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import os
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.roop_logging import logger
from scripts.swapper import UpscaleOptions, swap_face, ImageResult
from scripts.roop_version import version_flag
import os
def get_models():
models_path = os.path.join(scripts.basedir(), "models" + os.path.sep + "roop" + os.path.sep + "*")
models = glob.glob(models_path)
models = [x for x in models if x.endswith(".onnx") or x.endswith(".pth")]
return models
class FaceSwapScript(scripts.Script):
def title(self):
return f"roop"
def show(self, is_img2img):
return scripts.AlwaysVisible
def ui(self, is_img2img):
with gr.Accordion(f"roop {version_flag}", open=False):
with gr.Column():
img = gr.inputs.Image(type="pil")
enable = gr.Checkbox(False, placeholder="enable", label="Enable")
faces_index = gr.Textbox(
value="0",
placeholder="Which face to swap (comma separated), start from 0",
label="Comma separated face number(s)",
)
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 visibility"
)
upscaler_name = gr.inputs.Dropdown(
choices=[upscaler.name for upscaler in shared.sd_upscalers],
label="Upscaler",
)
upscaler_scale = gr.Slider(1, 8, 1, step=0.1, label="Upscaler scale")
upscaler_visibility = gr.Slider(
0, 1, 1, step=0.1, label="Upscaler visibility (if scale = 1)"
)
models = get_models()
if len(models) == 0:
logger.warning(
"You should at least have one model in models directory, please read the doc here : https://github.com/s0md3v/sd-webui-roop/"
)
model = gr.inputs.Dropdown(
choices=models,
label="Model not found, please download one and reload automatic 1111",
)
else:
model = gr.inputs.Dropdown(
choices=models, label="Model", default=models[0]
)
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,
)
return [
img,
enable,
faces_index,
model,
face_restorer_name,
face_restorer_visibility,
upscaler_name,
upscaler_scale,
upscaler_visibility,
swap_in_source,
swap_in_generated,
]
@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(
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,
faces_index,
model,
face_restorer_name,
face_restorer_visibility,
upscaler_name,
upscaler_scale,
upscaler_visibility,
swap_in_source,
swap_in_generated,
):
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.enable = enable
self.upscaler_name = upscaler_name
self.swap_in_generated = swap_in_generated
self.model = model
self.faces_index = {
int(x) for x in faces_index.strip(",").split(",") if x.isnumeric()
}
if len(self.faces_index) == 0:
self.faces_index = {0}
if self.enable:
if self.source is not None:
if isinstance(p, StableDiffusionProcessingImg2Img) and swap_in_source:
logger.info(f"roop enabled, face index %s", self.faces_index)
for i in range(len(p.init_images)):
logger.info(f"Swap in source %s", i)
result = swap_face(
self.source,
p.init_images[i],
faces_index=self.faces_index,
model=self.model,
upscale_options=self.upscale_options,
)
p.init_images[i] = result.image()
else:
logger.error(f"Please provide a source face")
def postprocess_batch(self, *args, **kwargs):
if self.enable:
return 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:
image: Image.Image = script_pp.image
result: ImageResult = swap_face(
self.source,
image,
faces_index=self.faces_index,
model=self.model,
upscale_options=self.upscale_options,
)
pp = scripts_postprocessing.PostprocessedImage(result.image())
pp.info = {}
p.extra_generation_params.update(pp.info)
script_pp.image = pp.image
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