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Merge pull request #46 from glucauze/v1.2.2

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V1.2.2 : Install speed fix and nsfw filter option
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35
README.md
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@ -14,7 +14,7 @@ While FaceSwapLab is still under development, it has reached a good level of sta
In short: In short:
+ **Ethical Guideline:** This extension should not be forked to create a public, easy way to bypass NSFW filtering. If you modify it for this purpose, keep it private, or you'll be banned. + **Ethical Guideline:** NSFW is now configurable due to performance issue. Please don't use this to do harm.
+ **License:** This software is distributed under the terms of the GNU Affero General Public License (AGPL), version 3 or later. + **License:** This software is distributed under the terms of the GNU Affero General Public License (AGPL), version 3 or later.
+ **Model License:** This software uses InsightFace's pre-trained models, which are available for non-commercial research purposes only. + **Model License:** This software uses InsightFace's pre-trained models, which are available for non-commercial research purposes only.
@ -24,6 +24,35 @@ More on this here : https://glucauze.github.io/sd-webui-faceswaplab/
+ Older versions of gradio don't work well with the extension. See this bug : https://github.com/glucauze/sd-webui-faceswaplab/issues/5 + Older versions of gradio don't work well with the extension. See this bug : https://github.com/glucauze/sd-webui-faceswaplab/issues/5
## Quick Start
### Simple
1. Put a face in the reference.
2. Select a face number.
3. Select "Enable."
4. Select "CodeFormer" in **Global Post-Processing** tab.
Once you're happy with some results but want to improve, the next steps are to:
+ Use advanced settings in face units (which are not as complex as they might seem, it's basically fine tuning post-processing for each faces).
+ Use pre/post inpainting to tweak the image a bit for more natural results.
### Better
1. Put a face in the reference.
2. Select a face number.
3. Select "Enable."
4. In **Post-Processing** accordeon:
+ Select "CodeFormer"
+ Select "LDSR" or a faster model "003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN" in upscaler. See [here for a list of upscalers](https://github.com/glucauze/sd-webui-faceswaplab/discussions/29).
+ Use sharpen, color_correction and improved mask
5. Disable "CodeFormer" in **Global Post-Processing** tab (otherwise it will be applied twice)
Don't hesitate to share config in the [discussion section](https://github.com/glucauze/sd-webui-faceswaplab/discussions).
### Features ### Features
+ **Face Unit Concept**: Similar to controlNet, the program introduces the concept of a face unit. You can configure up to 10 units (3 units are the default setting) in the program settings (sd). + **Face Unit Concept**: Similar to controlNet, the program introduces the concept of a face unit. You can configure up to 10 units (3 units are the default setting) in the program settings (sd).
@ -32,6 +61,8 @@ More on this here : https://glucauze.github.io/sd-webui-faceswaplab/
+ **Batch Processing** + **Batch Processing**
+ **GPU**
+ **Inpainting Fixes** : supports “only masked” and mask inpainting. + **Inpainting Fixes** : supports “only masked” and mask inpainting.
+ **Performance Improvements**: The overall performance of the software has been enhanced. + **Performance Improvements**: The overall performance of the software has been enhanced.
@ -62,7 +93,7 @@ More on this here : https://glucauze.github.io/sd-webui-faceswaplab/
+ **Upscaled Inswapper**: The program now includes an upscaled inswapper option, which improves results by incorporating upsampling, sharpness adjustment, and color correction before face is merged to the original image. + **Upscaled Inswapper**: The program now includes an upscaled inswapper option, which improves results by incorporating upsampling, sharpness adjustment, and color correction before face is merged to the original image.
+ **API with typing support** : + **API with typing support**
## Installation ## Installation

