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add sdp cross-optimization

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Vladimir Mandic
2023-03-06 16:52:48 -05:00
parent 5ec19418f1
commit 61aa82dcd0
7 changed files with 153 additions and 46 deletions
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cli/modules/process.py
+66 -33
View File
@@ -53,25 +53,29 @@ params = Map({
'blur_samplesize': 60, # sample size to use for blur detection
'similarity_size': 64, # base similarity detection on reduced images
# original image processing settings
'keep_original': True, # keep original image
'keep_original': False, # keep original image
# face processing settings
'extract_face': False, # extract face from image
'face_score': 0.7, # min face detection score
'face_pad': 0.2, # pad face image percentage
'face_pad': 0.1, # pad face image percentage
'face_model': 1, # which face model to use 0/close-up 1/standard
'face_blur': False, # check for body blur
'face_blur_score': 1.5, # max score for face blur detection
'face_range': False, # check for body blur
'face_range_score': 0.15, # min score for face dynamic range detection
'face_restore': True, # attempt to restore face quality
'face_upscale': True, # attempt to scale small faces
'face_restore': False, # attempt to restore face quality
'face_upscale': False, # attempt to scale small faces
'face_segmentation': False, # segmentation enabled
# body processing settings
'extract_body': False, # extract face from image
'extract_body': False, # extract body from image
'body_score': 0.9, # min body detection score
'body_visibility': 0.5, # min visibility score for each detected body part
'body_parts': 15, # min number of detected body parts with sufficient visibility
'body_pad': 0.2, # pad body image percentage
'body_model': 2, # body model to use 0/low 1/medium 2/high
'body_blur': False, # check for body blur
'body_blur_score': 1.8, # max score for body blur detection
'face_range': False, # check for body blur
'body_range_score': 0.15, # min score for body dynamic range detection
'body_segmentation': False, # segmentation enabled
# similarity detection settings
@@ -212,19 +216,21 @@ def extract_face(img):
else:
squared = cropped
blur = detect_blur(squared)
if blur > params.face_blur_score:
log.info({ 'process face skip': 'blur check fail', 'blur': blur })
return None, True
else:
log.debug({ 'process face blur': blur })
if params.face_blur:
blur = detect_blur(squared)
if blur > params.face_blur_score:
log.info({ 'process face skip': 'blur check fail', 'blur': blur })
return None, True
else:
log.debug({ 'process face blur': blur })
range = detect_dynamicrange(squared)
if range < params.face_range_score:
log.info({ 'process face skip': 'dynamic range check fail', 'range': range })
return None, True
else:
log.debug({ 'process face dynamic range': range })
if params.face_range:
range = detect_dynamicrange(squared)
if range < params.face_range_score:
log.info({ 'process face skip': 'dynamic range check fail', 'range': range })
return None, True
else:
log.debug({ 'process face dynamic range': range })
similarity = detect_simmilar(squared)
if similarity > params.similarity_score:
@@ -275,19 +281,21 @@ def extract_body(img):
else:
squared = cropped
blur = detect_blur(squared)
if blur > params.body_blur_score:
log.info({ 'process body skip': 'blur check fail', 'blur': blur })
return None, True
else:
log.debug({ 'process body blur': blur })
if params.body_blur:
blur = detect_blur(squared)
if blur > params.body_blur_score:
log.info({ 'process body skip': 'blur check fail', 'blur': blur })
return None, True
else:
log.debug({ 'process body blur': blur })
range = detect_dynamicrange(squared)
if range < params.body_range_score:
log.info({ 'process body skip': 'dynamic range check fail', 'range': range })
return None, True
else:
log.debug({ 'process body dynamic range': range })
if params.body_range:
range = detect_dynamicrange(squared)
if range < params.body_range_score:
