clean code, fix extract

check.sh

@@ -0,0 +1,4 @@
+#!/bin/bash
+autoflake --in-place --remove-unused-variables -r --remove-all-unused-imports .
+mypy --install-types
+pre-commit run --all-files

example/api/roop_api_example.py

@@ -1,5 +1,4 @@
 import requests
-from PIL import Image
 from client_utils import (
     FaceSwapRequest,
     FaceSwapUnit,

install.py

@@ -14,7 +14,7 @@ model_name = os.path.basename(model_url)
 model_path = os.path.join(models_dir, model_name)
 
 
-def download(url, path):
+def download(url: str, path: str) -> None:
     request = urllib.request.urlopen(url)
     total = int(request.headers.get("Content-Length", 0))
     with tqdm(

mypy.ini

@@ -4,4 +4,5 @@ disallow_any_generics = True
 disallow_untyped_calls = True
 disallow_untyped_defs = True
 ignore_missing_imports = True
-strict_optional = False
+strict_optional = False
+explicit_package_bases=True

preload.py

@@ -1,4 +1,7 @@
-def preload(parser):
+from argparse import ArgumentParser
+
+
+def preload(parser: ArgumentParser) -> None:
     parser.add_argument(
         "--faceswaplab_loglevel",
         default="INFO",

scripts/faceswaplab.py

@@ -4,7 +4,6 @@ from scripts.faceswaplab_settings import faceswaplab_settings
 from scripts.faceswaplab_ui import faceswaplab_tab, faceswaplab_unit_ui
 from scripts.faceswaplab_utils.models_utils import (
     get_current_model,
-    get_face_checkpoints,
 )
 
 from scripts import faceswaplab_globals
@@ -12,7 +11,6 @@ from scripts.faceswaplab_swapping import swapper
 from scripts.faceswaplab_utils import faceswaplab_logging, imgutils
 from scripts.faceswaplab_utils import models_utils
 from scripts.faceswaplab_postprocessing import upscaling
-import numpy as np
 
 # Reload all the modules when using "apply and restart"
 # This is mainly done for development purposes
@@ -29,15 +27,13 @@ importlib.reload(faceswaplab_api)
 import os
 from dataclasses import fields
 from pprint import pformat
-from typing import List, Optional, Tuple
+from typing import Any, List, Optional, Tuple
 
-import dill as pickle
 import gradio as gr
 import modules.scripts as scripts
 from modules import script_callbacks, scripts
-from insightface.app.common import Face
 from modules import scripts, shared
-from modules.images import save_image, image_grid
+from modules.images import save_image
 from modules.processing import (
     Processed,
     StableDiffusionProcessing,
@@ -46,7 +42,6 @@ from modules.processing import (
 from modules.shared import opts
 from PIL import Image
 
-from scripts.faceswaplab_utils.imgutils import pil_to_cv2, check_against_nsfw
 from scripts.faceswaplab_utils.faceswaplab_logging import logger, save_img_debug
 from scripts.faceswaplab_globals import VERSION_FLAG
 from scripts.faceswaplab_postprocessing.postprocessing_options import (
@@ -76,15 +71,15 @@ class FaceSwapScript(scripts.Script):
         super().__init__()
 
     @property
-    def units_count(self):
+    def units_count(self) -> int:
         return opts.data.get("faceswaplab_units_count", 3)
 
     @property
-    def upscaled_swapper_in_generated(self):
+    def upscaled_swapper_in_generated(self) -> bool:
         return opts.data.get("faceswaplab_upscaled_swapper", False)
 
     @property
-    def upscaled_swapper_in_source(self):
+    def upscaled_swapper_in_source(self) -> bool:
         return opts.data.get("faceswaplab_upscaled_swapper_in_source", False)
 
     @property
@@ -93,24 +88,24 @@ class FaceSwapScript(scripts.Script):
         return any([u.enable for u in self.units]) and not shared.state.interrupted
 
     @property
-    def keep_original_images(self):
+    def keep_original_images(self) -> bool:
         return opts.data.get("faceswaplab_keep_original", False)
 
     @property
-    def swap_in_generated_units(self):
+    def swap_in_generated_units(self) -> List[FaceSwapUnitSettings]:
         return [u for u in self.units if u.swap_in_generated and u.enable]
 
     @property
-    def swap_in_source_units(self):
+    def swap_in_source_units(self) -> List[FaceSwapUnitSettings]:
         return [u for u in self.units if u.swap_in_source and u.enable]
 
-    def title(self):
+    def title(self) -> str:
         return f"faceswaplab"
 
-    def show(self, is_img2img):
+    def show(self, is_img2img: bool) -> bool:
         return scripts.AlwaysVisible
 
-    def ui(self, is_img2img):
+    def ui(self, is_img2img: bool) -> List[gr.components.Component]:
         with gr.Accordion(f"FaceSwapLab {VERSION_FLAG}", open=False):
             components = []
             for i in range(1, self.units_count + 1):
@@ -119,16 +114,9 @@ class FaceSwapScript(scripts.Script):
         # If the order is modified, the before_process should be changed accordingly.
         return components + upscaler
 