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

15
docs/documentation.markdown
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@ -17,6 +17,21 @@ Once you're happy with some results but want to improve, the next steps are to:
+ Use advanced settings in face units (which are not as complex as they might seem, it's basically fine tuning post-processing for each faces). + Use advanced settings in face units (which are not as complex as they might seem, it's basically fine tuning post-processing for each faces).
+ Use pre/post inpainting to tweak the image a bit for more natural results. + Use pre/post inpainting to tweak the image a bit for more natural results.
### Getting better results
1. Put a face in the reference.
2. Select a face number.
3. Select "Enable."
4. In **Post-Processing** accordeon:
+ Select "CodeFormer"
+ Select "LDSR" or a faster model "003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN" in upscaler. See [here for a list of upscalers](https://github.com/glucauze/sd-webui-faceswaplab/discussions/29).
+ Use sharpen, color_correction and improved mask
5. Disable "CodeFormer" in **Global Post-Processing** tab (otherwise it will be applied twice)
Don't hesitate to share config in the [discussion section](https://github.com/glucauze/sd-webui-faceswaplab/discussions).
## Main Interface ## Main Interface
Here is the interface for FaceSwap Lab. It is available in the form of an accordion in both img2img and txt2img. Here is the interface for FaceSwap Lab. It is available in the form of an accordion in both img2img and txt2img.

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docs/index.markdown
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@ -20,7 +20,7 @@ While FaceSwapLab is still under development, it has reached a good level of sta
In short: In short:
+ **Ethical Guideline:** This extension should not be forked to create a public, easy way to circumvent NSFW filtering. + **Ethical Guideline:** This extension is **not intended to facilitate the creation of not safe for work (NSFW) or non-consensual deepfake content**. Its purpose is to bring consistency to image creation, making it easier to repair existing images, or bring characters back to life.
+ **License:** This software is distributed under the terms of the GNU Affero General Public License (AGPL), version 3 or later. + **License:** This software is distributed under the terms of the GNU Affero General Public License (AGPL), version 3 or later.
+ **Model License:** This software uses InsightFace's pre-trained models, which are available for non-commercial research purposes only. + **Model License:** This software uses InsightFace's pre-trained models, which are available for non-commercial research purposes only.
@ -28,8 +28,6 @@ In short:
This extension is **not intended to facilitate the creation of not safe for work (NSFW) or non-consensual deepfake content**. Its purpose is to bring consistency to image creation, making it easier to repair existing images, or bring characters back to life. This extension is **not intended to facilitate the creation of not safe for work (NSFW) or non-consensual deepfake content**. Its purpose is to bring consistency to image creation, making it easier to repair existing images, or bring characters back to life.
While the code for this extension is licensed under the AGPL in compliance with models and other source materials, it's important to stress that **we strongly discourage any attempts to fork this project to create an uncensored version**. Any modifications to the code to enable the production of such content would be contrary to the ethical guidelines we advocate for.
We will comply with European regulations regarding this type of software. As required by law, the code may include both visible and invisible watermarks. If your local laws prohibit the use of this extension, you should not use it. We will comply with European regulations regarding this type of software. As required by law, the code may include both visible and invisible watermarks. If your local laws prohibit the use of this extension, you should not use it.
From an ethical perspective, the main goal of this extension is to generate consistent images by swapping faces. It's important to note that we've done our best to integrate censorship features. However, when users can access the source code, they might bypass these censorship measures. That's why we urge users to use this extension responsibly and avoid any malicious use. We emphasize the importance of respecting people's privacy and consent when swapping faces in images. We discourage any activities that could harm others, invade their privacy, or negatively affect their well-being. From an ethical perspective, the main goal of this extension is to generate consistent images by swapping faces. It's important to note that we've done our best to integrate censorship features. However, when users can access the source code, they might bypass these censorship measures. That's why we urge users to use this extension responsibly and avoid any malicious use. We emphasize the importance of respecting people's privacy and consent when swapping faces in images. We discourage any activities that could harm others, invade their privacy, or negatively affect their well-being.

