From 9a86140cf2d836291b4769e4bfab16e5846cdc22 Mon Sep 17 00:00:00 2001
From: Gourieff <777@lovemet.ru>
Date: Fri, 1 Sep 2023 12:38:07 +0700
Subject: [PATCH] UPDATE: More unique filenames
To prevent conflicts with other extensions during importing modules and scripts
+VersionUP (beta4)
---
README.md | 2 +-
README_RU.md | 2 +-
scripts/console_log_patch.py | 2 +-
scripts/reactor_api.py | 4 +-
scripts/{faceswap.py => reactor_faceswap.py} | 724 +++++++++----------
scripts/{globals.py => reactor_globals.py} | 0
scripts/{helpers.py => reactor_helpers.py} | 12 +-
scripts/{logger.py => reactor_logger.py} | 2 +-
scripts/{swapper.py => reactor_swapper.py} | 602 +++++++--------
scripts/{version.py => reactor_version.py} | 4 +-
10 files changed, 672 insertions(+), 682 deletions(-)
rename scripts/{faceswap.py => reactor_faceswap.py} (96%)
rename scripts/{globals.py => reactor_globals.py} (100%)
rename scripts/{helpers.py => reactor_helpers.py} (82%)
rename scripts/{logger.py => reactor_logger.py} (97%)
rename scripts/{swapper.py => reactor_swapper.py} (97%)
rename scripts/{version.py => reactor_version.py} (57%)
diff --git a/README.md b/README.md
index 8b5d9b0..864437f 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@
- 
+ 
[](https://github.com/Gourieff/sd-webui-reactor/commits/main)
[](https://github.com/Gourieff/sd-webui-reactor/issues?cacheSeconds=0)
diff --git a/README_RU.md b/README_RU.md
index 160e8e3..81df1f8 100644
--- a/README_RU.md
+++ b/README_RU.md
@@ -2,7 +2,7 @@
- 
+ 
[](https://github.com/Gourieff/sd-webui-reactor/commits/main)
[](https://github.com/Gourieff/sd-webui-reactor/issues?cacheSeconds=0)
diff --git a/scripts/console_log_patch.py b/scripts/console_log_patch.py
index 1e737e3..8e9d6db 100644
--- a/scripts/console_log_patch.py
+++ b/scripts/console_log_patch.py
@@ -14,7 +14,7 @@ from insightface.model_zoo import model_zoo
import onnxruntime
import onnx
from onnx import numpy_helper
-from scripts.logger import logger
+from scripts.reactor_logger import logger
def patched_get_model(self, **kwargs):
diff --git a/scripts/reactor_api.py b/scripts/reactor_api.py
index 6c2106f..ea3db93 100644
--- a/scripts/reactor_api.py
+++ b/scripts/reactor_api.py
@@ -14,8 +14,8 @@ from modules.api import api
import gradio as gr
-from scripts.swapper import UpscaleOptions, swap_face
-from scripts.logger import logger
+from scripts.reactor_swapper import UpscaleOptions, swap_face
+from scripts.reactor_logger import logger
def default_file_path():
diff --git a/scripts/faceswap.py b/scripts/reactor_faceswap.py
similarity index 96%
rename from scripts/faceswap.py
rename to scripts/reactor_faceswap.py
index 009a400..c68f511 100644
--- a/scripts/faceswap.py
+++ b/scripts/reactor_faceswap.py
@@ -1,362 +1,362 @@
-import os, glob
-import gradio as gr
-from PIL import Image
-
-from typing import List
-
-import modules.scripts as scripts
-from modules.upscaler import Upscaler, UpscalerData
-from modules import scripts, shared, images, scripts_postprocessing
-from modules.processing import (
- Processed,
- StableDiffusionProcessing,
- StableDiffusionProcessingImg2Img,
-)
-from modules.face_restoration import FaceRestoration
-from modules.paths_internal import models_path
-from modules.images import save_image
-
-from scripts.logger import logger
-from scripts.swapper import UpscaleOptions, swap_face, check_process_halt, reset_messaged
-from scripts.version import version_flag, app_title
-from scripts.console_log_patch import apply_logging_patch
-from scripts.helpers import make_grid
-
-
-MODELS_PATH = None
-
-def get_models():
- global MODELS_PATH
- models_path_init = os.path.join(models_path, "insightface/*")
- models = glob.glob(models_path_init)
- models = [x for x in models if x.endswith(".onnx") or x.endswith(".pth")]
- models_names = []
- for model in models:
- model_path = os.path.split(model)
- if MODELS_PATH is None:
- MODELS_PATH = model_path[0]
- model_name = model_path[1]
- models_names.append(model_name)
- return models_names
-
-
-class FaceSwapScript(scripts.Script):
- def title(self):
- return f"{app_title}"
-
- def show(self, is_img2img):
- return scripts.AlwaysVisible
-
- def ui(self, is_img2img):
- with gr.Accordion(f"{app_title}", open=False):
- with gr.Tab("Main"):
- with gr.Column():
- img = gr.inputs.Image(type="pil")
- enable = gr.Checkbox(False, label="Enable", info=f"The Fast and Simple FaceSwap Extension - {version_flag}")
- save_original = gr.Checkbox(False, label="Save Original", info="Save the original image(s) made before swapping; If you use \"img2img\" - this option will affect with \"Swap in generated\" only")
- gr.Markdown("
")
- gr.Markdown("Source Image (above):")
- with gr.Row():
- source_faces_index = gr.Textbox(
- value="0",
- placeholder="Which face(s) to use as Source (comma separated)",
- label="Comma separated face number(s); Example: 0,2,1",
- )
- gender_source = gr.Radio(
- ["No", "Female Only", "Male Only"],
- value="No",
- label="Gender Detection (Source)",
- type="index",
- )
- gr.Markdown("
")
- gr.Markdown("Target Image (result):")
- with gr.Row():
- faces_index = gr.Textbox(
- value="0",
- placeholder="Which face(s) to Swap into Target (comma separated)",
- label="Comma separated face number(s); Example: 1,0,2",
- )
- gender_target = gr.Radio(
- ["No", "Female Only", "Male Only"],
- value="No",
- label="Gender Detection (Target)",
- type="index",
- )
- gr.Markdown("
")
- with gr.Row():
- face_restorer_name = gr.Radio(
- label="Restore Face",
- choices=["None"] + [x.name() for x in shared.face_restorers],
- value=shared.face_restorers[0].name(),
- type="value",
- )
- face_restorer_visibility = gr.Slider(
