InvokeAI/ldm/dream/args.py

"""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 dream> 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 dream> 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 dream.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.


"""

import argparse
from argparse import Namespace
import shlex
import json
import hashlib
import os
import copy
import base64
from ldm.dream.conditioning import split_weighted_subprompts

SAMPLER_CHOICES = [
    'ddim',
    'k_dpm_2_a',
    'k_dpm_2',
    'k_euler_a',
    'k_euler',
    'k_heun',
    'k_lms',
    'plms',
]

# is there a way to pick this up during git commits?
APP_ID      = 'lstein/stable-diffusion'
APP_VERSION = 'v1.15'

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 dream> 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 dream>-style command string '''
        command = cmd_string.replace("'", "\\'")
        try:
            elements = shlex.split(command)
        except ValueError:
            import sys, traceback
            print(traceback.format_exc(), file=sys.stderr)
            return
        switches = ['']
        switches_started = False

        for element in elements:
            if element[0] == '-' and not switches_started:
                switches_started = True
            if switches_started:
                switches.append(element)
            else:
                switches[0] += element
                switches[0] += ' '
        switches[0] = switches[0][: len(switches[0]) - 1]
        try:
            self._cmd_switches = self._cmd_parser.parse_args(switches)
            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()
        switches.append(f'"{a["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['grid']:
            switches.append('--grid')
        if a['seamless']:
            switches.append('--seamless')

        # 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 ddim') # TODO: FIX ME WHEN IMG2IMG SUPPORTS ALL SAMPLERS
            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"]}')
        else:
            switches.append(f'-A {a["sampler_name"]}')

        # gfpgan-specific parameters
        if a['gfpgan_strength']:
            switches.append(f'-G {a["gfpgan_strength"]}')

        # esrgan-specific parameters
        if a['upscale']:
            switches.append(f'-U {" ".join([str(u) for u in a["upscale"]])}')
        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"]])}')
        if a['with_variations']:
            formatted_variations = ','.join(f'{seed}:{weight}' for seed, weight in (a["with_variations"]))
            switches.append(f'-V {formatted_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':
            return not cmd_switches.individual and value_arg  # arg supersedes cmd
        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(
            '--conf',
            '-c',
            '-conf',
            dest='conf',
            default='./configs/models.yaml',
            help='Path to configuration file for alternate models.',
        )
        model_group.add_argument(
            '--model',
            default='stable-diffusion-1.4',
            help='Indicates which diffusion model to load. (currently "stable-diffusion-1.4" (default) or "laion400m")',
        )
        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='Use more memory-intensive full precision math for calculations',
        )
        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(
            '--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',
        )
        # GFPGAN related args
        postprocessing_group.add_argument(
            '--gfpgan_bg_upsampler',
            type=str,
            default='realesrgan',
            help='Background upsampler. Default: realesrgan. Options: realesrgan, none.',

        )
        postprocessing_group.add_argument(
            '--gfpgan_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/GFPGANv1.3.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(
            '--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'
        )
        return parser

    # This creates the parser that processes commands on the dream> command line
    def _create_dream_cmd_parser(self):
        parser = argparse.ArgumentParser(
            description='Example: dream> a fantastic alien landscape -W1024 -H960 -s100 -n12'
        )
        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')
        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(
            '--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',
        )
        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(
            '--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,
        )
        postprocessing_group.add_argument(
            '-ft',
            '--facetool',
            type=str,
            help='Select the face restoration AI to use: gfpgan, codeformer',
        )
        postprocessing_group.add_argument(
            '-G',
            '--gfpgan_strength',
            type=float,
            help='The strength at which to apply the GFPGAN model to the result, in order to improve faces.',
            default=0,
        )
        postprocessing_group.add_argument(
            '-cf',
            '--codeformer_fidelity',
            type=float,
            help='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 (2, 4) 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='Embiggen tiled img2img for higher resolution and detail without extra VRAM usage. Takes scale factor relative to the size of the --init_img (-I), followed by ESRGAN upscaling strength (0-1.0), followed by minimum amount of overlap between tiles as a decimal ratio (0 - 1.0) or number of pixels. ESRGAN strength defaults to 0.75, and overlap defaults to 0.25 . ESRGAN is used to upscale the init prior to cutting it into tiles/pieces to run through img2img and then stitch back togeather.',
            default=None,
        )
        postprocessing_group.add_argument(
            '--embiggen_tiles',
            '-embiggen_tiles',
            nargs='+',
            type=int,
            help='If while doing Embiggen we are altering only parts of the image, takes a list of tiles by number to process and replace onto the image e.g. `1 3 5`, useful for redoing problematic spots from a prior Embiggen run',
            default=None,
        )
        special_effects_group.add_argument(
            '--seamless',
            action='store_true',
            help='Change the model to seamless tiling (circular) mode',
        )
        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,...'
        )
        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
    )

    # 'postprocessing' is either null or an array of postprocessing metadatal
    if postprocessing:
        # TODO: This is just a hack until postprocessing pipeline work completed
        image_dict['postprocessing'] = []

        if image_dict['gfpgan_strength'] and image_dict['gfpgan_strength'] > 0:
            image_dict['postprocessing'].append('GFPGAN (not RFC compliant)')
        if image_dict['upscale'] and image_dict['upscale'][0] > 0:
            image_dict['postprocessing'].append('ESRGAN (not RFC compliant)')
    else:
        image_dict['postprocessing'] = None

    # remove any image keys not mentioned in RFC #266
    rfc266_img_fields = ['type','postprocessing','sampler','prompt','seed','variations','steps',
                         'cfg_scale','step_number','width','height','extra','strength']

    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 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['sampler']        = 'ddim'  # TODO: FIX ME WHEN IMG2IMG SUPPORTS ALL SAMPLERS
    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

def metadata_loads(metadata):
    '''
    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.
    '''
    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
            image['prompt']     = ','.join([':'.join([x['prompt'],   str(x['weight'])]) for x in image['prompt']])
            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 KeyError as e:
        import sys, traceback
        print('>> badly-formatted metadata',file=sys.stderr)
        print(traceback.format_exc(), file=sys.stderr)
    return results

# 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()