-    # def make_script_first(self,p: StableDiffusionProcessing) :
+    def read_config(
-    # FIXME : not really useful, will only impact postprocessing (kept for further testing)
+        self, p: StableDiffusionProcessing, *components: List[gr.components.Component]
-    #     runner : scripts.ScriptRunner = p.scripts
+    ) -> None:
-    #     alwayson = runner.alwayson_scripts
-    #     alwayson.pop(alwayson.index(self))
-    #     alwayson.insert(0, self)
-    #     print("Running in ", alwayson.index(self), "position")
-    #     logger.info("Running scripts : %s", pformat(runner.alwayson_scripts))
-
-    def read_config(self, p: StableDiffusionProcessing, *components):
         # The order of processing for the components is important
         # The method first process faceswap units then postprocessing units
 
@@ -148,14 +136,16 @@ class FaceSwapScript(scripts.Script):
         len_conf: int = len(fields(FaceSwapUnitSettings))
         shift: int = self.units_count * len_conf
         self.postprocess_options = PostProcessingOptions(
-            *components[shift : shift + len(fields(PostProcessingOptions))]
+            *components[shift : shift + len(fields(PostProcessingOptions))]  # type: ignore
         )
         logger.debug("%s", pformat(self.postprocess_options))
 
         if self.enabled:
             p.do_not_save_samples = not self.keep_original_images
 
-    def process(self, p: StableDiffusionProcessing, *components):
+    def process(
+        self, p: StableDiffusionProcessing, *components: List[gr.components.Component]
+    ) -> None:
         self.read_config(p, *components)
 
         # If is instance of img2img, we check if face swapping in source is required.
@@ -175,7 +165,9 @@ class FaceSwapScript(scripts.Script):
                 if new_inits is not None:
                     p.init_images = [img[0] for img in new_inits]
 
-    def postprocess(self, p: StableDiffusionProcessing, processed: Processed, *args):
+    def postprocess(
+        self, p: StableDiffusionProcessing, processed: Processed, *args: List[Any]
+    ) -> None:
         if self.enabled:
             # Get the original images without the grid
             orig_images: List[Image.Image] = processed.images[

scripts/faceswaplab_api/faceswaplab_api.py

@@ -1,52 +1,68 @@
 from PIL import Image
 import numpy as np
-from fastapi import FastAPI, Body
+from fastapi import FastAPI
-from fastapi.exceptions import HTTPException
-from modules.api.models import *
 from modules.api import api
 from scripts.faceswaplab_api.faceswaplab_api_types import (
-    FaceSwapUnit,
     FaceSwapRequest,
     FaceSwapResponse,
 )
 from scripts.faceswaplab_globals import VERSION_FLAG
 import gradio as gr
-from typing import List, Optional
+from typing import Dict, List, Optional, Union
 from scripts.faceswaplab_swapping import swapper
-from scripts.faceswaplab_utils.faceswaplab_logging import save_img_debug
 from scripts.faceswaplab_ui.faceswaplab_unit_settings import FaceSwapUnitSettings
 from scripts.faceswaplab_utils.imgutils import (
-    pil_to_cv2,
-    check_against_nsfw,
     base64_to_pil,
 )
 from scripts.faceswaplab_utils.models_utils import get_current_model
 from modules.shared import opts
 
 
-def encode_to_base64(image):
+def encode_to_base64(image: Union[str, Image.Image, np.ndarray]) -> str:
-    if type(image) is str:
+    """
+    Encode an image to a base64 string.
+
+    The image can be a file path (str), a PIL Image, or a NumPy array.
+
+    Args:
+        image (Union[str, Image.Image, np.ndarray]): The image to encode.
+
+    Returns:
+        str: The base64-encoded image if successful, otherwise an empty string.
+    """
+    if isinstance(image, str):
         return image
-    elif type(image) is Image.Image:
+    elif isinstance(image, Image.Image):
         return api.encode_pil_to_base64(image)
-    elif type(image) is np.ndarray:
+    elif isinstance(image, np.ndarray):
         return encode_np_to_base64(image)
     else:
         return ""
 
 
-def encode_np_to_base64(image):
+def encode_np_to_base64(image: np.ndarray) -> str:
+    """
+    Encode a NumPy array to a base64 string.
+
+    The array is first converted to a PIL Image, then encoded.
+
+    Args:
+        image (np.ndarray): The NumPy array to encode.
+
+    Returns:
+        str: The base64-encoded image.
+    """
     pil = Image.fromarray(image)
     return api.encode_pil_to_base64(pil)
 
 
-def faceswaplab_api(_: gr.Blocks, app: FastAPI):
+def faceswaplab_api(_: gr.Blocks, app: FastAPI) -> None:
     @app.get(
         "/faceswaplab/version",
         tags=["faceswaplab"],
         description="Get faceswaplab version",
     )
-    async def version():
+    async def version() -> Dict[str, str]:
         return {"version": VERSION_FLAG}
 
