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UPDATE: Console Log Patch + FIXes

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- Insightface "lots of messages" Console Log Patch (thnx @ssitu for the implementation in the repo ComfyUI_roop)
- VersionUP (beta passed)
- Filename changed: roop_logging, roop_version
- Checkboxes placeholder no need
- Diffusers - no need in the requirements
This commit is contained in:
Gourieff 2023-07-31 00:02:40 +07:00
parent df8aea5f1d
commit bfeae4b4a0
9 changed files with 184 additions and 56 deletions
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2
README.md
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@ -1,4 +1,4 @@
# Roop-GE 0.2.3b for StableDiffusion
# Roop-GE 0.2.3 for StableDiffusion
### NSFW (uncensored) version (use it on your own responsibility) of [original sd-webui-roop](https://github.com/s0md3v/sd-webui-roop) with a lot of improvements
> GE (Gourieff Edition), aka "NSFW-Roop"

1
example/api_example.py
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@ -35,6 +35,7 @@ args=[
1, #10 Upscaler visibility (if scale = 1)
False, #11 Swap in source image
True, #12 Swap in generated image
1, #13 Console Log Level (0 - min, 1 - med or 2 - max)
]
# The args for roop-ge can be found by

1
requirements.txt
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@ -2,4 +2,3 @@ insightface==0.7.3
onnx==1.14.0
onnxruntime==1.15.0
opencv-python==4.7.0.72
diffusers==0.17.1

120
scripts/console_log_patch.py Normal file
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@ -0,0 +1,120 @@
import os.path as osp
import glob
import logging
import insightface
from insightface.model_zoo.model_zoo import ModelRouter, PickableInferenceSession
from insightface.model_zoo.retinaface import RetinaFace
from insightface.model_zoo.landmark import Landmark
from insightface.model_zoo.attribute import Attribute
from insightface.model_zoo.inswapper import INSwapper
from insightface.model_zoo.arcface_onnx import ArcFaceONNX
from insightface.app import FaceAnalysis
from insightface.utils import DEFAULT_MP_NAME, ensure_available
from insightface.model_zoo import model_zoo
import onnxruntime
import onnx
from onnx import numpy_helper
from scripts.logger import logger
def patched_get_model(self, **kwargs):
session = PickableInferenceSession(self.onnx_file, **kwargs)
inputs = session.get_inputs()
input_cfg = inputs[0]
input_shape = input_cfg.shape
outputs = session.get_outputs()
if len(outputs) >= 5:
return RetinaFace(model_file=self.onnx_file, session=session)
elif input_shape[2] == 192 and input_shape[3] == 192:
return Landmark(model_file=self.onnx_file, session=session)
elif input_shape[2] == 96 and input_shape[3] == 96:
return Attribute(model_file=self.onnx_file, session=session)
elif len(inputs) == 2 and input_shape[2] == 128 and input_shape[3] == 128:
return INSwapper(model_file=self.onnx_file, session=session)
elif input_shape[2] == input_shape[3] and input_shape[2] >= 112 and input_shape[2] % 16 == 0:
return ArcFaceONNX(model_file=self.onnx_file, session=session)
else:
return None
def patched_faceanalysis_init(self, name=DEFAULT_MP_NAME, root='~/.insightface', allowed_modules=None, **kwargs):
onnxruntime.set_default_logger_severity(3)
self.models = {}
self.model_dir = ensure_available('models', name, root=root)
onnx_files = glob.glob(osp.join(self.model_dir, '*.onnx'))
onnx_files = sorted(onnx_files)
for onnx_file in onnx_files:
model = model_zoo.get_model(onnx_file, **kwargs)
if model is None:
print('model not recognized:', onnx_file)
elif allowed_modules is not None and model.taskname not in allowed_modules:
print('model ignore:', onnx_file, model.taskname)
del model
elif model.taskname not in self.models and (allowed_modules is None or model.taskname in allowed_modules):
self.models[model.taskname] = model
else:
print('duplicated model task type, ignore:', onnx_file, model.taskname)
del model
assert 'detection' in self.models
self.det_model = self.models['detection']
def patched_faceanalysis_prepare(self, ctx_id, det_thresh=0.5, det_size=(640, 640)):
self.det_thresh = det_thresh
assert det_size is not None
self.det_size = det_size
for taskname, model in self.models.items():
if taskname == 'detection':
model.prepare(ctx_id, input_size=det_size, det_thresh=det_thresh)
else:
model.prepare(ctx_id)
def patched_inswapper_init(self, model_file=None, session=None):
self.model_file = model_file
self.session = session
model = onnx.load(self.model_file)
graph = model.graph
self.emap = numpy_helper.to_array(graph.initializer[-1])
self.input_mean = 0.0
self.input_std = 255.0
if self.session is None:
self.session = onnxruntime.InferenceSession(self.model_file, None)
inputs = self.session.get_inputs()
self.input_names = []
for inp in inputs:
self.input_names.append(inp.name)
outputs = self.session.get_outputs()
output_names = []
for out in outputs:
output_names.append(out.name)
self.output_names = output_names
assert len(self.output_names) == 1
input_cfg = inputs[0]
input_shape = input_cfg.shape
self.input_shape = input_shape
self.input_size = tuple(input_shape[2:4][::-1])
def patch_insightface(get_model, faceanalysis_init, faceanalysis_prepare, inswapper_init):
insightface.model_zoo.model_zoo.ModelRouter.get_model = get_model
insightface.app.FaceAnalysis.__init__ = faceanalysis_init
insightface.app.FaceAnalysis.prepare = faceanalysis_prepare
insightface.model_zoo.inswapper.INSwapper.__init__ = inswapper_init
original_functions = [ModelRouter.get_model, FaceAnalysis.__init__, FaceAnalysis.prepare, INSwapper.__init__]
patched_functions = [patched_get_model, patched_faceanalysis_init, patched_faceanalysis_prepare, patched_inswapper_init]
def apply_logging_patch(console_logging_level):
if console_logging_level == 0:
patch_insightface(*patched_functions)
logger.setLevel(logging.WARNING)
elif console_logging_level == 1:
patch_insightface(*patched_functions)
logger.setLevel(logging.INFO)
elif console_logging_level == 2:
patch_insightface(*original_functions)
logger.setLevel(logging.INFO)

