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import os, glob
import gradio as gr
from PIL import Image
try:
import torch.cuda as cuda
EP_is_visible = True if cuda.is_available() else False
except:
EP_is_visible = False
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.images import save_image
try:
from modules.paths_internal import models_path
except:
try:
from modules.paths import models_path
except:
model_path = os.path.abspath("models")
from scripts.reactor_logger import logger
from scripts.reactor_swapper import (
EnhancementOptions,
swap_face,
check_process_halt,
reset_messaged,
build_face_model
)
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, get_image_path, set_Device, get_model_names, get_facemodels
from scripts.reactor_globals import DEVICE, DEVICE_LIST
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):
def update_fm_list(selected: str):
return gr.Dropdown.update(
value=selected, choices=get_model_names(get_facemodels)
)
def update_upscalers_list(selected: str):
return gr.Dropdown.update(
value=selected, choices=[upscaler.name for upscaler in shared.sd_upscalers]
)
def update_models_list(selected: str):
return gr.Dropdown.update(
value=selected, choices=get_models()
)
# TAB MAIN
with gr.Tab("Main"):
with gr.Column():
img = gr.Image(
type="pil",
label="Source Image",
)
# face_model = gr.File(
# file_types=[".safetensors"],
# label="Face Model",
# show_label=True,
# )
enable = gr.Checkbox(False, label="Enable", info=f"The Fast and Simple FaceSwap Extension - {version_flag}")
gr.Markdown("<br>")
with gr.Row():
select_source = gr.Radio(
["Image","Face Model"],
value="Image",
label="Select Source",
type="index",
scale=1,
)
face_models = get_model_names(get_facemodels)
face_model = gr.Dropdown(
choices=face_models,
label="Choose Face Model",
value="None",
scale=2,
)
fm_update = gr.Button(
value="🔄",
variant="tool",
)
fm_update.click(
update_fm_list,
inputs=[face_model],
outputs=[face_model],
)
setattr(face_model, "do_not_save_to_config", True)
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"
)
mask_face = gr.Checkbox(
False,
label="Face Mask Correction",
info="Apply this option if you see some pixelation around face contours"
)
gr.Markdown("<br>")
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("<br>")
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("<br>")
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",
)
with gr.Column():
face_restorer_visibility = gr.Slider(
0, 1, 1, step=0.1, label="Restore Face Visibility"
)
codeformer_weight = gr.Slider(
0, 1, 0.5, step=0.1, label="CodeFormer Weight", info="0 = maximum effect, 1 = minimum effect"
)
gr.Markdown("<br>")
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,
)
# TAB UPSCALE
with gr.Tab("Upscale"):
restore_first = gr.Checkbox(
True,
label="1. Restore Face -> 2. Upscale (-Uncheck- if you want vice versa)",
info="Postprocessing Order"
)
with gr.Row():
upscaler_name = gr.Dropdown(
choices=[upscaler.name for upscaler in shared.sd_upscalers],
label="Upscaler",
value="None",
info="Won't scale if you choose -Swap in Source- via img2img, only 1x-postprocessing will affect (texturing, denoising, restyling etc.)"
)
upscalers_update = gr.Button(
value="🔄",
variant="tool",
)
upscalers_update.click(
update_upscalers_list,
inputs=[upscaler_name],
outputs=[upscaler_name],
)
gr.Markdown("<br>")
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)"
)
# TAB TOOLS
with gr.Tab("Tools 🆕"):
with gr.Tab("Face Models"):
gr.Markdown("Load an image containing one person, name it and click 'Build and Save'")
img_fm = gr.Image(
type="pil",
label="Load Image to build Face Model",
)
with gr.Row(equal_height=True):
fm_name = gr.Textbox(
value="",
placeholder="Please type any name (e.g. Elena)",
label="Face Model Name",
)
save_fm_btn = gr.Button("Build and Save")
save_fm = gr.Markdown("You can find saved models in 'models/reactor/faces'")
save_fm_btn.click(
build_face_model,
inputs=[img_fm, fm_name],
outputs=[save_fm],
)
# TAB SETTINGS
with gr.Tab("Settings"):
models = get_models()
with gr.Row(visible=EP_is_visible):
device = gr.Radio(
label="Execution Provider",
choices=DEVICE_LIST,
value=DEVICE,
type="value",
info="If you already run 'Generate' - RESTART is required to apply. Click 'Save', (A1111) Extensions Tab -> 'Apply and restart UI' or (SD.Next) close the Server and start it again",
scale=2,
)
save_device_btn = gr.Button("Save", scale=0)
save = gr.Markdown("", visible=EP_is_visible)
setattr(device, "do_not_save_to_config", True)
save_device_btn.click(
set_Device,
inputs=[device],
outputs=[save],
)
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.Dropdown(
choices=models,
label="Model not found, please download one and refresh the list"
)
else:
model = gr.Dropdown(
choices=models, label="Model", value=models[0]
)
models_update = gr.Button(
value="🔄",
variant="tool",
)
models_update.click(
update_models_list,
inputs=[model],
outputs=[model],
)
console_logging_level = gr.Radio(
["No log", "Minimum", "Default"],
value="Minimum",
label="Console Log Level",
type="index"
)
gr.Markdown("<br>")
with gr.Row():
source_hash_check = gr.Checkbox(
True,
label="Source Image Hash Check",
info="Recommended to keep it ON. Processing is faster when Source Image is the same."