log.info({ 'process body skip': 'dynamic range check fail', 'range': range })
return None, True
else:
log.debug({ 'process body dynamic range': range })
similarity = detect_simmilar(squared)
if similarity > params.similarity_score:
@@ -318,7 +326,7 @@ def encode(img):
return encoded
def interrogate(img, fn):
def interrogate(img, fn, intag = None):
if len(params.interrogate_model) == 0:
return
caption = ''
@@ -329,16 +337,22 @@ def interrogate(img, fn):
if model == 'clip':
caption = res.caption if 'caption' in res else ''
caption = caption.split(',')[0].replace('a ', '')
if intag is not None:
caption = intag + ', ' + caption
if model == 'deepdanbooru':
tag = res.caption if 'caption' in res else ''
tags = tag.split(',')
tags = [t.replace('(', '').replace(')', '').split(':')[0].strip() for t in tags]
if intag is not None:
for t in intag.split(',')[::-1]:
tags.insert(0, t.strip())
if params.interrogate_captions:
file = fn.replace(params.format, '.txt')
f = open(file, 'w')
f.write(caption)
f.close()
tags.insert(0, caption.split(' ')[0])
pos = 0 if len(tags) == 0 else 1
tags.insert(pos, caption.split(' ')[1])
if len(tags) > params.tag_limit:
tags = tags[:params.tag_limit]
log.info({ 'interrogate': caption, 'tags': tags })
@@ -348,7 +362,8 @@ def interrogate(img, fn):
i = {}
metadata = Map({})
def process_file(f: str, dst: str = None, preview: bool = False, offline: bool = False, txt = None):
# entry point when used as module
def process_file(f: str, dst: str = None, preview: bool = False, offline: bool = False, txt = None, tag = None, opts = []):
def save(img, f, what):
i[what] = i.get(what, 0) + 1
if dst is None:
@@ -365,10 +380,28 @@ def process_file(f: str, dst: str = None, preview: bool = False, offline: bool =
if not preview:
img.save(os.path.join(dir, fn))
if not offline:
caption, tags = interrogate(img, os.path.join(dir, fn))
caption, tags = interrogate(img, os.path.join(dir, fn), tag)
metadata[os.path.join(parent, basename)] = { 'caption': caption, 'tags': ','.join(tags) }
return fn
# overrides
if 'original' in opts:
params.keep_original = True
if 'face' in opts:
params.extract_face = True
if 'body' in opts:
params.extract_body = True
if 'blur' in opts:
params.face_blur = True
params.body_blur = True
if 'range' in opts:
params.face_range = True
params.body_range = True
if 'upscale' in opts:
params.face_upscale = True
if 'restore' in opts:
params.face_restore = True
log.info({ 'processing': f })
try:
image = Image.open(f)
cli/modules/sdapi.py
+14 -1
View File
@@ -4,11 +4,12 @@ helper methods that creates HTTP session with managed connection pool
provides async HTTP get/post methods and several helper methods
"""
import sys
import json
import aiohttp
import asyncio
import logging
import requests
import sys
from util import Map, log
@@ -128,6 +129,12 @@ async def progress():
return res
def options():
options = getsync('/sdapi/v1/options')
flags = getsync('/sdapi/v1/cmd-flags')
return { 'options': options, 'flags': flags }
def shutdown():
try:
postsync('/sdapi/v1/shutdown')
@@ -168,6 +175,12 @@ if __name__ == "__main__":
asyncio.run(interrupt())
if 'progress' in sys.argv:
asyncio.run(progress())
if 'options' in sys.argv:
opt = options()
log.debug({ 'options' })
print(json.dumps(opt['options'], indent = 2))
log.debug({ 'cmd-flags' })
print(json.dumps(opt['flags'], indent = 2))
if 'shutdown' in sys.argv:
shutdown()
asyncio.run(close())
cli/train-lora.py
+25 -12
View File
@@ -14,6 +14,7 @@ Disabled/broken:
"""
import os
import re
import gc
import sys
import json
@@ -127,12 +128,13 @@ def mem_stats():
if __name__ == '__main__':
parser = argparse.ArgumentParser(description = 'train lora')
parser.add_argument('--model', type=str, default=None, required=True, help='original model to use a base for training')
parser.add_argument('--input', type=str, default=None, required=True, help='input folder with training images')
parser.add_argument('--output', type=str, default=None, required=True, help='lora name')