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install.py
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@ -7,15 +7,15 @@ from packaging.version import parse
def check_install() -> None: def check_install() -> None:
use_gpu = getattr( use_gpu = not getattr(shared.cmd_opts, "use-cpu", False)
shared.cmd_opts, "faceswaplab_gpu", False
) or shared.opts.data.get("faceswaplab_use_gpu", False)
if use_gpu and sys.platform != "darwin": if use_gpu and sys.platform != "darwin":
print("Faceswaplab : Use GPU requirements")
req_file = os.path.join( req_file = os.path.join(
os.path.dirname(os.path.realpath(__file__)), "requirements-gpu.txt" os.path.dirname(os.path.realpath(__file__)), "requirements-gpu.txt"
) )
else: else:
print("Faceswaplab : Use CPU requirements")
req_file = os.path.join( req_file = os.path.join(
os.path.dirname(os.path.realpath(__file__)), "requirements.txt" os.path.dirname(os.path.realpath(__file__)), "requirements.txt"
) )
@ -36,6 +36,8 @@ def check_install() -> None:
required_version = parse(package.split(">=")[1]) required_version = parse(package.split(">=")[1])
return installed_version >= required_version return installed_version >= required_version
else: else:
if package_name == "opencv-python":
return launch.is_installed(package_name) or launch.is_installed("cv2")
return launch.is_installed(package_name) return launch.is_installed(package_name)
print("Checking faceswaplab requirements") print("Checking faceswaplab requirements")
@ -54,9 +56,12 @@ def check_install() -> None:
except Exception as e: except Exception as e:
print(e) print(e)
print( print(
f"Warning: Failed to install {package}, faceswaplab will not work." f"Warning: Failed to install {package}, faceswaplab may not work. Try to restart server or install dependencies manually."
) )
raise e raise e
check_install() import timeit
check_time = timeit.timeit(check_install, number=1)
print(check_time)

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models.json
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@ -0,0 +1 @@
[{"analyzerName":"intellisense-members-lstm-pylance","languageName":"python","identity":{"modelId":"E61945A9A512ED5E1A3EE3F1A2365B88F8FE","outputId":"E4E9EADA96734F01970E616FAB2FAC19","modifiedTimeUtc":"2020-08-11T14:06:50.811Z"},"filePath":"E61945A9A512ED5E1A3EE3F1A2365B88F8FE_E4E9EADA96734F01970E616FAB2FAC19","lastAccessTimeUtc":"2023-08-14T21:58:14.988Z"}]

1
requirements-gpu.txt
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@ -3,6 +3,7 @@ dill
ifnude ifnude
insightface==0.7.3 insightface==0.7.3
onnx>=1.14.0 onnx>=1.14.0
protobuf>=3.20.2
opencv-python opencv-python
pandas pandas
pydantic pydantic

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requirements.txt
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@ -1,3 +1,4 @@
protobuf>=3.20.2
cython cython
dill dill
ifnude ifnude

46
scripts/configure.py
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@ -1,51 +1,15 @@
import os import os
from tqdm import tqdm from tqdm import tqdm
import traceback
import urllib.request import urllib.request
from scripts.faceswaplab_utils.faceswaplab_logging import logger from scripts.faceswaplab_utils.faceswaplab_logging import logger
from scripts.faceswaplab_globals import * from scripts.faceswaplab_globals import *
from packaging import version from packaging import version
import pkg_resources import pkg_resources
import hashlib from scripts.faceswaplab_utils.models_utils import check_model
ALREADY_DONE = False ALREADY_DONE = False
def check_install() -> None:
# Very ugly hack :( due to sdnext optimization not calling install.py every time if git log has not changed
import importlib.util
import sys
import os
current_dir = os.path.dirname(os.path.realpath(__file__))
check_install_path = os.path.join(current_dir, "..", "install.py")
spec = importlib.util.spec_from_file_location("check_install", check_install_path)
check_install = importlib.util.module_from_spec(spec)
sys.modules["check_install"] = check_install
spec.loader.exec_module(check_install)
check_install.check_install() # type: ignore
#### End of ugly hack :( !
def is_sha1_matching(file_path: str, expected_sha1: str) -> bool:
sha1_hash = hashlib.sha1(usedforsecurity=False)
try:
with open(file_path, "rb") as file:
for byte_block in iter(lambda: file.read(4096), b""):
sha1_hash.update(byte_block)
if sha1_hash.hexdigest() == expected_sha1:
return True
else:
return False
except Exception as e:
logger.error(
"Failed to check model hash, check the model is valid or has been downloaded adequately : %e",
e,
)
traceback.print_exc()
return False
def check_configuration() -> None: def check_configuration() -> None:
global ALREADY_DONE global ALREADY_DONE
@ -83,13 +47,7 @@ def check_configuration() -> None:
if not os.path.exists(model_path): if not os.path.exists(model_path):
download(model_url, model_path) download(model_url, model_path)
check_model()
if not is_sha1_matching(model_path, EXPECTED_INSWAPPER_SHA1):
logger.error(
"Suspicious sha1 for model %s, check the model is valid or has been downloaded adequately. Should be %s",
model_path,
EXPECTED_INSWAPPER_SHA1,
)
gradio_version = pkg_resources.get_distribution("gradio").version gradio_version = pkg_resources.get_distribution("gradio").version