)
target_hash_check = gr.Checkbox(
False,
label="Target Image Hash Check",
info="Affects if you use Extras tab or img2img with only 'Swap in source image' on."
)
gr.Markdown("<span style='display:block;text-align:right;padding:3px;font-size:0.666em'>by Eugene Gourieff</span>")
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,
codeformer_weight,
source_hash_check,
target_hash_check,
device,
mask_face,
select_source,
face_model,
]
@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 enhancement_options(self) -> EnhancementOptions:
return EnhancementOptions(
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,
codeformer_weight=self.codeformer_weight,
)
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,
codeformer_weight,
source_hash_check,
target_hash_check,
device,
mask_face,
select_source,
face_model,
):
self.enable = enable
if self.enable:
logger.debug("*** Start process")
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_source = swap_in_source
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
self.codeformer_weight = codeformer_weight
self.source_hash_check = source_hash_check
self.target_hash_check = target_hash_check
self.device = device
self.mask_face = mask_face
self.select_source = select_source
self.face_model = face_model
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.save_original is None:
self.save_original = False
if self.source_hash_check is None:
self.source_hash_check = True
if self.target_hash_check is None:
self.target_hash_check = False
if self.mask_face is None:
self.mask_face = False
logger.debug("*** Set Device")
set_Device(self.device)
if (self.source is not None and self.select_source == 0) or ((self.face_model is not None and self.face_model != "None") and self.select_source == 1):
logger.debug("*** Log patch")
apply_logging_patch(console_logging_level)
if isinstance(p, StableDiffusionProcessingImg2Img) and self.swap_in_source:
logger.status("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.status("Swap in %s", i)
result, output, swapped = swap_face(
self.source,
p.init_images[i],
source_faces_index=self.source_faces_index,
faces_index=self.faces_index,
model=self.model,
enhancement_options=self.enhancement_options,
gender_source=self.gender_source,
gender_target=self.gender_target,
source_hash_check=self.source_hash_check,
target_hash_check=self.target_hash_check,
device=self.device,
mask_face=self.mask_face,
select_source=self.select_source,
face_model = self.face_model,
)
p.init_images[i] = result
# result_path = get_image_path(p.init_images[i], p.outpath_samples, "", p.all_seeds[i], p.all_prompts[i], "txt", p=p, suffix="-swapped")
# if len(output) != 0:
# with open(result_path, 'w', encoding="utf8") as f:
# f.writelines(output)
if shared.state.interrupted or shared.state.skipped:
return
else:
logger.error("Please provide a source face")
return
def postprocess(self, p: StableDiffusionProcessing, processed: Processed, *args):
if self.enable:
logger.debug("*** Check postprocess")
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
# result_info: List = processed.infotexts
if self.swap_in_generated:
logger.status("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.status("Swap in %s", i)
result, output, swapped = swap_face(
self.source,
img,
source_faces_index=self.source_faces_index,
faces_index=self.faces_index,
model=self.model,
enhancement_options=self.enhancement_options,
gender_source=self.gender_source,
gender_target=self.gender_target,
source_hash_check=self.source_hash_check,
target_hash_check=self.target_hash_check,
device=self.device,
mask_face=self.mask_face,
select_source=self.select_source,
face_model = self.face_model,
)
if result is not None and swapped > 0:
result_images.append(result)
suffix = "-swapped"
try:
img_path = 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)")
elif result is None:
logger.error("Cannot create a result image")