- 0, 1, 1, step=0.1, label="Restore Face Visibility"
- )
- gr.Markdown("
")
- swap_in_source = gr.Checkbox(
- False,
- label="Swap in source image",
- visible=is_img2img,
- )
- swap_in_generated = gr.Checkbox(
- True,
- label="Swap in generated image",
- visible=is_img2img,
- )
- with gr.Tab("Upscale"):
- restore_first = gr.Checkbox(
- True,
- label="1. Restore Face -> 2. Upscale (-Uncheck- if you want vice versa)",
- info="Postprocessing Order"
- )
- upscaler_name = gr.inputs.Dropdown(
- choices=[upscaler.name for upscaler in shared.sd_upscalers],
- label="Upscaler",
- )
- gr.Markdown("
")
- with gr.Row():
- upscaler_scale = gr.Slider(1, 8, 1, step=0.1, label="Scale by")
- upscaler_visibility = gr.Slider(
- 0, 1, 1, step=0.1, label="Upscaler Visibility (if scale = 1)"
- )
- with gr.Tab("Settings"):
- models = get_models()
- with gr.Row():
- if len(models) == 0:
- logger.warning(
- "You should at least have one model in models directory, please read the doc here : https://github.com/Gourieff/sd-webui-reactor/"
- )
- model = gr.inputs.Dropdown(
- choices=models,
- label="Model not found, please download one and reload WebUI",
- )
- else:
- model = gr.inputs.Dropdown(
- choices=models, label="Model", default=models[0]
- )
- console_logging_level = gr.Radio(
- ["No log", "Minimum", "Default"],
- value="Minimum",
- label="Console Log Level",
- type="index",
- )
-
- return [
- img,
- enable,
- source_faces_index,
- faces_index,
- model,
- face_restorer_name,
- face_restorer_visibility,
- restore_first,
- upscaler_name,
- upscaler_scale,
- upscaler_visibility,
- swap_in_source,
- swap_in_generated,
- console_logging_level,
- gender_source,
- gender_target,
- save_original,
- ]
-
-
- @property
- def upscaler(self) -> UpscalerData:
- for upscaler in shared.sd_upscalers:
- if upscaler.name == self.upscaler_name:
- return upscaler
- return None
-
- @property
- def face_restorer(self) -> FaceRestoration:
- for face_restorer in shared.face_restorers:
- if face_restorer.name() == self.face_restorer_name:
- return face_restorer
- return None
-
- @property
- def upscale_options(self) -> UpscaleOptions:
- return UpscaleOptions(
- do_restore_first = self.restore_first,
- scale=self.upscaler_scale,
- upscaler=self.upscaler,
- face_restorer=self.face_restorer,
- upscale_visibility=self.upscaler_visibility,
- restorer_visibility=self.face_restorer_visibility,
- )
-
- def process(
- self,
- p: StableDiffusionProcessing,
- img,
- enable,
- source_faces_index,
- faces_index,
- model,
- face_restorer_name,
- face_restorer_visibility,
- restore_first,
- upscaler_name,
- upscaler_scale,
- upscaler_visibility,
- swap_in_source,
- swap_in_generated,
- console_logging_level,
- gender_source,
- gender_target,
- save_original,
- ):
- self.enable = enable
- if self.enable:
-
- reset_messaged()
- if check_process_halt():
- return
-
- global MODELS_PATH
- self.source = img
- self.face_restorer_name = face_restorer_name
- self.upscaler_scale = upscaler_scale
- self.upscaler_visibility = upscaler_visibility
- self.face_restorer_visibility = face_restorer_visibility
- self.restore_first = restore_first
- self.upscaler_name = upscaler_name
- self.swap_in_generated = swap_in_generated
- self.model = os.path.join(MODELS_PATH,model)
- self.console_logging_level = console_logging_level
- self.gender_source = gender_source
- self.gender_target = gender_target
- self.save_original = save_original
- if self.gender_source is None or self.gender_source == "No":
- self.gender_source = 0
- if self.gender_target is None or self.gender_target == "No":
- self.gender_target = 0
- self.source_faces_index = [
- int(x) for x in source_faces_index.strip(",").split(",") if x.isnumeric()
- ]
- self.faces_index = [
- int(x) for x in faces_index.strip(",").split(",") if x.isnumeric()
- ]
- if len(self.source_faces_index) == 0:
- self.source_faces_index = [0]
- if len(self.faces_index) == 0:
- self.faces_index = [0]
-
- if self.source is not None:
- apply_logging_patch(console_logging_level)
- if isinstance(p, StableDiffusionProcessingImg2Img) and swap_in_source:
- logger.info("Working: source face index %s, target face index %s", self.source_faces_index, self.faces_index)
-
- for i in range(len(p.init_images)):
- if len(p.init_images) > 1:
- logger.info("Swap in %s", i)
- result = swap_face(
- self.source,
- p.init_images[i],
- source_faces_index=self.source_faces_index,
- faces_index=self.faces_index,
- model=self.model,
- upscale_options=self.upscale_options,
- gender_source=self.gender_source,
- gender_target=self.gender_target,
- )
- p.init_images[i] = result
-
- if shared.state.interrupted or shared.state.skipped:
- return
-
- else:
- logger.error("Please provide a source face")
-
- def postprocess(self, p: StableDiffusionProcessing, processed: Processed, *args):
- if self.enable:
-
- reset_messaged()
- if check_process_halt():
- return
-
- if self.save_original:
-
- postprocess_run: bool = True
-
- orig_images : List[Image.Image] = processed.images[processed.index_of_first_image:]
- orig_infotexts : List[str] = processed.infotexts[processed.index_of_first_image:]
-
- result_images: List = processed.images
-
- if self.swap_in_generated:
- logger.info("Working: source face index %s, target face index %s", self.source_faces_index, self.faces_index)
- if self.source is not None:
- for i,(img,info) in enumerate(zip(orig_images, orig_infotexts)):
- if check_process_halt():
- postprocess_run = False
- break
- if len(orig_images) > 1:
- logger.info("Swap in %s", i)
- result = swap_face(
- self.source,
- img,
- source_faces_index=self.source_faces_index,
- faces_index=self.faces_index,