23
scripts/faceswaplab.py
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@ -1,4 +1,5 @@
from scripts.configure import check_configuration from scripts.configure import check_configuration
from scripts.faceswaplab_utils.sd_utils import get_sd_option
check_configuration() check_configuration()
@ -12,7 +13,7 @@ from scripts.faceswaplab_settings import faceswaplab_settings
from scripts.faceswaplab_swapping import swapper from scripts.faceswaplab_swapping import swapper
from scripts.faceswaplab_ui import faceswaplab_tab, faceswaplab_unit_ui from scripts.faceswaplab_ui import faceswaplab_tab, faceswaplab_unit_ui
from scripts.faceswaplab_utils import faceswaplab_logging, imgutils, models_utils from scripts.faceswaplab_utils import faceswaplab_logging, imgutils, models_utils
from scripts.faceswaplab_utils.models_utils import get_current_model from scripts.faceswaplab_utils.models_utils import get_current_swap_model
from scripts.faceswaplab_utils.typing import * from scripts.faceswaplab_utils.typing import *
from scripts.faceswaplab_utils.ui_utils import dataclasses_from_flat_list from scripts.faceswaplab_utils.ui_utils import dataclasses_from_flat_list
from scripts.faceswaplab_utils.faceswaplab_logging import logger, save_img_debug from scripts.faceswaplab_utils.faceswaplab_logging import logger, save_img_debug
@ -76,7 +77,7 @@ class FaceSwapScript(scripts.Script):
@property @property
def units_count(self) -> int: def units_count(self) -> int:
return opts.data.get("faceswaplab_units_count", 3) return get_sd_option("faceswaplab_units_count", 3)
@property @property
def enabled(self) -> bool: def enabled(self) -> bool:
@ -85,7 +86,7 @@ class FaceSwapScript(scripts.Script):
@property @property
def keep_original_images(self) -> bool: def keep_original_images(self) -> bool:
return opts.data.get("faceswaplab_keep_original", False) return get_sd_option("faceswaplab_keep_original", False)
@property @property
def swap_in_generated_units(self) -> List[FaceSwapUnitSettings]: def swap_in_generated_units(self) -> List[FaceSwapUnitSettings]:
@ -99,7 +100,7 @@ class FaceSwapScript(scripts.Script):
return f"faceswaplab" return f"faceswaplab"
def show(self, is_img2img: bool) -> bool: def show(self, is_img2img: bool) -> bool:
return scripts.AlwaysVisible return scripts.AlwaysVisible # type: ignore
def ui(self, is_img2img: bool) -> List[gr.components.Component]: def ui(self, is_img2img: bool) -> List[gr.components.Component]:
with gr.Accordion(f"FaceSwapLab {VERSION_FLAG}", open=False): with gr.Accordion(f"FaceSwapLab {VERSION_FLAG}", open=False):
@ -147,7 +148,7 @@ class FaceSwapScript(scripts.Script):
(img, None) for img in p.init_images (img, None) for img in p.init_images
] ]
new_inits = swapper.process_images_units( new_inits = swapper.process_images_units(
get_current_model(), get_current_swap_model(),
self.swap_in_source_units, self.swap_in_source_units,
images=init_images, images=init_images,
force_blend=True, force_blend=True,
@ -181,7 +182,7 @@ class FaceSwapScript(scripts.Script):
for i, (img, info) in enumerate(zip(orig_images, orig_infotexts)): for i, (img, info) in enumerate(zip(orig_images, orig_infotexts)):
batch_index = i % p.batch_size batch_index = i % p.batch_size
swapped_images = swapper.process_images_units( swapped_images = swapper.process_images_units(
get_current_model(), get_current_swap_model(),
self.swap_in_generated_units, self.swap_in_generated_units,
images=[(img, info)], images=[(img, info)],
) )
@ -213,8 +214,8 @@ class FaceSwapScript(scripts.Script):
swp_img, swp_img,
p.outpath_samples, p.outpath_samples,
"", "",
p.all_seeds[batch_index], p.all_seeds[batch_index], # type: ignore
p.all_prompts[batch_index], p.all_prompts[batch_index], # type: ignore
opts.samples_format, opts.samples_format,
info=new_info, info=new_info,
p=p, p=p,
@ -231,7 +232,7 @@ class FaceSwapScript(scripts.Script):
text = processed.infotexts[0] text = processed.infotexts[0]
infotexts.insert(0, text) infotexts.insert(0, text)
if opts.enable_pnginfo: if opts.enable_pnginfo:
grid.info["parameters"] = text grid.info["parameters"] = text # type: ignore
images.insert(0, grid) images.insert(0, grid)
if opts.grid_save: if opts.grid_save:
@ -239,8 +240,8 @@ class FaceSwapScript(scripts.Script):
grid, grid,
p.outpath_grids, p.outpath_grids,
"swapped-grid", "swapped-grid",
p.all_seeds[0], p.all_seeds[0], # type: ignore
p.all_prompts[0], p.all_prompts[0], # type: ignore
opts.grid_format, opts.grid_format,
info=text, info=text,
short_filename=not opts.grid_extended_filename, short_filename=not opts.grid_extended_filename,