     # use post as we consider the method non idempotent (which is debatable)

scripts/faceswaplab_api/faceswaplab_api_types.py

@@ -1,16 +1,8 @@
-from scripts.faceswaplab_swapping import swapper
-import numpy as np
 from typing import List, Tuple
-import dill as pickle
-import gradio as gr
-from insightface.app.common import Face
 from PIL import Image
 from scripts.faceswaplab_utils.imgutils import (
-    pil_to_cv2,
-    check_against_nsfw,
     base64_to_pil,
 )
-from scripts.faceswaplab_utils.faceswaplab_logging import logger
 from pydantic import BaseModel, Field
 from scripts.faceswaplab_postprocessing.postprocessing_options import InpaintingWhen
 

scripts/faceswaplab_globals.py

@@ -1,10 +1,11 @@
-from scripts.faceswaplab_utils.faceswaplab_logging import logger
 import os
 
 MODELS_DIR = os.path.abspath(os.path.join("models", "faceswaplab"))
 ANALYZER_DIR = os.path.abspath(os.path.join(MODELS_DIR, "analysers"))
 FACE_PARSER_DIR = os.path.abspath(os.path.join(MODELS_DIR, "parser"))
 
-VERSION_FLAG = "v1.1.0"
+VERSION_FLAG: str = "v1.1.0"
 EXTENSION_PATH = os.path.join("extensions", "sd-webui-faceswaplab")
-NSFW_SCORE = 0.7
+
+# The NSFW score threshold. If any part of the image has a score greater than this threshold, the image will be considered NSFW.
+NSFW_SCORE_THRESHOLD: float = 0.7

scripts/faceswaplab_postprocessing/i2i_pp.py

@@ -1,15 +1,11 @@
-from modules.face_restoration import FaceRestoration
-from modules.upscaler import UpscalerData
 from scripts.faceswaplab_utils.faceswaplab_logging import logger
 from PIL import Image
-import numpy as np
 from modules import shared
 from scripts.faceswaplab_utils import imgutils
-from modules import shared, processing, codeformer_model
+from modules import shared, processing
 from modules.processing import StableDiffusionProcessingImg2Img
 from scripts.faceswaplab_postprocessing.postprocessing_options import (
     PostProcessingOptions,
-    InpaintingWhen,
 )
 from modules import sd_models
 

scripts/faceswaplab_postprocessing/postprocessing.py

@@ -1,4 +1,3 @@
-from modules.face_restoration import FaceRestoration
 from scripts.faceswaplab_utils.faceswaplab_logging import logger
 from PIL import Image
 from scripts.faceswaplab_postprocessing.postprocessing_options import (

scripts/faceswaplab_postprocessing/upscaling.py

@@ -1,11 +1,10 @@
 from scripts.faceswaplab_postprocessing.postprocessing_options import (
     PostProcessingOptions,
-    InpaintingWhen,
 )
 from scripts.faceswaplab_utils.faceswaplab_logging import logger
 from PIL import Image
 import numpy as np
-from modules import shared, processing, codeformer_model
+from modules import codeformer_model
 
 
 def upscale_img(image: Image.Image, pp_options: PostProcessingOptions) -> Image.Image:

scripts/faceswaplab_settings/faceswaplab_settings.py

@@ -3,7 +3,7 @@ from modules import script_callbacks, shared
 import gradio as gr
 
 
-def on_ui_settings():
+def on_ui_settings() -> None:
     section = ("faceswaplab", "FaceSwapLab")
     models = get_models()
     shared.opts.add_option(

scripts/faceswaplab_swapping/parsing/__init__.py

@@ -40,7 +40,6 @@ please contact the contributor(s) of the work.
 
 
 import torch
-import cv2
 import os
 import torch
 from torch.hub import download_url_to_file, get_dir

scripts/faceswaplab_swapping/swapper.py

@@ -1,7 +1,7 @@
 import copy
 import os
 from dataclasses import dataclass
-from typing import Dict, List, Set, Tuple, Optional, Union
+from typing import Any, Dict, List, Set, Tuple, Optional
 
 import cv2
 import insightface
@@ -13,7 +13,6 @@ from sklearn.metrics.pairwise import cosine_similarity
 
 from scripts.faceswaplab_swapping import upscaled_inswapper
 from scripts.faceswaplab_utils.imgutils import (
-    cv2_to_pil,
     pil_to_cv2,
     check_against_nsfw,
 )
@@ -26,7 +25,7 @@ from scripts.faceswaplab_ui.faceswaplab_unit_settings import FaceSwapUnitSetting
 providers = ["CPUExecutionProvider"]
 
 
-def cosine_similarity_face(face1, face2) -> float:
+def cosine_similarity_face(face1: Face, face2: Face) -> float:
     """
     Calculates the cosine similarity between two face embeddings.
 
@@ -92,7 +91,7 @@ class FaceModelException(Exception):
 
 
 @lru_cache(maxsize=1)
-def getAnalysisModel():
+def getAnalysisModel() -> insightface.app.FaceAnalysis:
     """
     Retrieves the analysis model for face analysis.
 
@@ -112,11 +111,11 @@ def getAnalysisModel():
         logger.error(
             "Loading of swapping model failed, please check the requirements (On Windows, download and install Visual Studio. During the install, make sure to include the Python and C++ packages.)"
         )
-        raise FaceModelException("Loading of swapping model failed")
+        raise FaceModelException("Loading of analysis model failed")
 
 
 @lru_cache(maxsize=1)
-def getFaceSwapModel(model_path: str):
+def getFaceSwapModel(model_path: str) -> upscaled_inswapper.UpscaledINSwapper:
     """
     Retrieves the face swap model and initializes it if necessary.
 