- model=self.model,
- upscale_options=self.upscale_options,
- gender_source=self.gender_source,
- gender_target=self.gender_target,
- )
- if result is not None:
- suffix = "-swapped"
- result_images.append(result)
- try:
- save_image(result, p.outpath_samples, "", p.all_seeds[0], p.all_prompts[0], "png",info=info, p=p, suffix=suffix)
- except:
- logger.error("Cannot save a result image - please, check SD WebUI Settings (Saving and Paths)")
- else:
- logger.error("Cannot create a result image")
-
- if shared.opts.return_grid and len(result_images) > 2 and postprocess_run:
- grid = make_grid(result_images)
- result_images.insert(0, grid)
- try:
- save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], shared.opts.grid_format, info=info, short_filename=not shared.opts.grid_extended_filename, p=p, grid=True)
- except:
- logger.error("Cannot save a grid - please, check SD WebUI Settings (Saving and Paths)")
-
- processed.images = result_images
-
- def postprocess_batch(self, p, *args, **kwargs):
- if self.enable and not self.save_original:
- images = kwargs["images"]
-
- def postprocess_image(self, p, script_pp: scripts.PostprocessImageArgs, *args):
- if self.enable and self.swap_in_generated and not self.save_original:
-
- current_job_number = shared.state.job_no + 1
- job_count = shared.state.job_count
- if current_job_number == job_count:
- reset_messaged()
- if check_process_halt():
- return
-
- if self.source is not None:
- logger.info("Working: source face index %s, target face index %s", self.source_faces_index, self.faces_index)
- image: Image.Image = script_pp.image
- result = swap_face(
- self.source,
- image,
- source_faces_index=self.source_faces_index,
- faces_index=self.faces_index,
- model=self.model,
- upscale_options=self.upscale_options,
- gender_source=self.gender_source,
- gender_target=self.gender_target,
- )
- try:
- pp = scripts_postprocessing.PostprocessedImage(result)
- pp.info = {}
- p.extra_generation_params.update(pp.info)
- script_pp.image = pp.image
- except:
- logger.error("Cannot create a result image")
+import os, glob
+import gradio as gr
+from PIL import Image
+
+from typing import List
+
+import modules.scripts as scripts
+from modules.upscaler import Upscaler, UpscalerData
+from modules import scripts, shared, images, scripts_postprocessing
+from modules.processing import (
+ Processed,
+ StableDiffusionProcessing,
+ StableDiffusionProcessingImg2Img,
+)
+from modules.face_restoration import FaceRestoration
+from modules.paths_internal import models_path
+from modules.images import save_image
+
+from scripts.reactor_logger import logger
+from scripts.reactor_swapper import UpscaleOptions, swap_face, check_process_halt, reset_messaged
+from scripts.reactor_version import version_flag, app_title
+from scripts.console_log_patch import apply_logging_patch
+from scripts.reactor_helpers import make_grid
+
+
+MODELS_PATH = None
+
+def get_models():
+ global MODELS_PATH
+ models_path_init = os.path.join(models_path, "insightface/*")
+ models = glob.glob(models_path_init)
+ models = [x for x in models if x.endswith(".onnx") or x.endswith(".pth")]
+ models_names = []
+ for model in models:
+ model_path = os.path.split(model)
+ if MODELS_PATH is None:
+ MODELS_PATH = model_path[0]
+ model_name = model_path[1]
+ models_names.append(model_name)
+ return models_names
+
+
+class FaceSwapScript(scripts.Script):
+ def title(self):
+ return f"{app_title}"
+
+ def show(self, is_img2img):
+ return scripts.AlwaysVisible
+
+ def ui(self, is_img2img):
+ with gr.Accordion(f"{app_title}", open=False):
+ with gr.Tab("Main"):
+ with gr.Column():
+ img = gr.inputs.Image(type="pil")
+ enable = gr.Checkbox(False, label="Enable", info=f"The Fast and Simple FaceSwap Extension - {version_flag}")
+ save_original = gr.Checkbox(False, label="Save Original", info="Save the original image(s) made before swapping; If you use \"img2img\" - this option will affect with \"Swap in generated\" only")
+ gr.Markdown("
")
+ gr.Markdown("Source Image (above):")
+ with gr.Row():
+ source_faces_index = gr.Textbox(
+ value="0",
+ placeholder="Which face(s) to use as Source (comma separated)",
+ label="Comma separated face number(s); Example: 0,2,1",
+ )
+ gender_source = gr.Radio(
+ ["No", "Female Only", "Male Only"],
+ value="No",
+ label="Gender Detection (Source)",
+ type="index",
+ )
+ gr.Markdown("
")
+ gr.Markdown("Target Image (result):")
+ with gr.Row():
+ faces_index = gr.Textbox(
+ value="0",
+ placeholder="Which face(s) to Swap into Target (comma separated)",
+ label="Comma separated face number(s); Example: 1,0,2",
+ )
+ gender_target = gr.Radio(
+ ["No", "Female Only", "Male Only"],
+ value="No",
+ label="Gender Detection (Target)",
+ type="index",
+ )
+ gr.Markdown("
")
+ with gr.Row():
+ face_restorer_name = gr.Radio(
+ label="Restore Face",
+ choices=["None"] + [x.name() for x in shared.face_restorers],
+ value=shared.face_restorers[0].name(),
+ type="value",
+ )
+ face_restorer_visibility = gr.Slider(
+ 0, 1, 1, step=0.1, label="Restore Face Visibility"
+ )
+ gr.Markdown("
")
+ swap_in_source = gr.Checkbox(
+ False,
+ label="Swap in source image",
+ visible=is_img2img,
+ )
+ swap_in_generated = gr.Checkbox(
+ True,
+ label="Swap in generated image",
+ visible=is_img2img,
+ )
+ with gr.Tab("Upscale"):
+ restore_first = gr.Checkbox(
+ True,
+ label="1. Restore Face -> 2. Upscale (-Uncheck- if you want vice versa)",
+ info="Postprocessing Order"
+ )
+ upscaler_name = gr.inputs.Dropdown(
+ choices=[upscaler.name for upscaler in shared.sd_upscalers],
+ label="Upscaler",
+ )
+ gr.Markdown("
")
+ with gr.Row():