5
scripts/faceswaplab_api/faceswaplab_api.py
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@ -18,9 +18,10 @@ from scripts.faceswaplab_postprocessing.postprocessing_options import (
PostProcessingOptions, PostProcessingOptions,
) )
from client_api import api_utils from client_api import api_utils
from scripts.faceswaplab_utils.face_checkpoints_utils import ( from scripts.faceswaplab_swapping.face_checkpoints import (
build_face_checkpoint_and_save, build_face_checkpoint_and_save,
) )
from scripts.faceswaplab_utils.typing import PILImage
def encode_to_base64(image: Union[str, Image.Image, np.ndarray]) -> str: # type: ignore def encode_to_base64(image: Union[str, Image.Image, np.ndarray]) -> str: # type: ignore
@ -99,7 +100,7 @@ def faceswaplab_api(_: gr.Blocks, app: FastAPI) -> None:
pp_options = None pp_options = None
units = get_faceswap_units_settings(request.units) units = get_faceswap_units_settings(request.units)
swapped_images = swapper.batch_process( swapped_images: Optional[List[PILImage]] = swapper.batch_process(
[src_image], None, units=units, postprocess_options=pp_options [src_image], None, units=units, postprocess_options=pp_options
) )

12
scripts/faceswaplab_globals.py
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@ -1,18 +1,24 @@
import os import os
from modules import scripts from modules import scripts
# Defining the absolute path for the 'faceswaplab' directory inside 'models' directory
MODELS_DIR = os.path.abspath(os.path.join("models", "faceswaplab")) MODELS_DIR = os.path.abspath(os.path.join("models", "faceswaplab"))
# Defining the absolute path for the 'analysers' directory inside 'MODELS_DIR'
ANALYZER_DIR = os.path.abspath(os.path.join(MODELS_DIR, "analysers")) ANALYZER_DIR = os.path.abspath(os.path.join(MODELS_DIR, "analysers"))
# Defining the absolute path for the 'parser' directory inside 'MODELS_DIR'
FACE_PARSER_DIR = os.path.abspath(os.path.join(MODELS_DIR, "parser")) FACE_PARSER_DIR = os.path.abspath(os.path.join(MODELS_DIR, "parser"))
# Defining the absolute path for the 'faces' directory inside 'MODELS_DIR'
FACES_DIR = os.path.abspath(os.path.join(MODELS_DIR, "faces")) FACES_DIR = os.path.abspath(os.path.join(MODELS_DIR, "faces"))
# Constructing the path for 'references' directory inside the 'extensions' and 'sd-webui-faceswaplab' directories, based on the base directory of scripts
REFERENCE_PATH = os.path.join( REFERENCE_PATH = os.path.join(
scripts.basedir(), "extensions", "sd-webui-faceswaplab", "references" scripts.basedir(), "extensions", "sd-webui-faceswaplab", "references"
) )
VERSION_FLAG: str = "v1.2.1" # Defining the version flag for the application
VERSION_FLAG: str = "v1.2.2"
# Defining the path for 'sd-webui-faceswaplab' inside the 'extensions' directory
EXTENSION_PATH = os.path.join("extensions", "sd-webui-faceswaplab") EXTENSION_PATH = os.path.join("extensions", "sd-webui-faceswaplab")
# The NSFW score threshold. If any part of the image has a score greater than this threshold, the image will be considered NSFW. # Defining the expected SHA1 hash value for 'INSWAPPER'
NSFW_SCORE_THRESHOLD: float = 0.7
EXPECTED_INSWAPPER_SHA1 = "17a64851eaefd55ea597ee41e5c18409754244c5" EXPECTED_INSWAPPER_SHA1 = "17a64851eaefd55ea597ee41e5c18409754244c5"

