file_group.add_argument(
'--from_file',
dest='infile',
type=str,
help='If specified, load prompts from this file',
)
file_group.add_argument(
'--outdir',
'-o',
type=str,
help='Directory to save generated images and a log of prompts and seeds. Default: outputs/img-samples',
default='outputs/img-samples',
)
file_group.add_argument(
'--prompt_as_dir',
'-p',
action='store_true',
help='Place images in subdirectories named after the prompt.',
)
render_group.add_argument(
'--fnformat',
default='{prefix}.{seed}.png',
type=str,
help='Overwrite the filename format. You can use any argument as wildcard enclosed in curly braces. Default is {prefix}.{seed}.png',
)
render_group.add_argument(
'--grid',
'-g',
action='store_true',
help='generate a grid'
)
render_group.add_argument(
'--embedding_path',
type=str,
help='Path to a pre-trained embedding manager checkpoint - can only be set on command line',
)
# Restoration related args
postprocessing_group.add_argument(
'--no_restore',
dest='restore',
action='store_false',
help='Disable face restoration with GFPGAN or codeformer',
)
postprocessing_group.add_argument(
'--no_upscale',
dest='esrgan',
action='store_false',
help='Disable upscaling with ESRGAN',
)
postprocessing_group.add_argument(
'--esrgan_bg_tile',
type=int,
default=400,
help='Tile size for background sampler, 0 for no tile during testing. Default: 400.',
)
postprocessing_group.add_argument(
'--gfpgan_model_path',
type=str,
default='experiments/pretrained_models/GFPGANv1.4.pth',
help='Indicates the path to the GFPGAN model, relative to --gfpgan_dir.',
)
postprocessing_group.add_argument(
'--gfpgan_dir',
type=str,
default='./src/gfpgan',
help='Indicates the directory containing the GFPGAN code.',
)
web_server_group.add_argument(
'--web',
dest='web',
action='store_true',
help='Start in web server mode.',
)
web_server_group.add_argument(
'--web_develop',
dest='web_develop',
action='store_true',
help='Start in web server development mode.',
)
web_server_group.add_argument(
"--web_verbose",
action="store_true",
help="Enables verbose logging",
)
web_server_group.add_argument(
"--cors",
nargs="*",
type=str,
help="Additional allowed origins, comma-separated",
)
web_server_group.add_argument(
'--host',
type=str,
default='127.0.0.1',
help='Web server: Host or IP to listen on. Set to 0.0.0.0 to accept traffic from other devices on your network.'
)
web_server_group.add_argument(
'--port',
type=int,
default='9090',
help='Web server: Port to listen on'
)
web_server_group.add_argument(
'--gui',
dest='gui',
action='store_true',
help='Start InvokeAI GUI',
)
return parser
# This creates the parser that processes commands on the invoke> command line
def _create_dream_cmd_parser(self):
parser = PagingArgumentParser(
formatter_class=ArgFormatter,
description=
"""
*Image generation*
invoke> a fantastic alien landscape -W576 -H512 -s60 -n4
*postprocessing*
!fix applies upscaling/facefixing to a previously-generated image.
invoke> !fix 0000045.4829112.png -G1 -U4 -ft codeformer
*History manipulation*
!fetch retrieves the command used to generate an earlier image.
invoke> !fetch 0000015.8929913.png
invoke> a fantastic alien landscape -W 576 -H 512 -s 60 -A plms -C 7.5
!history lists all the commands issued during the current session.