+ upscaler_scale = gr.Slider(1, 8, 1, step=0.1, label="Scale by")
+ upscaler_visibility = gr.Slider(
+ 0, 1, 1, step=0.1, label="Upscaler Visibility (if scale = 1)"
+ )
+ with gr.Tab("Settings"):
+ models = get_models()
+ with gr.Row():
+ if len(models) == 0:
+ logger.warning(
+ "You should at least have one model in models directory, please read the doc here : https://github.com/Gourieff/sd-webui-reactor/"
+ )
+ model = gr.inputs.Dropdown(
+ choices=models,
+ label="Model not found, please download one and reload WebUI",
+ )
+ else:
+ model = gr.inputs.Dropdown(
+ choices=models, label="Model", default=models[0]
+ )
+ console_logging_level = gr.Radio(
+ ["No log", "Minimum", "Default"],
+ value="Minimum",
+ label="Console Log Level",
+ type="index",
+ )
+
+ return [
+ img,
+ enable,
+ source_faces_index,
+ faces_index,
+ model,
+ face_restorer_name,
+ face_restorer_visibility,
+ restore_first,
+ upscaler_name,
+ upscaler_scale,
+ upscaler_visibility,
+ swap_in_source,
+ swap_in_generated,
+ console_logging_level,
+ gender_source,
+ gender_target,
+ save_original,
+ ]
+
+
+ @property
+ def upscaler(self) -> UpscalerData:
+ for upscaler in shared.sd_upscalers:
+ if upscaler.name == self.upscaler_name:
+ return upscaler
+ return None
+
+ @property
+ def face_restorer(self) -> FaceRestoration:
+ for face_restorer in shared.face_restorers:
+ if face_restorer.name() == self.face_restorer_name:
+ return face_restorer
+ return None
+
+ @property
+ def upscale_options(self) -> UpscaleOptions:
+ return UpscaleOptions(
+ do_restore_first = self.restore_first,
+ scale=self.upscaler_scale,
+ upscaler=self.upscaler,
+ face_restorer=self.face_restorer,
+ upscale_visibility=self.upscaler_visibility,
+ restorer_visibility=self.face_restorer_visibility,
+ )
+
+ def process(
+ self,
+ p: StableDiffusionProcessing,
+ img,
+ enable,
+ source_faces_index,
+ faces_index,
+ model,
+ face_restorer_name,
+ face_restorer_visibility,
+ restore_first,
+ upscaler_name,
+ upscaler_scale,
+ upscaler_visibility,
+ swap_in_source,
+ swap_in_generated,
+ console_logging_level,
+ gender_source,
+ gender_target,
+ save_original,
+ ):
+ self.enable = enable
+ if self.enable:
+
+ reset_messaged()
+ if check_process_halt():
+ return
+
+ global MODELS_PATH
+ self.source = img
+ self.face_restorer_name = face_restorer_name
+ self.upscaler_scale = upscaler_scale
+ self.upscaler_visibility = upscaler_visibility
+ self.face_restorer_visibility = face_restorer_visibility
+ self.restore_first = restore_first
+ self.upscaler_name = upscaler_name
+ self.swap_in_generated = swap_in_generated
+ self.model = os.path.join(MODELS_PATH,model)
+ self.console_logging_level = console_logging_level
+ self.gender_source = gender_source
+ self.gender_target = gender_target
+ self.save_original = save_original
+ if self.gender_source is None or self.gender_source == "No":
+ self.gender_source = 0
+ if self.gender_target is None or self.gender_target == "No":
+ self.gender_target = 0
+ self.source_faces_index = [
+ int(x) for x in source_faces_index.strip(",").split(",") if x.isnumeric()
+ ]
+ self.faces_index = [
+ int(x) for x in faces_index.strip(",").split(",") if x.isnumeric()
+ ]
+ if len(self.source_faces_index) == 0:
+ self.source_faces_index = [0]
+ if len(self.faces_index) == 0:
+ self.faces_index = [0]
+
+ if self.source is not None:
+ apply_logging_patch(console_logging_level)
+ if isinstance(p, StableDiffusionProcessingImg2Img) and swap_in_source:
+ logger.info("Working: source face index %s, target face index %s", self.source_faces_index, self.faces_index)
+
+ for i in range(len(p.init_images)):
+ if len(p.init_images) > 1:
+ logger.info("Swap in %s", i)
+ result = swap_face(
+ self.source,
+ p.init_images[i],
+ source_faces_index=self.source_faces_index,
+ faces_index=self.faces_index,
+ model=self.model,
+ upscale_options=self.upscale_options,
+ gender_source=self.gender_source,
+ gender_target=self.gender_target,
+ )
+ p.init_images[i] = result
+
+ if shared.state.interrupted or shared.state.skipped:
+ return
+
+ else:
+ logger.error("Please provide a source face")
+
+ def postprocess(self, p: StableDiffusionProcessing, processed: Processed, *args):
+ if self.enable:
+
+ reset_messaged()
+ if check_process_halt():
+ return
+
+ if self.save_original:
+
+ postprocess_run: bool = True
+
+ orig_images : List[Image.Image] = processed.images[processed.index_of_first_image:]
+ orig_infotexts : List[str] = processed.infotexts[processed.index_of_first_image:]
+
+ result_images: List = processed.images
+
+ if self.swap_in_generated:
+ logger.info("Working: source face index %s, target face index %s", self.source_faces_index, self.faces_index)
+ if self.source is not None:
+ for i,(img,info) in enumerate(zip(orig_images, orig_infotexts)):
+ if check_process_halt():
+ postprocess_run = False
+ break
+ if len(orig_images) > 1:
+ logger.info("Swap in %s", i)
+ result = swap_face(
+ self.source,
+ img,
+ source_faces_index=self.source_faces_index,
+ faces_index=self.faces_index,
+ model=self.model,
+ upscale_options=self.upscale_options,
+ gender_source=self.gender_source,
+ gender_target=self.gender_target,
+ )
+ if result is not None:
+ suffix = "-swapped"
+ result_images.append(result)
+ try:
+ save_image(result, p.outpath_samples, "", p.all_seeds[0], p.all_prompts[0], "png",info=info, p=p, suffix=suffix)
+ except:
+ logger.error("Cannot save a result image - please, check SD WebUI Settings (Saving and Paths)")
+ else:
+ logger.error("Cannot create a result image")
+
+ if shared.opts.return_grid and len(result_images) > 2 and postprocess_run:
+ grid = make_grid(result_images)