6
scripts/faceswaplab_inpainting/faceswaplab_inpainting.py
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@ -1,5 +1,5 @@
from dataclasses import dataclass from dataclasses import dataclass
from typing import List from typing import List, Optional
import gradio as gr import gradio as gr
from client_api import api_utils from client_api import api_utils
@ -15,10 +15,10 @@ class InpaintingOptions:
@staticmethod @staticmethod
def from_gradio(components: List[gr.components.Component]) -> "InpaintingOptions": def from_gradio(components: List[gr.components.Component]) -> "InpaintingOptions":
return InpaintingOptions(*components) return InpaintingOptions(*components) # type: ignore
@staticmethod @staticmethod
def from_api_dto(dto: api_utils.InpaintingOptions) -> "InpaintingOptions": def from_api_dto(dto: Optional[api_utils.InpaintingOptions]) -> "InpaintingOptions":
""" """
Converts a InpaintingOptions object from an API DTO (Data Transfer Object). Converts a InpaintingOptions object from an API DTO (Data Transfer Object).

7
scripts/faceswaplab_postprocessing/postprocessing_options.py
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@ -1,3 +1,4 @@
from typing import Optional
from modules.face_restoration import FaceRestoration from modules.face_restoration import FaceRestoration
from modules.upscaler import UpscalerData from modules.upscaler import UpscalerData
from dataclasses import dataclass from dataclasses import dataclass
@ -27,17 +28,17 @@ class PostProcessingOptions:
inpainting_when: InpaintingWhen = InpaintingWhen.BEFORE_UPSCALING inpainting_when: InpaintingWhen = InpaintingWhen.BEFORE_UPSCALING
# (Don't use optional for this or gradio parsing will fail) : # (Don't use optional for this or gradio parsing will fail) :
inpainting_options: InpaintingOptions = None inpainting_options: InpaintingOptions = None # type: ignore
@property @property
def upscaler(self) -> UpscalerData: def upscaler(self) -> Optional[UpscalerData]:
for upscaler in shared.sd_upscalers: for upscaler in shared.sd_upscalers:
if upscaler.name == self.upscaler_name: if upscaler.name == self.upscaler_name:
return upscaler return upscaler
return None return None
@property @property
def face_restorer(self) -> FaceRestoration: def face_restorer(self) -> Optional[FaceRestoration]:
for face_restorer in shared.face_restorers: for face_restorer in shared.face_restorers:
if face_restorer.name() == self.face_restorer_name: if face_restorer.name() == self.face_restorer_name:
return face_restorer return face_restorer

2
scripts/faceswaplab_postprocessing/upscaling.py
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@ -17,7 +17,7 @@ def upscale_img(image: PILImage, pp_options: PostProcessingOptions) -> PILImage:
pp_options.scale, pp_options.scale,
) )
result_image = pp_options.upscaler.scaler.upscale( result_image = pp_options.upscaler.scaler.upscale(
image, pp_options.scale, pp_options.upscaler.data_path image, pp_options.scale, pp_options.upscaler.data_path # type: ignore
) )
# FIXME : Could be better (managing images whose dimensions are not multiples of 16) # FIXME : Could be better (managing images whose dimensions are not multiples of 16)