@@ -135,13 +134,14 @@ def getFaceSwapModel(model_path: str):
         logger.error(
             "Loading of swapping model failed, please check the requirements (On Windows, download and install Visual Studio. During the install, make sure to include the Python and C++ packages.)"
         )
+        raise FaceModelException("Loading of swapping model failed")
 
 
 def get_faces(
-    img_data: np.ndarray,
+    img_data: np.ndarray,  # type: ignore
-    det_size=(640, 640),
+    det_size: Tuple[int, int] = (640, 640),
-    det_thresh: Optional[int] = None,
+    det_thresh: Optional[float] = None,
-    sort_by_face_size=False,
+    sort_by_face_size: bool = False,
 ) -> List[Face]:
     """
     Detects and retrieves faces from an image using an analysis model.
@@ -211,7 +211,7 @@ class ImageResult:
     """
 
 
-def get_or_default(l, index, default):
+def get_or_default(l: List[Any], index: int, default: Any) -> Any:
     """
     Retrieve the value at the specified index from the given list.
     If the index is out of bounds, return the default value instead.
@@ -227,7 +227,10 @@ def get_or_default(l, index, default):
     return l[index] if index < len(l) else default
 
 
-def get_faces_from_img_files(files):
+import gradio as gr
+
+
+def get_faces_from_img_files(files: List[gr.File]) -> List[Optional[np.ndarray]]:  # type: ignore
     """
     Extracts faces from a list of image files.
 
@@ -300,15 +303,15 @@ def blend_faces(faces: List[Face]) -> Face:
 
 
 def swap_face(
-    reference_face: np.ndarray,
+    reference_face: np.ndarray,  # type: ignore
-    source_face: np.ndarray,
+    source_face: np.ndarray,  # type: ignore
     target_img: Image.Image,
     model: str,
     faces_index: Set[int] = {0},
-    same_gender=True,
+    same_gender: bool = True,
-    upscaled_swapper=False,
+    upscaled_swapper: bool = False,
-    compute_similarity=True,
+    compute_similarity: bool = True,
-    sort_by_face_size=False,
+    sort_by_face_size: bool = False,
 ) -> ImageResult:
     """
     Swaps faces in the target image with the source face.
@@ -344,6 +347,7 @@ def swap_face(
             for i, swapped_face in enumerate(target_faces):
                 logger.info(f"swap face {i}")
                 if i in faces_index:
+                    # type : ignore
                     result = face_swapper.get(
                         result, swapped_face, source_face, upscale=upscaled_swapper
                     )
@@ -385,13 +389,13 @@ def swap_face(
 
 
 def process_image_unit(
-    model,
+    model: str,
     unit: FaceSwapUnitSettings,
     image: Image.Image,
-    info=None,
+    info: str = None,
-    upscaled_swapper=False,
+    upscaled_swapper: bool = False,
-    force_blend=False,
+    force_blend: bool = False,
-) -> List:
+) -> List[Tuple[Image.Image, str]]:
     """Process one image and return a List of (image, info) (one if blended, many if not).
 
     Args:
@@ -472,12 +476,12 @@ def process_image_unit(
 
 
 def process_images_units(
-    model,
+    model: str,
     units: List[FaceSwapUnitSettings],
     images: List[Tuple[Optional[Image.Image], Optional[str]]],
-    upscaled_swapper=False,
+    upscaled_swapper: bool = False,
-    force_blend=False,
+    force_blend: bool = False,
-) -> Union[List, None]:
+) -> Optional[List[Tuple[Image.Image, str]]]:
     if len(units) == 0:
         logger.info("Finished processing image, return %s images", len(images))
         return None

scripts/faceswaplab_swapping/upscaled_inswapper.py

@@ -1,17 +1,11 @@
 import cv2
 import numpy as np
-import onnx
-import onnxruntime
 from insightface.model_zoo.inswapper import INSwapper
 from insightface.utils import face_align
-from modules import codeformer_model, processing, scripts, shared
+from modules import processing, shared
-from modules.face_restoration import FaceRestoration
+from modules.shared import opts
-from modules.shared import cmd_opts, opts, state
 from modules.upscaler import UpscalerData
-from onnx import numpy_helper
-from PIL import Image
 
-from scripts.faceswaplab_utils.faceswaplab_logging import logger
 from scripts.faceswaplab_postprocessing import upscaling
 from scripts.faceswaplab_postprocessing.postprocessing_options import (
     PostProcessingOptions,

scripts/faceswaplab_ui/faceswaplab_tab.py

@@ -5,13 +5,12 @@ from pprint import pformat, pprint
 import dill as pickle
 import gradio as gr
 import modules.scripts as scripts
-import numpy as np
 import onnx
 import pandas as pd
 from scripts.faceswaplab_ui.faceswaplab_unit_ui import faceswap_unit_ui
 from scripts.faceswaplab_ui.faceswaplab_upscaler_ui import upscaler_ui
 from insightface.app.common import Face
-from modules import script_callbacks, scripts
+from modules import scripts
 from PIL import Image
 from modules.shared import opts
 