!NN retrieves the NNth command from the history
*Model manipulation*
!models -- list models in configs/models.yaml
!switch <model_name> -- switch to model named <model_name>
!import_model path/to/weights/file.ckpt -- adds a model to your config
!edit_model <model_name> -- edit a model's description
!del_model <model_name> -- delete a model
"""
)
render_group = parser.add_argument_group('General rendering')
img2img_group = parser.add_argument_group('Image-to-image and inpainting')
variation_group = parser.add_argument_group('Creating and combining variations')
postprocessing_group = parser.add_argument_group('Post-processing')
special_effects_group = parser.add_argument_group('Special effects')
render_group.add_argument(
'--prompt',
default='',
help='prompt string',
)
render_group.add_argument(
'-s',
'--steps',
type=int,
default=50,
help='Number of steps'
)
render_group.add_argument(
'-S',
'--seed',
type=int,
default=None,
help='Image seed; a +ve integer, or use -1 for the previous seed, -2 for the one before that, etc',
)
render_group.add_argument(
'-n',
'--iterations',
type=int,
default=1,
help='Number of samplings to perform (slower, but will provide seeds for individual images)',
)
render_group.add_argument(
'-W',
'--width',
type=int,
help='Image width, multiple of 64',
)
render_group.add_argument(
'-H',
'--height',
type=int,
help='Image height, multiple of 64',
)
render_group.add_argument(
'-C',
'--cfg_scale',
default=7.5,
type=float,
help='Classifier free guidance (CFG) scale - higher numbers cause generator to "try" harder.',
)
render_group.add_argument(
'--threshold',
default=0.0,
type=float,
help='Latent threshold for classifier free guidance (CFG) - prevent generator from "trying" too hard. Use positive values, 0 disables.',
)
render_group.add_argument(
'--perlin',
default=0.0,
type=float,
help='Perlin noise scale (0.0 - 1.0) - add perlin noise to the initialization instead of the usual gaussian noise.',
)
render_group.add_argument(
'--fnformat',
default='{prefix}.{seed}.png',
type=str,
help='Overwrite the filename format. You can use any argument as wildcard enclosed in curly braces. Default is {prefix}.{seed}.png',
)
render_group.add_argument(
'--grid',
'-g',
action='store_true',
help='generate a grid'
)
render_group.add_argument(
'-i',
'--individual',
action='store_true',
help='override command-line --grid setting and generate individual images'
)
render_group.add_argument(
'-x',
'--skip_normalize',
action='store_true',
help='Skip subprompt weight normalization',
)
render_group.add_argument(
'-A',
'-m',
'--sampler',
dest='sampler_name',
type=str,
choices=SAMPLER_CHOICES,
metavar='SAMPLER_NAME',
help=f'Switch to a different sampler. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
)
render_group.add_argument(
'-t',
'--log_tokenization',
action='store_true',
help='shows how the prompt is split into tokens'
)
render_group.add_argument(
'--outdir',
'-o',
type=str,
help='Directory to save generated images and a log of prompts and seeds',
)
render_group.add_argument(
'--hires_fix',
action='store_true',
dest='hires_fix',
help='Create hires image using img2img to prevent duplicated objects'
)
render_group.add_argument(
'--save_intermediates',
type=int,
default=0,
dest='save_intermediates',
help='Save every nth intermediate image into an "intermediates" directory within the output directory'
)
render_group.add_argument(
'--png_compression','-z',
type=int,
default=6,
choices=range(0,10),
dest='png_compression',
help='level of PNG compression, from 0 (none) to 9 (maximum). Default is 6.'
)
img2img_group.add_argument(
'-I',
'--init_img',
type=str,
help='Path to input image for img2img mode (supersedes width and height)',
)
img2img_group.add_argument(
'-M',
'--init_mask',
type=str,
help='Path to input mask for inpainting mode (supersedes width and height)',
)
img2img_group.add_argument(
'--invert_mask',
action='store_true',
help='Invert the mask',
)
img2img_group.add_argument(
'-tm',
'--text_mask',
nargs='+',
type=str,
help='Use the clipseg classifier to generate the mask area for inpainting. Provide a description of the area to mask ("a mug"), optionally followed by the confidence level threshold (0-1.0; defaults to 0.5).',
default=None,
)
img2img_group.add_argument(
'--init_color',
type=str,
help='Path to reference image for color correction (used for repeated img2img and inpainting)'
)
img2img_group.add_argument(
'-T',