14
scripts/faceswaplab_settings/faceswaplab_settings.py
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@ -1,11 +1,11 @@
from scripts.faceswaplab_utils.models_utils import get_models from scripts.faceswaplab_utils.models_utils import get_swap_models
from modules import script_callbacks, shared from modules import script_callbacks, shared
import gradio as gr import gradio as gr
def on_ui_settings() -> None: def on_ui_settings() -> None:
section = ("faceswaplab", "FaceSwapLab") section = ("faceswaplab", "FaceSwapLab")
models = get_models() models = get_swap_models()
shared.opts.add_option( shared.opts.add_option(
"faceswaplab_model", "faceswaplab_model",
shared.OptionInfo( shared.OptionInfo(
@ -46,6 +46,16 @@ def on_ui_settings() -> None:
section=section, section=section,
), ),
) )
shared.opts.add_option(
"faceswaplab_nsfw_threshold",
shared.OptionInfo(
0.7,
"NSFW score threshold. Any image part with a score above this value will be treated as NSFW (use extension responsibly !). 1=Disable filtering",
gr.Slider,
{"minimum": 0, "maximum": 1, "step": 0.01},
section=section,
),
)
shared.opts.add_option( shared.opts.add_option(
"faceswaplab_det_size", "faceswaplab_det_size",

83
scripts/faceswaplab_utils/face_checkpoints_utils.py → scripts/faceswaplab_swapping/face_checkpoints.py
Unescape Escape View File

@ -11,7 +11,7 @@ from scripts.faceswaplab_swapping.upcaled_inswapper_options import InswappperOpt
from scripts.faceswaplab_utils.faceswaplab_logging import logger from scripts.faceswaplab_utils.faceswaplab_logging import logger
from scripts.faceswaplab_utils.typing import * from scripts.faceswaplab_utils.typing import *
from scripts.faceswaplab_utils import imgutils from scripts.faceswaplab_utils import imgutils
from scripts.faceswaplab_utils.models_utils import get_models from scripts.faceswaplab_utils.models_utils import get_swap_models
import traceback import traceback
import dill as pickle # will be removed in future versions import dill as pickle # will be removed in future versions
@ -38,8 +38,11 @@ def sanitize_name(name: str) -> str:
def build_face_checkpoint_and_save( def build_face_checkpoint_and_save(
images: List[PILImage], name: str, overwrite: bool = False, path: str = None images: List[PILImage],
) -> PILImage: name: str,
overwrite: bool = False,
path: Optional[str] = None,
) -> Optional[PILImage]:
""" """
Builds a face checkpoint using the provided image files, performs face swapping, 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 and saves the result to a file. If a blended face is successfully obtained and the face swapping
@ -57,13 +60,17 @@ def build_face_checkpoint_and_save(
name = sanitize_name(name) name = sanitize_name(name)
images = images or [] images = images or []
logger.info("Build %s with %s images", name, len(images)) logger.info("Build %s with %s images", name, len(images))
faces = swapper.get_faces_from_img_files(images) faces: List[Face] = swapper.get_faces_from_img_files(images=images)
blended_face = swapper.blend_faces(faces) if faces is None or len(faces) == 0:
logger.error("No source faces found")
return None
blended_face: Optional[Face] = swapper.blend_faces(faces)
preview_path = os.path.join( preview_path = os.path.join(
scripts.basedir(), "extensions", "sd-webui-faceswaplab", "references" scripts.basedir(), "extensions", "sd-webui-faceswaplab", "references"
) )
reference_preview_img: PILImage = None reference_preview_img: PILImage
if blended_face: if blended_face:
if blended_face["gender"] == 0: if blended_face["gender"] == 0:
reference_preview_img = Image.open( reference_preview_img = Image.open(
@ -85,41 +92,51 @@ def build_face_checkpoint_and_save(
"Failed to open reference image, cannot create preview : That should not happen unless you deleted the references folder or change the detection threshold." "Failed to open reference image, cannot create preview : That should not happen unless you deleted the references folder or change the detection threshold."
) )
else: else:
result = swapper.swap_face( result: swapper.ImageResult = swapper.swap_face(
reference_face=blended_face,
target_faces=[target_face], target_faces=[target_face],
source_face=blended_face, source_face=blended_face,
target_img=reference_preview_img, target_img=reference_preview_img,
model=get_models()[0], model=get_swap_models()[0],
swapping_options=InswappperOptions(face_restorer_name="Codeformer"), swapping_options=InswappperOptions(
face_restorer_name="CodeFormer",
restorer_visibility=1,
upscaler_name="Lanczos",
codeformer_weight=1,
improved_mask=True,
color_corrections=False,
sharpen=True,
),
) )
preview_image = result.image preview_image = result.image
if path: if path:
file_path = path file_path = path
else: else:
file_path = os.path.join(get_checkpoint_path(), f"{name}.safetensors") file_path = os.path.join(
if not overwrite: get_checkpoint_path(), f"{name}.safetensors"
file_number = 1 )
while os.path.exists(file_path): if not overwrite:
file_path = os.path.join( file_number = 1
get_checkpoint_path(), f"{name}_{file_number}.safetensors" while os.path.exists(file_path):
) file_path = os.path.join(
file_number += 1 get_checkpoint_path(),
save_face(filename=file_path, face=blended_face) f"{name}_{file_number}.safetensors",
preview_image.save(file_path + ".png") )
try: file_number += 1
data = load_face(file_path) save_face(filename=file_path, face=blended_face)
logger.debug(data) preview_image.save(file_path + ".png")
except Exception as e: try:
logger.error("Error loading checkpoint, after creation %s", e) data = load_face(file_path)
traceback.print_exc() logger.debug(data)
except Exception as e:
return preview_image logger.error("Error loading checkpoint, after creation %s", e)
traceback.print_exc()
return preview_image
else: else:
logger.error("No face found") logger.error("No face found")
return None return None # type: ignore
except Exception as e: except Exception as e:
logger.error("Failed to build checkpoint %s", e) logger.error("Failed to build checkpoint %s", e)
traceback.print_exc() traceback.print_exc()
@ -140,7 +157,7 @@ def save_face(face: Face, filename: str) -> None:
raise e raise e
def load_face(name: str) -> Face: def load_face(name: str) -> Optional[Face]:
if name.startswith("data:application/face;base64,"): if name.startswith("data:application/face;base64,"):
with tempfile.NamedTemporaryFile(delete=True) as temp_file: with tempfile.NamedTemporaryFile(delete=True) as temp_file:
api_utils.base64_to_safetensors(name, temp_file.name) api_utils.base64_to_safetensors(name, temp_file.name)