22
scripts/faceswap.py
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@ -11,9 +11,10 @@ from PIL import Image
import glob
from modules.face_restoration import FaceRestoration
from scripts.roop_logging import logger
from scripts.logger import logger
from scripts.swapper import UpscaleOptions, swap_face
from scripts.roop_version import version_flag
from scripts.version import version_flag
from scripts.console_log_patch import apply_logging_patch
import os
def get_models():
@ -34,7 +35,7 @@ class FaceSwapScript(scripts.Script):
with gr.Accordion(f"Roop-GE {version_flag}", open=False):
with gr.Column():
img = gr.inputs.Image(type="pil")
enable = gr.Checkbox(False, placeholder="enable", label="Enable")
enable = gr.Checkbox(False, label="Enable")
source_faces_index = gr.Textbox(
value="0",
placeholder="Which face(s) to use as source (comma separated)",
@ -57,7 +58,6 @@ class FaceSwapScript(scripts.Script):
)
restore_first = gr.Checkbox(
True,
placeholder="Restore face, then Upscale",
label="1. Restore face -> 2. Upscale (-Uncheck- if you want vice versa)",
)
upscaler_name = gr.inputs.Dropdown(
@ -76,7 +76,7 @@ class FaceSwapScript(scripts.Script):
)
model = gr.inputs.Dropdown(
choices=models,
label="Model not found, please download one and reload automatic 1111",
label="Model not found, please download one and reload WebUI",
)
else:
model = gr.inputs.Dropdown(
@ -85,16 +85,20 @@ class FaceSwapScript(scripts.Script):
swap_in_source = gr.Checkbox(
False,
placeholder="Swap face in source image",
label="Swap in source image",
visible=is_img2img,
)
swap_in_generated = gr.Checkbox(
True,
placeholder="Swap face in generated image",
label="Swap in generated image",
visible=is_img2img,
)
console_logging_level = gr.Radio(
["Minimum", "Medium", "Maximum"],
value="Medium",
label="Console Log Level",
type="index",
)
return [
img,
@ -110,6 +114,7 @@ class FaceSwapScript(scripts.Script):
upscaler_visibility,
swap_in_source,
swap_in_generated,
console_logging_level
]
@ -154,6 +159,7 @@ class FaceSwapScript(scripts.Script):
upscaler_visibility,
swap_in_source,
swap_in_generated,
console_logging_level,
):
self.source = img
self.face_restorer_name = face_restorer_name
@ -165,6 +171,7 @@ class FaceSwapScript(scripts.Script):
self.upscaler_name = upscaler_name
self.swap_in_generated = swap_in_generated
self.model = model
self.console_logging_level = console_logging_level
self.source_faces_index = [
int(x) for x in source_faces_index.strip(",").split(",") if x.isnumeric()
]
@ -177,6 +184,7 @@ class FaceSwapScript(scripts.Script):
self.faces_index = [0]
if self.enable:
if self.source is not None:
apply_logging_patch(console_logging_level)
if isinstance(p, StableDiffusionProcessingImg2Img) and swap_in_source:
logger.info(f"Roop-GE is enabled, face index %s", self.faces_index)