'-fit',
'--fit',
action='store_true',
help='If specified, will resize the input image to fit within the dimensions of width x height (512x512 default)',
)
img2img_group.add_argument(
'-f',
'--strength',
type=float,
help='Strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely',
default=0.75,
)
img2img_group.add_argument(
'-D',
'--out_direction',
nargs='+',
type=str,
metavar=('direction', 'pixels'),
help='Direction to extend the given image (left|right|top|bottom). If a distance pixel value is not specified it defaults to half the image size'
)
img2img_group.add_argument(
'-c',
'--outcrop',
nargs='+',
type=str,
metavar=('direction','pixels'),
help='Outcrop the image with one or more direction/pixel pairs: -c top 64 bottom 128 left 64 right 64',
)
img2img_group.add_argument(
'-r',
'--inpaint_replace',
type=float,
default=0.0,
help='when inpainting, adjust how aggressively to replace the part of the picture under the mask, from 0.0 (a gentle merge) to 1.0 (replace entirely)',
)
postprocessing_group.add_argument(
'-ft',
'--facetool',
type=str,
default='gfpgan',
help='Select the face restoration AI to use: gfpgan, codeformer',
)
postprocessing_group.add_argument(
'-G',
'--facetool_strength',
'--gfpgan_strength',
type=float,
help='The strength at which to apply the face restoration to the result.',
default=0.0,
)
postprocessing_group.add_argument(
'-cf',
'--codeformer_fidelity',
type=float,
help='Used along with CodeFormer. Takes values between 0 and 1. 0 produces high quality but low accuracy. 1 produces high accuracy but low quality.',
default=0.75
)
postprocessing_group.add_argument(
'-U',
'--upscale',
nargs='+',
type=float,
help='Scale factor (1, 2, 3, 4, etc..) for upscaling final output followed by upscaling strength (0-1.0). If strength not specified, defaults to 0.75',
default=None,
)
postprocessing_group.add_argument(
'--save_original',
'-save_orig',
action='store_true',
help='Save original. Use it when upscaling to save both versions.',
)
postprocessing_group.add_argument(
'--embiggen',
'-embiggen',
nargs='+',
type=float,
help='Arbitrary upscaling using img2img. Provide scale factor (0.75), optionally followed by strength (0.75) and tile overlap proportion (0.25).',
default=None,
)
postprocessing_group.add_argument(
'--embiggen_tiles',
'-embiggen_tiles',
nargs='+',
type=int,
help='For embiggen, provide list of tiles to process and replace onto the image e.g. `1 3 5`.',
default=None,
)
special_effects_group.add_argument(
'--seamless',
action='store_true',
help='Change the model to seamless tiling (circular) mode',
)
special_effects_group.add_argument(
'--seamless_axes',
default=['x', 'y'],
type=list[str],
help='Specify which axes to use circular convolution on.',
)
variation_group.add_argument(
'-v',
'--variation_amount',
default=0.0,
type=float,
help='If > 0, generates variations on the initial seed instead of random seeds per iteration. Must be between 0 and 1. Higher values will be more different.'
)
variation_group.add_argument(
'-V',
'--with_variations',
default=None,
type=str,
help='list of variations to apply, in the format `seed:weight,seed:weight,...'
)
render_group.add_argument(
'--use_mps_noise',
action='store_true',
dest='use_mps_noise',
help='Simulate noise on M1 systems to get the same results'
)
return parser
def format_metadata(**kwargs):
print(f'format_metadata() is deprecated. Please use metadata_dumps()')
return metadata_dumps(kwargs)
def metadata_dumps(opt,
seeds=[],
model_hash=None,
postprocessing=None):
'''
Given an Args object, returns a dict containing the keys and
structure of the proposed stable diffusion metadata standard
https://github.com/lstein/stable-diffusion/discussions/392
This is intended to be turned into JSON and stored in the
"sd
'''
# top-level metadata minus `image` or `images`
metadata = {
'model' : 'stable diffusion',
'model_id' : opt.model,
'model_hash' : model_hash,
'app_id' : APP_ID,
'app_version' : APP_VERSION,
}
# # add some RFC266 fields that are generated internally, and not as
# # user args
image_dict = opt.to_dict(
postprocessing=postprocessing
)
# remove any image keys not mentioned in RFC #266
rfc266_img_fields = ['type','postprocessing','sampler','prompt','seed','variations','steps',
'cfg_scale','threshold','perlin','fnformat', 'step_number','width','height','extra','strength',
'init_img','init_mask','facetool','facetool_strength','upscale']
rfc_dict ={}