@@ -25,12 +24,13 @@ from scripts.faceswaplab_postprocessing.postprocessing_options import (
 )
 from scripts.faceswaplab_postprocessing.postprocessing import enhance_image
 from dataclasses import fields
-from typing import List
+from typing import Any, List, Optional, Union
 from scripts.faceswaplab_ui.faceswaplab_unit_settings import FaceSwapUnitSettings
 from scripts.faceswaplab_utils.models_utils import get_current_model
+import re
 
 
-def compare(img1, img2):
+def compare(img1: Image.Image, img2: Image.Image) -> Union[float, str]:
     if img1 is not None and img2 is not None:
         return swapper.compare_faces(img1, img2)
 
@@ -40,19 +40,10 @@ def compare(img1, img2):
 def extract_faces(
     files,
     extract_path,
-    face_restorer_name,
+    *components: List[gr.components.Component],
-    face_restorer_visibility,
-    codeformer_weight,
-    upscaler_name,
-    upscaler_scale,
-    upscaler_visibility,
-    inpainting_denoising_strengh,
-    inpainting_prompt,
-    inpainting_negative_prompt,
-    inpainting_steps,
-    inpainting_sampler,
-    inpainting_when,
 ):
+    postprocess_options = PostProcessingOptions(*components)  # type: ignore
+
     if not extract_path:
         tempfile.mkdtemp()
     if files is not None:
@@ -66,24 +57,16 @@ def extract_faces(
                     bbox = face.bbox.astype(int)
                     x_min, y_min, x_max, y_max = bbox
                     face_image = img.crop((x_min, y_min, x_max, y_max))
-                    if face_restorer_name or face_restorer_visibility:
+                    if (
-                        scale = 1 if face_image.width > 512 else 512 // face_image.width
+                        postprocess_options.face_restorer_name
+                        or postprocess_options.restorer_visibility
+                    ):
+                        postprocess_options.scale = (
+                            1 if face_image.width > 512 else 512 // face_image.width
+                        )
                         face_image = enhance_image(
                             face_image,
-                            PostProcessingOptions(
+                            postprocess_options,
-                                face_restorer_name=face_restorer_name,
-                                restorer_visibility=face_restorer_visibility,
-                                codeformer_weight=codeformer_weight,
-                                upscaler_name=upscaler_name,
-                                upscale_visibility=upscaler_visibility,
-                                scale=scale,
-                                inpainting_denoising_strengh=inpainting_denoising_strengh,
-                                inpainting_prompt=inpainting_prompt,
-                                inpainting_steps=inpainting_steps,
-                                inpainting_negative_prompt=inpainting_negative_prompt,
-                                inpainting_when=inpainting_when,
-                                inpainting_sampler=inpainting_sampler,
-                            ),
                         )
                     path = tempfile.NamedTemporaryFile(
                         delete=False, suffix=".png", dir=extract_path
@@ -95,7 +78,7 @@ def extract_faces(
     return None
 
 
-def analyse_faces(image, det_threshold=0.5):
+def analyse_faces(image: Image.Image, det_threshold: float = 0.5) -> str:
     try:
         faces = swapper.get_faces(imgutils.pil_to_cv2(image), det_thresh=det_threshold)
         result = ""
@@ -110,27 +93,40 @@ def analyse_faces(image, det_threshold=0.5):
         return "Analysis Failed"
 
 
-def build_face_checkpoint_and_save(batch_files, name):
+def sanitize_name(name: str) -> str:
+    logger.debug(f"Sanitize name {name}")
+    name = re.sub("[^A-Za-z0-9_. ]+", "", name)
+    name = name.replace(" ", "_")
+    logger.debug(f"Sanitized name {name[:255]}")
+    return name[:255]
+
+
+def build_face_checkpoint_and_save(
+    batch_files: gr.File, name: str
+) -> Optional[Image.Image]:
     """
-    Builds a face checkpoint, swaps faces, and saves the result to a file.
+    Builds a face checkpoint using the provided image files, performs face swapping,
+    and saves the result to a file. If a blended face is successfully obtained and the face swapping
+    process succeeds, the resulting image is returned. Otherwise, None is returned.
 
     Args:
-        batch_files (list): List of image file paths.
+        batch_files (list): List of image file paths used to create the face checkpoint.
-        name (str): Name of the face checkpoint
+        name (str): The name assigned to the face checkpoint.
 