+ result_images.insert(0, grid)
+ try:
+ save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], shared.opts.grid_format, info=info, short_filename=not shared.opts.grid_extended_filename, p=p, grid=True)
+ except:
+ logger.error("Cannot save a grid - please, check SD WebUI Settings (Saving and Paths)")
+
+ processed.images = result_images
+
+ def postprocess_batch(self, p, *args, **kwargs):
+ if self.enable and not self.save_original:
+ images = kwargs["images"]
+
+ def postprocess_image(self, p, script_pp: scripts.PostprocessImageArgs, *args):
+ if self.enable and self.swap_in_generated and not self.save_original:
+
+ current_job_number = shared.state.job_no + 1
+ job_count = shared.state.job_count
+ if current_job_number == job_count:
+ reset_messaged()
+ if check_process_halt():
+ return
+
+ if self.source is not None:
+ logger.info("Working: source face index %s, target face index %s", self.source_faces_index, self.faces_index)
+ image: Image.Image = script_pp.image
+ result = swap_face(
+ self.source,
+ image,
+ source_faces_index=self.source_faces_index,
+ faces_index=self.faces_index,
+ model=self.model,
+ upscale_options=self.upscale_options,
+ gender_source=self.gender_source,
+ gender_target=self.gender_target,
+ )
+ try:
+ pp = scripts_postprocessing.PostprocessedImage(result)
+ pp.info = {}
+ p.extra_generation_params.update(pp.info)
+ script_pp.image = pp.image
+ except:
+ logger.error("Cannot create a result image")
diff --git a/scripts/globals.py b/scripts/reactor_globals.py
similarity index 100%
rename from scripts/globals.py
rename to scripts/reactor_globals.py
diff --git a/scripts/helpers.py b/scripts/reactor_helpers.py
similarity index 82%
rename from scripts/helpers.py
rename to scripts/reactor_helpers.py
index 0f5eb59..672418b 100644
--- a/scripts/helpers.py
+++ b/scripts/reactor_helpers.py
@@ -4,16 +4,6 @@ from math import isqrt, ceil
from typing import List
def make_grid(image_list: List):
- """
- Creates a square image by combining multiple images in a grid pattern.
-
- Args:
- image_list (list): List of PIL Image objects to be combined.
-
- Returns:
- PIL Image object: The resulting square image.
- None: If the image_list is empty or contains only one image.
- """
# Count the occurrences of each image size in the image_list
size_counter = Counter(image.size for image in image_list)
@@ -52,4 +42,4 @@ def make_grid(image_list: List):
return square_image
# Return None if there are no images or only one image in the image_list
- return None
\ No newline at end of file
+ return None
diff --git a/scripts/logger.py b/scripts/reactor_logger.py
similarity index 97%
rename from scripts/logger.py
rename to scripts/reactor_logger.py
index 690df55..b8e33f0 100644
--- a/scripts/logger.py
+++ b/scripts/reactor_logger.py
@@ -3,7 +3,7 @@ import copy
import sys
from modules import shared
-from scripts.globals import IS_RUN
+from scripts.reactor_globals import IS_RUN
class ColoredFormatter(logging.Formatter):
diff --git a/scripts/swapper.py b/scripts/reactor_swapper.py
similarity index 97%
rename from scripts/swapper.py
rename to scripts/reactor_swapper.py
index 4047302..6bb0014 100644
--- a/scripts/swapper.py
+++ b/scripts/reactor_swapper.py
@@ -1,301 +1,301 @@
-import copy
-import os
-from dataclasses import dataclass
-from typing import List, Union
-
-import cv2
-import numpy as np
-from PIL import Image
-
-import insightface
-import onnxruntime
-
-from modules.face_restoration import FaceRestoration
-from modules.upscaler import UpscalerData
-from modules.shared import state
-from modules.paths_internal import models_path
-from scripts.logger import logger
-
-import warnings
-
-np.warnings = warnings
-np.warnings.filterwarnings('ignore')
-
-providers = onnxruntime.get_available_providers()
-
-
-@dataclass
-class UpscaleOptions:
- do_restore_first: bool = True
- scale: int = 1
- upscaler: UpscalerData = None
- upscale_visibility: float = 0.5
- face_restorer: FaceRestoration = None
- restorer_visibility: float = 0.5
-
-
-def cosine_distance(vector1: np.ndarray, vector2: np.ndarray) -> float:
- vec1 = vector1.flatten()
- vec2 = vector2.flatten()
-
- dot_product = np.dot(vec1, vec2)
- norm1 = np.linalg.norm(vec1)
- norm2 = np.linalg.norm(vec2)
-
- cosine_distance = 1 - (dot_product / (norm1 * norm2))
- return cosine_distance
-
-
-def cosine_similarity(test_vec: np.ndarray, source_vecs: List[np.ndarray]) -> float:
- cos_dist = sum(cosine_distance(test_vec, source_vec) for source_vec in source_vecs)
- average_cos_dist = cos_dist / len(source_vecs)
- return average_cos_dist
-
-
-MESSAGED_STOPPED = False
-MESSAGED_SKIPPED = False
-
-def reset_messaged():
- global MESSAGED_STOPPED, MESSAGED_SKIPPED
- if not state.interrupted:
- MESSAGED_STOPPED = False
- if not state.skipped:
- MESSAGED_SKIPPED = False
-
-def check_process_halt(msgforced: bool = False):
- global MESSAGED_STOPPED, MESSAGED_SKIPPED
- if state.interrupted:
- if not MESSAGED_STOPPED or msgforced:
- logger.info("Stopped by User")
- MESSAGED_STOPPED = True
- return True
- if state.skipped:
- if not MESSAGED_SKIPPED or msgforced:
- logger.info("Skipped by User")
- MESSAGED_SKIPPED = True
- return True
- return False
-
-
-FS_MODEL = None
-CURRENT_FS_MODEL_PATH = None
-
-ANALYSIS_MODEL = None
-
-
-def getAnalysisModel():
- global ANALYSIS_MODEL
- if ANALYSIS_MODEL is None:
- ANALYSIS_MODEL = insightface.app.FaceAnalysis(
- name="buffalo_l", providers=providers, root=os.path.join(models_path, "insightface") # note: allowed_modules=['detection', 'genderage']
- )
- return ANALYSIS_MODEL
-
-
-def getFaceSwapModel(model_path: str):
- global FS_MODEL
- global CURRENT_FS_MODEL_PATH