parser.add_argument('--model', type=str, default=None, required=False, help='original model to use a base for training, default: active model')
parser.add_argument('--input', '--dataset', type=str, default=None, required=True, help='input folder with training images')
parser.add_argument('--output', '--lora', type=str, default=None, required=True, help='lora name')
parser.add_argument('--tag', type=str, default=None, required=False, help='primary tag')
parser.add_argument('--dir', type=str, default=None, required=True, help='folder containing lora checkpoints')
parser.add_argument('--dir', type=str, default=None, required=False, help='folder containing lora checkpoints')
parser.add_argument('--interim', type=int, default=0, help = 'save interim checkpoints after n epoch')
parser.add_argument('--process', type=str, default='original', required=True, help='list of processing steps: original,face,body,blur,range,upscale,restore')
parser.add_argument('--noprocess', default = False, action='store_true', help = 'skip processing and use existing input data')
parser.add_argument('--notrain', default = False, action='store_true', help = 'just run processing and skip training')
parser.add_argument('--nocaptions', default = False, action='store_true', help = 'skip creating captions and tags')
@@ -152,19 +154,30 @@ if __name__ == '__main__':
parser.add_argument('--locon', default=False, action='store_true', help = "use locon style training")
parser.add_argument('--debug', default=False, action='store_true', help = "enable debug logging")
args = parser.parse_args()
defaults = Map({ 'options': {}, 'flags': {} }) if args.offline else Map(modules.sdapi.options())
if args.debug:
log.setLevel(logging.DEBUG)
log.debug({ 'debug': True })
if args.model is None:
args.model = defaults.options.get('sd_model_checkpoint', None)
args.model = args.model.split(' [')[0] if args.model is not None else None
if args.dir is None:
args.dir = defaults.flags.get('lora_dir', None)
if not os.path.isabs(args.model) and args.dir is not None and not os.path.exists(args.model):
args.model = os.path.abspath(os.path.join(args.dir, os.pardir, 'Stable-diffusion', args.model))
if args.dir is None:
args.dir = os.path.join(args.input, 'lora')
if not os.path.exists(args.model) or not os.path.isfile(args.model):
log.error({ 'lora cannot find model': args.model })
exit(1)
options.pretrained_model_name_or_path = args.model
if not os.path.exists(args.input) or not os.path.isdir(args.input):
log.error({ 'lora cannot find training dir': args.input })
exit(1)
if not os.path.exists(args.dir) or not os.path.isdir(args.dir):
log.error({ 'lora cannot find training dir': args.dir })
exit(1)
options.pretrained_model_name_or_path = args.model
options.output_dir = args.dir
options.output_name = args.output
options.max_train_steps = args.steps
@@ -200,9 +213,15 @@ if __name__ == '__main__':
if not args.noprocess:
# preprocess
processing_options = args.process.split(',')
processing_options = [opt.strip() for opt in re.split(',| ', args.process)]
log.info({ 'processing options': processing_options })
if os.path.exists(json_file):
os.remove(json_file)
for f in files:
try:
res, metadata = modules.process.process_file(f = f, dst = dir, preview = False, offline = args.offline, txt = args.dreambooth)
res, metadata = modules.process.process_file(f = f, dst = dir, preview = False, offline = args.offline, txt = args.dreambooth, tag = args.tag, opts = processing_options)
if not args.dreambooth:
with open(json_file, "w") as outfile:
outfile.write(json.dumps(metadata, indent=2))
@@ -210,12 +229,6 @@ if __name__ == '__main__':
exit(1)
modules.process.unload_models()
mem_stats()
if args.tag is not None:
for name, item in metadata.items():
item['caption'] = args.tag + ',' + item['caption']
item['tags'] = args.tag + ',' + item['tags']
with open(json_file, "w") as outfile:
outfile.write(json.dumps(metadata, indent=2))
if not args.nolatents and not args.dreambooth:
# create latents