     Returns:
-        PIL.Image.Image or None: Resulting swapped face image if successful, otherwise None.
+        PIL.Image.Image or None: The resulting swapped face image if the process is successful; None otherwise.
     """
+    name = sanitize_name(name)
     batch_files = batch_files or []
-    print("Build", name, [x.name for x in batch_files])
+    logger.info("Build %s %s", name, [x.name for x in batch_files])
     faces = swapper.get_faces_from_img_files(batch_files)
     blended_face = swapper.blend_faces(faces)
     preview_path = os.path.join(
         scripts.basedir(), "extensions", "sd-webui-faceswaplab", "references"
     )
     faces_path = os.path.join(scripts.basedir(), "models", "faceswaplab", "faces")
-    if not os.path.exists(faces_path):
+
-        os.makedirs(faces_path)
+    os.makedirs(faces_path, exist_ok=True)
 
     target_img = None
     if blended_face:
@@ -208,7 +204,9 @@ def explore_onnx_faceswap_model(model_path):
     return df
 
 
-def batch_process(files, save_path, *components):
+def batch_process(
+    files, save_path, *components: List[gr.components.Component]
+) -> Optional[List[Image.Image]]:
     try:
         if save_path is not None:
             os.makedirs(save_path, exist_ok=True)
@@ -228,7 +226,7 @@ def batch_process(files, save_path, *components):
         len_conf: int = len(fields(FaceSwapUnitSettings))
         shift: int = units_count * len_conf
         postprocess_options = PostProcessingOptions(
-            *components[shift : shift + len(fields(PostProcessingOptions))]
+            *components[shift : shift + len(fields(PostProcessingOptions))]  # type: ignore
         )
         logger.debug("%s", pformat(postprocess_options))
 
@@ -247,7 +245,7 @@ def batch_process(files, save_path, *components):
                     ),
                 )
                 if len(swapped_images) > 0:
-                    current_images += [img for img, info in swapped_images]
+                    current_images += [img for img, _ in swapped_images]
 
                 logger.info("%s images generated", len(current_images))
                 for i, img in enumerate(current_images):
@@ -269,7 +267,7 @@ def batch_process(files, save_path, *components):
     return None
 
 
-def tools_ui():
+def tools_ui() -> None:
     models = get_models()
     with gr.Tab("Tools"):
         with gr.Tab("Build"):
@@ -431,7 +429,7 @@ def tools_ui():
     )
 
 
-def on_ui_tabs():
+def on_ui_tabs() -> List[Any]:
     with gr.Blocks(analytics_enabled=False) as ui_faceswap:
         tools_ui()
     return [(ui_faceswap, "FaceSwapLab", "faceswaplab_tab")]

scripts/faceswaplab_ui/faceswaplab_unit_settings.py

@@ -8,7 +8,7 @@ import dill as pickle
 import gradio as gr
 from insightface.app.common import Face
 from PIL import Image
-from scripts.faceswaplab_utils.imgutils import pil_to_cv2, check_against_nsfw
+from scripts.faceswaplab_utils.imgutils import pil_to_cv2
 from scripts.faceswaplab_utils.faceswaplab_logging import logger
 
 

scripts/faceswaplab_ui/faceswaplab_unit_ui.py

@@ -1,8 +1,11 @@
+from typing import List
 from scripts.faceswaplab_utils.models_utils import get_face_checkpoints
 import gradio as gr
 
 
-def faceswap_unit_ui(is_img2img, unit_num=1, id_prefix="faceswaplab"):
+def faceswap_unit_ui(
+    is_img2img: bool, unit_num: int = 1, id_prefix: str = "faceswaplab"
+) -> List[gr.components.Component]:
     with gr.Tab(f"Face {unit_num}"):
         with gr.Column():
             gr.Markdown(
@@ -37,7 +40,7 @@ def faceswap_unit_ui(is_img2img, unit_num=1, id_prefix="faceswaplab"):
                     elem_id=f"{id_prefix}_face{unit_num}_refresh_checkpoints",
                 )
 
-                def refresh_fn(selected):
+                def refresh_fn(selected: str) -> None:
                     return gr.Dropdown.update(
                         value=selected, choices=get_face_checkpoints()
                     )

scripts/faceswaplab_ui/faceswaplab_upscaler_ui.py

@@ -1,13 +1,12 @@
+from typing import List
 import gradio as gr
 import modules
 from modules import shared, sd_models
-from modules.shared import cmd_opts, opts, state
+from modules.shared import opts
+from scripts.faceswaplab_postprocessing.postprocessing_options import InpaintingWhen
 
-import scripts.faceswaplab_postprocessing.upscaling as upscaling
-from scripts.faceswaplab_utils.faceswaplab_logging import logger
 
-
+def upscaler_ui() -> List[gr.components.Component]:
-def upscaler_ui():
     with gr.Tab(f"Post-Processing"):
         gr.Markdown(
             """Upscaling is performed on the whole image. Upscaling happens before face restoration."""
@@ -74,10 +73,8 @@ def upscaler_ui():
             )
             inpainting_when = gr.Dropdown(
                 elem_id="faceswaplab_pp_inpainting_when",
-                choices=[
+                choices=[e.value for e in InpaintingWhen.__members__.values()],
-                    e.value for e in upscaling.InpaintingWhen.__members__.values()
+                value=[InpaintingWhen.BEFORE_RESTORE_FACE.value],
-                ],
-                value=[upscaling.InpaintingWhen.BEFORE_RESTORE_FACE.value],
                 label="Enable/When",
             )
             inpainting_denoising_strength = gr.Slider(

scripts/faceswaplab_utils/faceswaplab_logging.py

@@ -1,12 +1,24 @@
 import logging
 import copy
 import sys
+from typing import Any
 from modules import shared
 from PIL import Image
+from logging import LogRecord
 