- if CURRENT_FS_MODEL_PATH is None or CURRENT_FS_MODEL_PATH != model_path:
- CURRENT_FS_MODEL_PATH = model_path
- FS_MODEL = insightface.model_zoo.get_model(model_path, providers=providers)
-
- return FS_MODEL
-
-
-def upscale_image(image: Image, upscale_options: UpscaleOptions):
- result_image = image
-
- if check_process_halt(msgforced=True):
- return result_image
-
- if upscale_options.do_restore_first:
- if upscale_options.face_restorer is not None:
- original_image = result_image.copy()
- logger.info("Restoring the face with %s", upscale_options.face_restorer.name())
- numpy_image = np.array(result_image)
- numpy_image = upscale_options.face_restorer.restore(numpy_image)
- restored_image = Image.fromarray(numpy_image)
- result_image = Image.blend(
- original_image, restored_image, upscale_options.restorer_visibility
- )
- if upscale_options.upscaler is not None and upscale_options.upscaler.name != "None":
- original_image = result_image.copy()
- logger.info(
- "Upscaling with %s scale = %s",
- upscale_options.upscaler.name,
- upscale_options.scale,
- )
- result_image = upscale_options.upscaler.scaler.upscale(
- original_image, upscale_options.scale, upscale_options.upscaler.data_path
- )
- if upscale_options.scale == 1:
- result_image = Image.blend(
- original_image, result_image, upscale_options.upscale_visibility
- )
- else:
- if upscale_options.upscaler is not None and upscale_options.upscaler.name != "None":
- original_image = result_image.copy()
- logger.info(
- "Upscaling with %s scale = %s",
- upscale_options.upscaler.name,
- upscale_options.scale,
- )
- result_image = upscale_options.upscaler.scaler.upscale(
- image, upscale_options.scale, upscale_options.upscaler.data_path
- )
- if upscale_options.scale == 1:
- result_image = Image.blend(
- original_image, result_image, upscale_options.upscale_visibility
- )
- if upscale_options.face_restorer is not None:
- original_image = result_image.copy()
- logger.info("Restoring the face with %s", upscale_options.face_restorer.name())
- numpy_image = np.array(result_image)
- numpy_image = upscale_options.face_restorer.restore(numpy_image)
- restored_image = Image.fromarray(numpy_image)
- result_image = Image.blend(
- original_image, restored_image, upscale_options.restorer_visibility
- )
-
- return result_image
-
-
-def get_face_gender(
- face,
- face_index,
- gender_condition,
- operated: str
-):
- gender = [
- x.sex
- for x in face
- ]
- gender.reverse()
- face_gender = gender[face_index]
- logger.info("%s Face %s: Detected Gender -%s-", operated, face_index, face_gender)
- if (gender_condition == 1 and face_gender == "F") or (gender_condition == 2 and face_gender == "M"):
- logger.info("OK - Detected Gender matches Condition")
- try:
- return sorted(face, key=lambda x: x.bbox[0])[face_index], 0
- except IndexError:
- return None, 0
- else:
- logger.info("WRONG - Detected Gender doesn't match Condition")
- return sorted(face, key=lambda x: x.bbox[0])[face_index], 1
-
-
-def reget_face_single(img_data, det_size, face_index):
- det_size_half = (det_size[0] // 2, det_size[1] // 2)
- return get_face_single(img_data, face_index=face_index, det_size=det_size_half)
-
-
-def get_face_single(img_data: np.ndarray, face_index=0, det_size=(640, 640), gender_source=0, gender_target=0):
- face_analyser = copy.deepcopy(getAnalysisModel())
- face_analyser.prepare(ctx_id=0, det_size=det_size)
- face = face_analyser.get(img_data)
-
- buffalo_path = os.path.join(models_path, "insightface/models/buffalo_l.zip")
- if os.path.exists(buffalo_path):
- os.remove(buffalo_path)
-
- if gender_source != 0:
- if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
- return reget_face_single(img_data, det_size, face_index)
- return get_face_gender(face,face_index,gender_source,"Source")
-
- if gender_target != 0:
- if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
- return reget_face_single(img_data, det_size, face_index)
- return get_face_gender(face,face_index,gender_target,"Target")
-
- if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
- return reget_face_single(img_data, det_size, face_index)
-
- try:
- return sorted(face, key=lambda x: x.bbox[0])[face_index], 0
- except IndexError:
- return None, 0
-
-
-def swap_face(
- source_img: Image.Image,
- target_img: Image.Image,
- model: Union[str, None] = None,
- source_faces_index: List[int] = [0],
- faces_index: List[int] = [0],
- upscale_options: Union[UpscaleOptions, None] = None,
- gender_source: int = 0,
- gender_target: int = 0,
-):
- result_image = target_img
-
- if check_process_halt():
- return result_image
-
- if model is not None:
-
- if isinstance(source_img, str): # source_img is a base64 string
- import base64, io
- if 'base64,' in source_img: # check if the base64 string has a data URL scheme
- # split the base64 string to get the actual base64 encoded image data
- base64_data = source_img.split('base64,')[-1]
- # decode base64 string to bytes
- img_bytes = base64.b64decode(base64_data)
- else:
- # if no data URL scheme, just decode
- img_bytes = base64.b64decode(source_img)
-
- source_img = Image.open(io.BytesIO(img_bytes))
-
- source_img = cv2.cvtColor(np.array(source_img), cv2.COLOR_RGB2BGR)
- target_img = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR)
-
- source_face, wrong_gender = get_face_single(source_img, face_index=source_faces_index[0], gender_source=gender_source)
-
- if len(source_faces_index) != 0 and len(source_faces_index) != 1 and len(source_faces_index) != len(faces_index):
- logger.info("Source Faces must have no entries (default=0), one entry, or same number of entries as target faces.")