for item in image_dict.items():
key,value = item
if key in rfc266_img_fields:
rfc_dict[key] = value
# semantic drift
rfc_dict['sampler'] = image_dict.get('sampler_name',None)
# display weighted subprompts (liable to change)
if opt.prompt:
subprompts = split_weighted_subprompts(opt.prompt)
subprompts = [{'prompt':x[0],'weight':x[1]} for x in subprompts]
rfc_dict['prompt'] = subprompts
# 'variations' should always exist and be an array, empty or consisting of {'seed': seed, 'weight': weight} pairs
rfc_dict['variations'] = [{'seed':x[0],'weight':x[1]} for x in opt.with_variations] if opt.with_variations else []
# if variations are present then we need to replace 'seed' with 'orig_seed'
if hasattr(opt,'first_seed'):
rfc_dict['seed'] = opt.first_seed
if opt.init_img:
rfc_dict['type'] = 'img2img'
rfc_dict['strength_steps'] = rfc_dict.pop('strength')
rfc_dict['orig_hash'] = calculate_init_img_hash(opt.init_img)
rfc_dict['inpaint_replace'] = opt.inpaint_replace
else:
rfc_dict['type'] = 'txt2img'
rfc_dict.pop('strength')
if len(seeds)==0 and opt.seed:
seeds=[seed]
if opt.grid:
images = []
for seed in seeds:
rfc_dict['seed'] = seed
images.append(copy.copy(rfc_dict))
metadata['images'] = images
else:
# there should only ever be a single seed if we did not generate a grid
assert len(seeds) == 1, 'Expected a single seed'
rfc_dict['seed'] = seeds[0]
metadata['image'] = rfc_dict
return metadata
@functools.lru_cache(maxsize=50)
def metadata_from_png(png_file_path) -> Args:
'''
Given the path to a PNG file created by dream.py, retrieves
an Args object containing the image metadata. Note that this
returns a single Args object, not multiple.
'''
meta = ldm.invoke.pngwriter.retrieve_metadata(png_file_path)
if 'sd-metadata' in meta and len(meta['sd-metadata'])>0 :
return metadata_loads(meta)[0]
else:
return legacy_metadata_load(meta,png_file_path)
def dream_cmd_from_png(png_file_path):
opt = metadata_from_png(png_file_path)
return opt.dream_prompt_str()
def metadata_loads(metadata) -> list:
'''
Takes the dictionary corresponding to RFC266 (https://github.com/lstein/stable-diffusion/issues/266)
and returns a series of opt objects for each of the images described in the dictionary. Note that this
returns a list, and not a single object. See metadata_from_png() for a more convenient function for
files that contain a single image.
'''
results = []
try:
if 'grid' in metadata['sd-metadata']:
images = metadata['sd-metadata']['images']
else:
images = [metadata['sd-metadata']['image']]
for image in images:
# repack the prompt and variations
if 'prompt' in image:
image['prompt'] = repack_prompt(image['prompt'])
if 'variations' in image:
image['variations'] = ','.join([':'.join([str(x['seed']),str(x['weight'])]) for x in image['variations']])
# fix a bit of semantic drift here
image['sampler_name']=image.pop('sampler')
opt = Args()
opt._cmd_switches = Namespace(**image)
results.append(opt)
except Exception as e:
import sys, traceback
print('>> could not read metadata',file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
return results
def repack_prompt(prompt_list:list)->str:
# in the common case of no weighting syntax, just return the prompt as is
if len(prompt_list) > 1:
return ','.join([':'.join([x['prompt'], str(x['weight'])]) for x in prompt_list])
else:
return prompt_list[0]['prompt']
# image can either be a file path on disk or a base64-encoded
# representation of the file's contents
def calculate_init_img_hash(image_string):
prefix = 'data:image/png;base64,'
hash = None
if image_string.startswith(prefix):
imagebase64 = image_string[len(prefix):]
imagedata = base64.b64decode(imagebase64)
with open('outputs/test.png','wb') as file:
file.write(imagedata)
sha = hashlib.sha256()
sha.update(imagedata)
hash = sha.hexdigest()
else:
hash = sha256(image_string)
return hash
# Bah. This should be moved somewhere else...
def sha256(path):
sha = hashlib.sha256()
with open(path,'rb') as f:
while True:
data = f.read(65536)
if not data:
break
sha.update(data)
return sha.hexdigest()
def legacy_metadata_load(meta,pathname) -> Args:
opt = Args()
if 'Dream' in meta and len(meta['Dream']) > 0:
dream_prompt = meta['Dream']
opt.parse_cmd(dream_prompt)
else: # if nothing else, we can get the seed
match = re.search('\d+\.(\d+)',pathname)
if match:
seed = match.groups()[0]
opt.seed = seed
else:
opt.prompt = ''
opt.seed = 0
return opt
Reference in New Issue View Git Blame Copy Permalink