 
 class ColoredFormatter(logging.Formatter):
-    COLORS = {
+    """
+    A custom logging formatter that outputs logs with level names colored.
+
+    Class Attributes:
+        COLORS (dict): A dictionary mapping logging level names to their corresponding color codes.
+
+    Inherits From:
+        logging.Formatter
+    """
+
+    COLORS: dict[str, str] = {
         "DEBUG": "\033[0;36m",  # CYAN
         "INFO": "\033[0;32m",  # GREEN
         "WARNING": "\033[0;33m",  # YELLOW
@@ -15,7 +27,21 @@ class ColoredFormatter(logging.Formatter):
         "RESET": "\033[0m",  # RESET COLOR
     }
 
-    def format(self, record):
+    def format(self, record: LogRecord) -> str:
+        """
+        Format the specified record as text.
+
+        The record's attribute dictionary is used as the operand to a string
+        formatting operation which yields the returned string. Before formatting
+        the dictionary, a check is made to see if the format uses the levelname
+        of the record. If it does, a colorized version is created and used.
+
+        Args:
+            record (LogRecord): The log record to be formatted.
+
+        Returns:
+            str: The formatted string which includes the colorized levelname.
+        """
         colored_record = copy.copy(record)
         levelname = colored_record.levelname
         seq = self.COLORS.get(levelname, self.COLORS["RESET"])
@@ -46,7 +72,24 @@ if logger.getEffectiveLevel() <= logging.DEBUG:
     DEBUG_DIR = tempfile.mkdtemp()
 
 
-def save_img_debug(img: Image.Image, message: str, *opts):
+def save_img_debug(img: Image.Image, message: str, *opts: Any) -> None:
+    """
+    Saves an image to a temporary file if the logger's effective level is set to DEBUG or lower.
+    After saving, it logs a debug message along with the file URI of the image.
+
+    Parameters
+    ----------
+    img : Image.Image
+        The image to be saved.
+    message : str
+        The message to be logged.
+    *opts : Any
+        Additional arguments to be passed to the logger's debug method.
+
+    Returns
+    -------
+    None
+    """
     if logger.getEffectiveLevel() <= logging.DEBUG:
         with tempfile.NamedTemporaryFile(
             dir=DEBUG_DIR, delete=False, suffix=".png"

scripts/faceswaplab_utils/imgutils.py

@@ -1,35 +1,76 @@
 import io
-from typing import Optional
+from typing import List, Optional, Tuple, Union, Dict
-from PIL import Image, ImageChops, ImageOps, ImageFilter
+from PIL import Image
 import cv2
 import numpy as np
 from math import isqrt, ceil
 import torch
 from ifnude import detect
-from scripts.faceswaplab_globals import NSFW_SCORE
+from scripts.faceswaplab_globals import NSFW_SCORE_THRESHOLD
 from modules import processing
 import base64
+from collections import Counter
+
+
+def check_against_nsfw(img: Image.Image) -> bool:
+    """
+    Check if an image exceeds the Not Safe for Work (NSFW) score.
+
+    Parameters:
+    img (PIL.Image.Image): The image to be checked.
+
+    Returns:
+    bool: True if any part of the image is considered NSFW, False otherwise.
+    """
 
+    shapes: List[bool] = []
+    chunks: List[Dict[str, Union[int, float]]] = detect(img)
 
-def check_against_nsfw(img):
-    shapes = []
-    chunks = detect(img)
     for chunk in chunks:
-        shapes.append(chunk["score"] > NSFW_SCORE)
+        shapes.append(chunk["score"] > NSFW_SCORE_THRESHOLD)
+
     return any(shapes)
 
 
-def pil_to_cv2(pil_img):
+def pil_to_cv2(pil_img: Image.Image) -> np.ndarray:  # type: ignore
+    """
+    Convert a PIL Image into an OpenCV image (cv2).
+
+    Args:
+        pil_img (PIL.Image.Image): An image in PIL format.
+
+    Returns:
+        np.ndarray: The input image converted to OpenCV format (BGR).
+    """
     return cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR)
 
 
-def cv2_to_pil(cv2_img):
+def cv2_to_pil(cv2_img: np.ndarray) -> Image.Image:  # type: ignore
+    """
+    Convert an OpenCV image (cv2) into a PIL Image.
+
+    Args:
+        cv2_img (np.ndarray): An image in OpenCV format (BGR).
+
+    Returns:
+        PIL.Image.Image: The input image converted to PIL format (RGB).
+    """
     return Image.fromarray(cv2.cvtColor(cv2_img, cv2.COLOR_BGR2RGB))
 
 
-def torch_to_pil(images):
+def torch_to_pil(images: torch.Tensor) -> List[Image.Image]:
     """
-    Convert a numpy image or a batch of images to a PIL image.
+    Converts a tensor image or a batch of tensor images to a PIL image or a list of PIL images.
+
+    Parameters
+    ----------
+    images : torch.Tensor
+        A tensor representing an image or a batch of images.
+
+    Returns
+    -------
+    list
+        A list of PIL images.
     """
     images = images.cpu().permute(0, 2, 3, 1).numpy()
     if images.ndim == 3:
@@ -39,9 +80,19 @@ def torch_to_pil(images):
     return pil_images
 
