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import gradio as gr
import modules.scripts as scripts
from modules.upscaler import Upscaler, UpscalerData
from modules import scripts, shared, images, scripts_postprocessing
from modules.processing import (
StableDiffusionProcessing,
StableDiffusionProcessingImg2Img,
)
from modules.shared import cmd_opts, opts, state
from PIL import Image
import glob
from modules.face_restoration import FaceRestoration
from scripts.logger import logger
from scripts.swapper import UpscaleOptions, swap_face
from scripts.version import version_flag, app_title
from scripts.console_log_patch import apply_logging_patch
import os
MODELS_PATH = None
def get_models():
global MODELS_PATH
models_path = os.path.join(scripts.basedir(), "models/roop/*")
models = glob.glob(models_path)
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.Column():
img = gr.inputs.Image(type="pil")
enable = gr.Checkbox(False, label="Enable", info=f"The Fast and Simple \"roop-based\" FaceSwap Extension - {version_flag}")
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"
)
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",
)
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)"
)
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,
)
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",
)
gr.Markdown("---")
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,
]
@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,
):
self.enable = enable
if self.enable:
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.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(f"Working: source face index %s, target face index %s", self.source_faces_index, self.faces_index)
for i in range(len(p.init_images)):
logger.info(f"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
else:
logger.error(f"Please provide a source face")
def postprocess_batch(self, p, *args, **kwargs):
if self.enable:
images = kwargs["images"]
def postprocess_image(self, p, script_pp: scripts.PostprocessImageArgs, *args):
if self.enable and self.swap_in_generated:
if self.source is not None:
logger.info(f"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(f"Cannot create a result image")
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