# if len(output) != 0:
# split_fullfn = os.path.splitext(img_path[0])
# fullfn = split_fullfn[0] + ".txt"
# with open(fullfn, 'w', encoding="utf8") as f:
# f.writelines(output)
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
# processed.infotexts = result_info
def postprocess_batch(self, p, *args, **kwargs):
if self.enable and not self.save_original:
logger.debug("*** Check postprocess_batch")
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:
logger.debug("*** Check postprocess_image")
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 and self.select_source == 0) or ((self.face_model is not None and self.face_model != "None") and self.select_source == 1):
logger.status("Working: source face index %s, target face index %s", self.source_faces_index, self.faces_index)
image: Image.Image = script_pp.image
result, output, swapped = swap_face(
self.source,
image,
source_faces_index=self.source_faces_index,
faces_index=self.faces_index,
model=self.model,
enhancement_options=self.enhancement_options,
gender_source=self.gender_source,
gender_target=self.gender_target,
source_hash_check=self.source_hash_check,
target_hash_check=self.target_hash_check,
device=self.device,
mask_face=self.mask_face,
select_source=self.select_source,
face_model = self.face_model,
)
try:
pp = scripts_postprocessing.PostprocessedImage(result)
pp.info = {}
p.extra_generation_params.update(pp.info)
script_pp.image = pp.image
# if len(output) != 0:
# result_path = get_image_path(script_pp.image, p.outpath_samples, "", p.all_seeds[0], p.all_prompts[0], "txt", p=p, suffix="-swapped")
# if len(output) != 0:
# with open(result_path, 'w', encoding="utf8") as f:
# f.writelines(output)
except:
logger.error("Cannot create a result image")
class FaceSwapScriptExtras(scripts_postprocessing.ScriptPostprocessing):
name = 'ReActor'
order = 20000
def ui(self):
with gr.Accordion(f"{app_title}", open=False):
def update_fm_list(selected: str):
return gr.Dropdown.update(
value=selected, choices=get_model_names(get_facemodels)
)
def update_upscalers_list(selected: str):
return gr.Dropdown.update(
value=selected, choices=[upscaler.name for upscaler in shared.sd_upscalers]
)
def update_models_list(selected: str):
return gr.Dropdown.update(
value=selected, choices=get_models()
)
# TAB MAIN
with gr.Tab("Main"):
with gr.Column():
img = gr.Image(type="pil")
enable = gr.Checkbox(False, label="Enable", info=f"The Fast and Simple FaceSwap Extension - {version_flag}")
# gr.Markdown("<br>")
with gr.Row():
select_source = gr.Radio(
["Image","Face Model"],
value="Image",
label="Select Source",
type="index",
scale=1,
)
face_models = get_model_names(get_facemodels)
face_model = gr.Dropdown(
choices=face_models,
label="Choose Face Model",
value="None",
scale=2,
)
fm_update = gr.Button(
value="🔄",
variant="tool",
)
fm_update.click(
update_fm_list,
inputs=[face_model],
outputs=[face_model],
)
setattr(face_model, "do_not_save_to_config", True)
mask_face = gr.Checkbox(
False,
label="Face Mask Correction",
info="Apply this option if you see some pixelation around face contours"
)
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("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",
)
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",
)
with gr.Column():
face_restorer_visibility = gr.Slider(
0, 1, 1, step=0.1, label="Restore Face Visibility"
)
codeformer_weight = gr.Slider(
0, 1, 0.5, step=0.1, label="CodeFormer Weight", info="0 = maximum effect, 1 = minimum effect"
)
# TAB UPSCALE
with gr.Tab("Upscale"):
restore_first = gr.Checkbox(
True,
label="1. Restore Face -> 2. Upscale (-Uncheck- if you want vice versa)",
info="Postprocessing Order"
)
with gr.Row():
upscaler_name = gr.Dropdown(
choices=[upscaler.name for upscaler in shared.sd_upscalers],
label="Upscaler",
value="None",
info="Won't scale if you choose -Swap in Source- via img2img, only 1x-postprocessing will affect (texturing, denoising, restyling etc.)"