- elif source_face is not None:
-
- result = target_img
- face_swapper = getFaceSwapModel(model)
-
- source_face_idx = 0
-
- swapped = 0
-
- for face_num in faces_index:
- if len(source_faces_index) > 1 and source_face_idx > 0:
- source_face, wrong_gender = get_face_single(source_img, face_index=source_faces_index[source_face_idx], gender_source=gender_source)
- source_face_idx += 1
-
- if source_face is not None and wrong_gender == 0:
- target_face, wrong_gender = get_face_single(target_img, face_index=face_num, gender_target=gender_target)
- if target_face is not None and wrong_gender == 0:
- result = face_swapper.get(result, target_face, source_face)
- swapped += 1
- elif wrong_gender == 1:
- wrong_gender = 0
- if source_face_idx == len(source_faces_index):
- result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
- if upscale_options is not None:
- result_image = upscale_image(result_image, upscale_options)
- return result_image
- else:
- logger.info(f"No target face found for {face_num}")
- elif wrong_gender == 1:
- wrong_gender = 0
- if source_face_idx == len(source_faces_index):
- result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
- if upscale_options is not None:
- result_image = upscale_image(result_image, upscale_options)
- return result_image
- else:
- logger.info(f"No source face found for face number {source_face_idx}.")
-
- result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
- if upscale_options is not None and swapped > 0:
- result_image = upscale_image(result_image, upscale_options)
-
- else:
- logger.info("No source face(s) found")
- return result_image
+import copy
+import os
+from dataclasses import dataclass
+from typing import List, Union
+
+import cv2
+import numpy as np
+from PIL import Image
+
+import insightface
+import onnxruntime
+
+from modules.face_restoration import FaceRestoration
+from modules.upscaler import UpscalerData
+from modules.shared import state
+from modules.paths_internal import models_path
+from scripts.reactor_logger import logger
+
+import warnings
+
+np.warnings = warnings
+np.warnings.filterwarnings('ignore')
+
+providers = onnxruntime.get_available_providers()
+
+
+@dataclass
+class UpscaleOptions:
+ do_restore_first: bool = True
+ scale: int = 1
+ upscaler: UpscalerData = None
+ upscale_visibility: float = 0.5
+ face_restorer: FaceRestoration = None
+ restorer_visibility: float = 0.5
+
+
+def cosine_distance(vector1: np.ndarray, vector2: np.ndarray) -> float:
+ vec1 = vector1.flatten()
+ vec2 = vector2.flatten()
+
+ dot_product = np.dot(vec1, vec2)
+ norm1 = np.linalg.norm(vec1)
+ norm2 = np.linalg.norm(vec2)
+
+ cosine_distance = 1 - (dot_product / (norm1 * norm2))
+ return cosine_distance
+
+
+def cosine_similarity(test_vec: np.ndarray, source_vecs: List[np.ndarray]) -> float:
+ cos_dist = sum(cosine_distance(test_vec, source_vec) for source_vec in source_vecs)
+ average_cos_dist = cos_dist / len(source_vecs)
+ return average_cos_dist
+
+
+MESSAGED_STOPPED = False
+MESSAGED_SKIPPED = False
+
+def reset_messaged():
+ global MESSAGED_STOPPED, MESSAGED_SKIPPED
+ if not state.interrupted:
+ MESSAGED_STOPPED = False
+ if not state.skipped:
+ MESSAGED_SKIPPED = False
+
+def check_process_halt(msgforced: bool = False):
+ global MESSAGED_STOPPED, MESSAGED_SKIPPED
+ if state.interrupted:
+ if not MESSAGED_STOPPED or msgforced:
+ logger.info("Stopped by User")
+ MESSAGED_STOPPED = True
+ return True
+ if state.skipped:
+ if not MESSAGED_SKIPPED or msgforced:
+ logger.info("Skipped by User")
+ MESSAGED_SKIPPED = True
+ return True
+ return False
+
+
+FS_MODEL = None
+CURRENT_FS_MODEL_PATH = None
+
+ANALYSIS_MODEL = None
+
+
+def getAnalysisModel():
+ global ANALYSIS_MODEL
+ if ANALYSIS_MODEL is None:
+ ANALYSIS_MODEL = insightface.app.FaceAnalysis(
+ name="buffalo_l", providers=providers, root=os.path.join(models_path, "insightface") # note: allowed_modules=['detection', 'genderage']
+ )
+ return ANALYSIS_MODEL
+
+
+def getFaceSwapModel(model_path: str):
+ global FS_MODEL
+ global CURRENT_FS_MODEL_PATH
+ if CURRENT_FS_MODEL_PATH is None or CURRENT_FS_MODEL_PATH != model_path:
+ CURRENT_FS_MODEL_PATH = model_path
+ FS_MODEL = insightface.model_zoo.get_model(model_path, providers=providers)
+
+ return FS_MODEL
+
+
+def upscale_image(image: Image, upscale_options: UpscaleOptions):
+ result_image = image
+
+ if check_process_halt(msgforced=True):
+ return result_image
+
+ if upscale_options.do_restore_first:
+ if upscale_options.face_restorer is not None:
+ original_image = result_image.copy()
+ logger.info("Restoring the face with %s", upscale_options.face_restorer.name())
+ numpy_image = np.array(result_image)
+ numpy_image = upscale_options.face_restorer.restore(numpy_image)
+ restored_image = Image.fromarray(numpy_image)
+ result_image = Image.blend(
+ original_image, restored_image, upscale_options.restorer_visibility
+ )
+ if upscale_options.upscaler is not None and upscale_options.upscaler.name != "None":
+ original_image = result_image.copy()
+ logger.info(
+ "Upscaling with %s scale = %s",
+ upscale_options.upscaler.name,
+ upscale_options.scale,
+ )
+ result_image = upscale_options.upscaler.scaler.upscale(
+ original_image, upscale_options.scale, upscale_options.upscaler.data_path
+ )
+ if upscale_options.scale == 1:
+ result_image = Image.blend(
+ original_image, result_image, upscale_options.upscale_visibility
+ )
+ else:
+ if upscale_options.upscaler is not None and upscale_options.upscaler.name != "None":
+ original_image = result_image.copy()
+ logger.info(
+ "Upscaling with %s scale = %s",
+ upscale_options.upscaler.name,
+ upscale_options.scale,
+ )
+ result_image = upscale_options.upscaler.scaler.upscale(
+ image, upscale_options.scale, upscale_options.upscaler.data_path
+ )
+ if upscale_options.scale == 1:
+ result_image = Image.blend(
+ original_image, result_image, upscale_options.upscale_visibility