 
-def pil_to_torch(pil_images):
+def pil_to_torch(pil_images: Union[Image.Image, List[Image.Image]]) -> torch.Tensor:
     """
-    Convert a PIL image or a list of PIL images to a torch tensor or a batch of torch tensors.
+    Converts a PIL image or a list of PIL images to a torch tensor or a batch of torch tensors.
+
+    Parameters
+    ----------
+    pil_images : Union[Image.Image, List[Image.Image]]
+        A PIL image or a list of PIL images.
+
+    Returns
+    -------
+    torch.Tensor
+        A tensor representing an image or a batch of images.
     """
     if isinstance(pil_images, list):
         numpy_images = [np.array(image) for image in pil_images]
@@ -53,10 +104,7 @@ def pil_to_torch(pil_images):
     return torch_image
 
 
-from collections import Counter
+def create_square_image(image_list: List[Image.Image]) -> Optional[Image.Image]:
-
-
-def create_square_image(image_list):
     """
     Creates a square image by combining multiple images in a grid pattern.
 
@@ -108,16 +156,41 @@ def create_square_image(image_list):
     return None
 
 
-def create_mask(image, box_coords):
+# def create_mask(image : Image.Image, box_coords : Tuple[int, int, int, int]) -> Image.Image:
+#     width, height = image.size
+#     mask = Image.new("L", (width, height), 255)
+#     x1, y1, x2, y2 = box_coords
+#     for x in range(width):
+#         for y in range(height):
+#             if x1 <= x <= x2 and y1 <= y <= y2:
+#                 mask.putpixel((x, y), 255)
+#             else:
+#                 mask.putpixel((x, y), 0)
+#     return mask
+
+
+def create_mask(
+    image: Image.Image, box_coords: Tuple[int, int, int, int]
+) -> Image.Image:
+    """
+    Create a binary mask for a given image and bounding box coordinates.
+
+    Args:
+        image (PIL.Image.Image): The input image.
+        box_coords (Tuple[int, int, int, int]): A tuple of 4 integers defining the bounding box.
+        It follows the pattern (x1, y1, x2, y2), where (x1, y1) is the top-left coordinate of the
+        box and (x2, y2) is the bottom-right coordinate of the box.
+
+    Returns:
+        PIL.Image.Image: A binary mask of the same size as the input image, where pixels within
+        the bounding box are white (255) and pixels outside the bounding box are black (0).
+    """
     width, height = image.size
-    mask = Image.new("L", (width, height), 255)
+    mask = Image.new("L", (width, height), 0)
     x1, y1, x2, y2 = box_coords
-    for x in range(width):
+    for x in range(x1, x2 + 1):
-        for y in range(height):
+        for y in range(y1, y2 + 1):
-            if x1 <= x <= x2 and y1 <= y <= y2:
+            mask.putpixel((x, y), 255)
-                mask.putpixel((x, y), 255)
-            else:
-                mask.putpixel((x, y), 0)
     return mask
 
 
@@ -185,12 +258,32 @@ def prepare_mask(
 
 
 def base64_to_pil(base64str: Optional[str]) -> Optional[Image.Image]:
+    """
+    Converts a base64 string to a PIL Image object.
+
+    Parameters:
+    base64str (Optional[str]): The base64 string to convert. This string may contain a data URL scheme
+    (i.e., 'data:image/jpeg;base64,') or just be the raw base64 encoded data. If None, the function
+    will return None.
+
+    Returns:
+    Optional[Image.Image]: A PIL Image object created from the base64 string. If the input is None,
+    the function returns None.
+
+    Raises:
+    binascii.Error: If the base64 string is not properly formatted or encoded.
+    PIL.UnidentifiedImageError: If the image format cannot be identified.
+    """
+
     if base64str is None:
         return None
-    if "base64," in base64str:  # check if the base64 string has a data URL scheme
+
+    # Check if the base64 string has a data URL scheme
+    if "base64," in base64str:
         base64_data = base64str.split("base64,")[-1]
         img_bytes = base64.b64decode(base64_data)
     else:
-        # if no data URL scheme, just decode
+        # If no data URL scheme, just decode
         img_bytes = base64.b64decode(base64str)
+
     return Image.open(io.BytesIO(img_bytes))

scripts/faceswaplab_utils/models_utils.py

@@ -1,5 +1,6 @@
 import glob
 import os
+from typing import List
 import modules.scripts as scripts
 from modules import scripts
 from scripts.faceswaplab_globals import EXTENSION_PATH
@@ -7,7 +8,7 @@ from modules.shared import opts
 from scripts.faceswaplab_utils.faceswaplab_logging import logger
 
 
-def get_models():
+def get_models() -> List[str]:
     """
     Retrieve a list of swap model files.
 
@@ -44,7 +45,7 @@ def get_current_model() -> str:
     return model
 
 
-def get_face_checkpoints():
+def get_face_checkpoints() -> List[str]:
     """
     Retrieve a list of face checkpoint paths.