82
scripts/roop_logging.py → scripts/logger.py
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@ -1,41 +1,41 @@
import logging
import copy
import sys
from modules import shared
class ColoredFormatter(logging.Formatter):
COLORS = {
"DEBUG": "\033[0;36m", # CYAN
"INFO": "\033[0;32m", # GREEN
"WARNING": "\033[0;33m", # YELLOW
"ERROR": "\033[0;31m", # RED
"CRITICAL": "\033[0;37;41m", # WHITE ON RED
"RESET": "\033[0m", # RESET COLOR
}
def format(self, record):
colored_record = copy.copy(record)
levelname = colored_record.levelname
seq = self.COLORS.get(levelname, self.COLORS["RESET"])
colored_record.levelname = f"{seq}{levelname}{self.COLORS['RESET']}"
return super().format(colored_record)
# Create a new logger
logger = logging.getLogger("Roop-GE")
logger.propagate = False
# Add handler if we don't have one.
if not logger.handlers:
handler = logging.StreamHandler(sys.stdout)
handler.setFormatter(
ColoredFormatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
)
logger.addHandler(handler)
# Configure logger
loglevel_string = getattr(shared.cmd_opts, "roop_loglevel", "INFO")
loglevel = getattr(logging, loglevel_string.upper(), "info")
logger.setLevel(loglevel)
import logging
import copy
import sys
from modules import shared
class ColoredFormatter(logging.Formatter):
COLORS = {
"DEBUG": "\033[0;36m", # CYAN
"INFO": "\033[0;32m", # GREEN
"WARNING": "\033[0;33m", # YELLOW
"ERROR": "\033[0;31m", # RED
"CRITICAL": "\033[0;37;41m", # WHITE ON RED
"RESET": "\033[0m", # RESET COLOR
}
def format(self, record):
colored_record = copy.copy(record)
levelname = colored_record.levelname
seq = self.COLORS.get(levelname, self.COLORS["RESET"])
colored_record.levelname = f"{seq}{levelname}{self.COLORS['RESET']}"
return super().format(colored_record)
# Create a new logger
logger = logging.getLogger("Roop-GE")
logger.propagate = False
# Add handler if we don't have one.
if not logger.handlers:
handler = logging.StreamHandler(sys.stdout)
handler.setFormatter(
ColoredFormatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
)
logger.addHandler(handler)
# Configure logger
loglevel_string = getattr(shared.cmd_opts, "roop_loglevel", "INFO")
loglevel = getattr(logging, loglevel_string.upper(), "info")
logger.setLevel(loglevel)

5
scripts/roop_version.py
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@ -1,5 +0,0 @@
version_flag = "v0.2.3b"
from scripts.roop_logging import logger
logger.info(f"Roop-GE {version_flag}")

2
scripts/swapper.py
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@ -12,7 +12,7 @@ import onnxruntime
from modules.face_restoration import FaceRestoration
from modules.upscaler import UpscalerData
from scripts.roop_logging import logger
from scripts.logger import logger
providers = onnxruntime.get_available_providers()

5
scripts/version.py Normal file
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@ -0,0 +1,5 @@
version_flag = "v0.2.3"
from scripts.logger import logger
logger.info(f"Roop-GE {version_flag}")