+ )
+ if upscale_options.face_restorer is not None:
+ original_image = result_image.copy()
+ logger.info("Restoring the face with %s", upscale_options.face_restorer.name())
+ numpy_image = np.array(result_image)
+ numpy_image = upscale_options.face_restorer.restore(numpy_image)
+ restored_image = Image.fromarray(numpy_image)
+ result_image = Image.blend(
+ original_image, restored_image, upscale_options.restorer_visibility
+ )
+
+ return result_image
+
+
+def get_face_gender(
+ face,
+ face_index,
+ gender_condition,
+ operated: str
+):
+ gender = [
+ x.sex
+ for x in face
+ ]
+ gender.reverse()
+ face_gender = gender[face_index]
+ logger.info("%s Face %s: Detected Gender -%s-", operated, face_index, face_gender)
+ if (gender_condition == 1 and face_gender == "F") or (gender_condition == 2 and face_gender == "M"):
+ logger.info("OK - Detected Gender matches Condition")
+ try:
+ return sorted(face, key=lambda x: x.bbox[0])[face_index], 0
+ except IndexError:
+ return None, 0
+ else:
+ logger.info("WRONG - Detected Gender doesn't match Condition")
+ return sorted(face, key=lambda x: x.bbox[0])[face_index], 1
+
+
+def reget_face_single(img_data, det_size, face_index):
+ det_size_half = (det_size[0] // 2, det_size[1] // 2)
+ return get_face_single(img_data, face_index=face_index, det_size=det_size_half)
+
+
+def get_face_single(img_data: np.ndarray, face_index=0, det_size=(640, 640), gender_source=0, gender_target=0):
+ face_analyser = copy.deepcopy(getAnalysisModel())
+ face_analyser.prepare(ctx_id=0, det_size=det_size)
+ face = face_analyser.get(img_data)
+
+ buffalo_path = os.path.join(models_path, "insightface/models/buffalo_l.zip")
+ if os.path.exists(buffalo_path):
+ os.remove(buffalo_path)
+
+ if gender_source != 0:
+ if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
+ return reget_face_single(img_data, det_size, face_index)
+ return get_face_gender(face,face_index,gender_source,"Source")
+
+ if gender_target != 0:
+ if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
+ return reget_face_single(img_data, det_size, face_index)
+ return get_face_gender(face,face_index,gender_target,"Target")
+
+ if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
+ return reget_face_single(img_data, det_size, face_index)
+
+ try:
+ return sorted(face, key=lambda x: x.bbox[0])[face_index], 0
+ except IndexError:
+ return None, 0
+
+
+def swap_face(
+ source_img: Image.Image,
+ target_img: Image.Image,
+ model: Union[str, None] = None,
+ source_faces_index: List[int] = [0],
+ faces_index: List[int] = [0],
+ upscale_options: Union[UpscaleOptions, None] = None,
+ gender_source: int = 0,
+ gender_target: int = 0,
+):
+ result_image = target_img
+
+ if check_process_halt():
+ return result_image
+
+ if model is not None:
+
+ if isinstance(source_img, str): # source_img is a base64 string
+ import base64, io
+ if 'base64,' in source_img: # check if the base64 string has a data URL scheme
+ # split the base64 string to get the actual base64 encoded image data
+ base64_data = source_img.split('base64,')[-1]
+ # decode base64 string to bytes
+ img_bytes = base64.b64decode(base64_data)
+ else:
+ # if no data URL scheme, just decode
+ img_bytes = base64.b64decode(source_img)
+
+ source_img = Image.open(io.BytesIO(img_bytes))
+
+ source_img = cv2.cvtColor(np.array(source_img), cv2.COLOR_RGB2BGR)
+ target_img = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR)
+
+ source_face, wrong_gender = get_face_single(source_img, face_index=source_faces_index[0], gender_source=gender_source)
+
+ if len(source_faces_index) != 0 and len(source_faces_index) != 1 and len(source_faces_index) != len(faces_index):
+ logger.info("Source Faces must have no entries (default=0), one entry, or same number of entries as target faces.")
+ elif source_face is not None:
+
+ result = target_img
+ face_swapper = getFaceSwapModel(model)
+
+ source_face_idx = 0
+
+ swapped = 0
+
+ for face_num in faces_index:
+ if len(source_faces_index) > 1 and source_face_idx > 0:
+ source_face, wrong_gender = get_face_single(source_img, face_index=source_faces_index[source_face_idx], gender_source=gender_source)
+ source_face_idx += 1
+
+ if source_face is not None and wrong_gender == 0:
+ target_face, wrong_gender = get_face_single(target_img, face_index=face_num, gender_target=gender_target)
+ if target_face is not None and wrong_gender == 0:
+ result = face_swapper.get(result, target_face, source_face)
+ swapped += 1
+ elif wrong_gender == 1:
+ wrong_gender = 0
+ if source_face_idx == len(source_faces_index):
+ result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
+ if upscale_options is not None:
+ result_image = upscale_image(result_image, upscale_options)
+ return result_image
+ else:
+ logger.info(f"No target face found for {face_num}")
+ elif wrong_gender == 1:
+ wrong_gender = 0
+ if source_face_idx == len(source_faces_index):
+ result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
+ if upscale_options is not None:
+ result_image = upscale_image(result_image, upscale_options)
+ return result_image
+ else:
+ logger.info(f"No source face found for face number {source_face_idx}.")
+
+ result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
+ if upscale_options is not None and swapped > 0:
+ result_image = upscale_image(result_image, upscale_options)
+
+ else:
+ logger.info("No source face(s) found")
+ return result_image
diff --git a/scripts/version.py b/scripts/reactor_version.py
similarity index 57%
rename from scripts/version.py
rename to scripts/reactor_version.py
index 61b56b8..612f35c 100644
--- a/scripts/version.py
+++ b/scripts/reactor_version.py
@@ -1,7 +1,7 @@
app_title = "ReActor"
-version_flag = "v0.4.1-b3"
+version_flag = "v0.4.1-b4"
-from scripts.logger import logger, get_Run, set_Run
+from scripts.reactor_logger import logger, get_Run, set_Run
is_run = get_Run()