)
upscalers_update = gr.Button(
value="🔄",
variant="tool",
)
upscalers_update.click(
update_upscalers_list,
inputs=[upscaler_name],
outputs=[upscaler_name],
)
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)"
)
# TAB TOOLS
with gr.Tab("Tools 🆕"):
with gr.Tab("Face Models"):
gr.Markdown("Load an image containing one person, name it and click 'Build and Save'")
img_fm = gr.Image(
type="pil",
label="Load Image to build Face Model",
)
with gr.Row(equal_height=True):
fm_name = gr.Textbox(
value="",
placeholder="Please type any name (e.g. Elena)",
label="Face Model Name",
)
save_fm_btn = gr.Button("Build and Save")
save_fm = gr.Markdown("You can find saved models in 'models/reactor/faces'")
save_fm_btn.click(
build_face_model,
inputs=[img_fm, fm_name],
outputs=[save_fm],
)
# TAB SETTINGS
with gr.Tab("Settings"):
models = get_models()
with gr.Row(visible=EP_is_visible):
device = gr.Radio(
label="Execution Provider",
choices=DEVICE_LIST,
value=DEVICE,
type="value",
info="If you already run 'Generate' - RESTART is required to apply. Click 'Save', (A1111) Extensions Tab -> 'Apply and restart UI' or (SD.Next) close the Server and start it again",
scale=2,
)
save_device_btn = gr.Button("Save", scale=0)
save = gr.Markdown("", visible=EP_is_visible)
setattr(device, "do_not_save_to_config", True)
save_device_btn.click(
set_Device,
inputs=[device],
outputs=[save],
)
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.Dropdown(
choices=models,
label="Model not found, please download one and refresh the list",
)
else:
model = gr.Dropdown(
choices=models, label="Model", value=models[0]
)
models_update = gr.Button(
value="🔄",
variant="tool",
)
models_update.click(
update_models_list,
inputs=[model],
outputs=[model],
)
console_logging_level = gr.Radio(
["No log", "Minimum", "Default"],
value="Minimum",
label="Console Log Level",
type="index",
)
gr.Markdown("<span style='display:block;text-align:right;padding-right:3px;font-size:0.666em;margin: -9px 0'>by Eugene Gourieff</span>")
args = {
'img': img,
'enable': enable,
'source_faces_index': source_faces_index,
'faces_index': faces_index,
'model': model,
'face_restorer_name': face_restorer_name,
'face_restorer_visibility': face_restorer_visibility,
'restore_first': restore_first,
'upscaler_name': upscaler_name,
'upscaler_scale': upscaler_scale,
'upscaler_visibility': upscaler_visibility,
'console_logging_level': console_logging_level,
'gender_source': gender_source,
'gender_target': gender_target,
'codeformer_weight': codeformer_weight,
'device': device,
'mask_face': mask_face,
'select_source': select_source,
'face_model': face_model,
}
return args
@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 enhancement_options(self) -> EnhancementOptions:
return EnhancementOptions(
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,
codeformer_weight=self.codeformer_weight,
)
def process(self, pp: scripts_postprocessing.PostprocessedImage, **args):
if args['enable']:
reset_messaged()
if check_process_halt():
return
global MODELS_PATH
self.source = args['img']
self.face_restorer_name = args['face_restorer_name']
self.upscaler_scale = args['upscaler_scale']
self.upscaler_visibility = args['upscaler_visibility']
self.face_restorer_visibility = args['face_restorer_visibility']
self.restore_first = args['restore_first']
self.upscaler_name = args['upscaler_name']
self.model = os.path.join(MODELS_PATH, args['model'])
self.console_logging_level = args['console_logging_level']
self.gender_source = args['gender_source']
self.gender_target = args['gender_target']
self.codeformer_weight = args['codeformer_weight']
self.device = args['device']
self.mask_face = args['mask_face']
self.select_source = args['select_source']
self.face_model = args['face_model']
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 args['source_faces_index'].strip(",").split(",") if x.isnumeric()
]
self.faces_index = [
int(x) for x in args['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.mask_face is None:
self.mask_face = False
current_job_number = shared.state.job_no + 1
job_count = shared.state.job_count
if current_job_number == job_count:
reset_messaged()
set_Device(self.device)
if (self.source is not None and self.select_source == 0) or ((self.face_model is not None and self.face_model != "None") and self.select_source == 1):
apply_logging_patch(self.console_logging_level)
logger.status("Working: source face index %s, target face index %s", self.source_faces_index, self.faces_index)
image: Image.Image = pp.image
result, output, swapped = swap_face(
self.source,
image,
source_faces_index=self.source_faces_index,
faces_index=self.faces_index,
model=self.model,
enhancement_options=self.enhancement_options,
gender_source=self.gender_source,
gender_target=self.gender_target,
source_hash_check=True,
target_hash_check=True,
device=self.device,
mask_face=self.mask_face,
select_source=self.select_source,
face_model=self.face_model,
)
try:
pp.info["ReActor"] = True
pp.image = result
logger.status("---Done!---")
except Exception:
logger.error("Cannot create a result image")
else:
logger.error("Please provide a source face")
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