mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
c94b8cd959
- fixes no closing quote in pretty-printed dream_prompt string - removes unecessary -f switch when txt2img used In addition, this commit does an experimental commenting-out of the random.seed() call in the variation-generating part of ldm.dream.generator.base. This fixes the problem of two calls that use the same seed and -v0.1 generating different images (#641). However, it does not fix the issue of two images generated using the same seed and -VXXXXXX being different.
642 lines
23 KiB
Python
642 lines
23 KiB
Python
"""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.
|
|
|
|
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.
|
|
|
|
We also export the function build_metadata
|
|
"""
|
|
|
|
import argparse
|
|
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'-W {a["width"]}')
|
|
switches.append(f'-H {a["height"]}')
|
|
switches.append(f'-C {a["cfg_scale"]}')
|
|
switches.append(f'-A {a["sampler_name"]}')
|
|
switches.append(f'-S {a["seed"]}')
|
|
if a['grid']:
|
|
switches.append('--grid')
|
|
if a['seamless']:
|
|
switches.append('--seamless')
|
|
if a['init_img'] and len(a['init_img'])>0:
|
|
switches.append(f'-I {a["init_img"]}')
|
|
if a['fit']:
|
|
switches.append(f'--fit')
|
|
if a['init_img'] and a['strength'] and a['strength']>0:
|
|
switches.append(f'-f {a["strength"]}')
|
|
if a['gfpgan_strength']:
|
|
switches.append(f'-G {a["gfpgan_strength"]}')
|
|
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['variation_amount'] > 0:
|
|
switches.append(f'-v {a["variation_amount"]}')
|
|
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(
|
|
'-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(
|
|
'-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(
|
|
'-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
|
|
|
|
# very partial implementation of https://github.com/lstein/stable-diffusion/issues/266
|
|
# it does not write all the required top-level metadata, writes too much image
|
|
# data, and doesn't support grids yet. But you gotta start somewhere, no?
|
|
def format_metadata(opt,
|
|
seeds=[],
|
|
weights=None,
|
|
model_hash=None,
|
|
postprocessing=None):
|
|
'''
|
|
Given an Args object, returns a partial implementation of
|
|
the stable diffusion metadata standard
|
|
'''
|
|
# add some RFC266 fields that are generated internally, and not as
|
|
# user args
|
|
image_dict = opt.to_dict(
|
|
postprocessing=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)')
|
|
|
|
# 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
|
|
if opt.with_variations:
|
|
variations = [{'seed':x[0],'weight':x[1]} for x in opt.with_variations]
|
|
rfc_dict['variations'] = variations
|
|
|
|
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' # FIX ME WHEN IMG2IMG SUPPORTS ALL SAMPLERS
|
|
else:
|
|
rfc_dict['type'] = 'txt2img'
|
|
|
|
images = []
|
|
for seed in seeds:
|
|
rfc_dict['seed'] = seed
|
|
images.append(copy.copy(rfc_dict))
|
|
|
|
return {
|
|
'model' : 'stable diffusion',
|
|
'model_id' : opt.model,
|
|
'model_hash' : model_hash,
|
|
'app_id' : APP_ID,
|
|
'app_version' : APP_VERSION,
|
|
'images' : images,
|
|
}
|
|
|
|
# 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()
|
|
|