2
scripts/faceswaplab_swapping/facemask.py
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@ -83,7 +83,7 @@ def generate_face_mask(face_image: np.ndarray, device: torch.device) -> np.ndarr
convert_bgr_to_rgb=True, convert_bgr_to_rgb=True,
use_float32=True, use_float32=True,
) )
normalize(face_input, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) normalize(face_input, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) # type: ignore
assert isinstance(face_input, torch.Tensor) assert isinstance(face_input, torch.Tensor)
face_input = torch.unsqueeze(face_input, 0).to(device) face_input = torch.unsqueeze(face_input, 0).to(device)

77
scripts/faceswaplab_swapping/swapper.py
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@ -26,24 +26,24 @@ from scripts.faceswaplab_utils.imgutils import (
) )
from scripts.faceswaplab_utils.faceswaplab_logging import logger, save_img_debug from scripts.faceswaplab_utils.faceswaplab_logging import logger, save_img_debug
from scripts import faceswaplab_globals from scripts import faceswaplab_globals
from modules.shared import opts
from functools import lru_cache from functools import lru_cache
from scripts.faceswaplab_ui.faceswaplab_unit_settings import FaceSwapUnitSettings from scripts.faceswaplab_ui.faceswaplab_unit_settings import FaceSwapUnitSettings
from scripts.faceswaplab_postprocessing.postprocessing import enhance_image from scripts.faceswaplab_postprocessing.postprocessing import enhance_image
from scripts.faceswaplab_postprocessing.postprocessing_options import ( from scripts.faceswaplab_postprocessing.postprocessing_options import (
PostProcessingOptions, PostProcessingOptions,
) )
from scripts.faceswaplab_utils.models_utils import get_current_model from scripts.faceswaplab_utils.models_utils import get_current_swap_model
from scripts.faceswaplab_utils.typing import CV2ImgU8, PILImage, Face from scripts.faceswaplab_utils.typing import CV2ImgU8, PILImage, Face
from scripts.faceswaplab_inpainting.i2i_pp import img2img_diffusion from scripts.faceswaplab_inpainting.i2i_pp import img2img_diffusion
from modules import shared from modules import shared
import onnxruntime import onnxruntime
from scripts.faceswaplab_utils.sd_utils import get_sd_option
def use_gpu() -> bool: def use_gpu() -> bool:
return ( return (
getattr(shared.cmd_opts, "faceswaplab_gpu", False) getattr(shared.cmd_opts, "faceswaplab_gpu", False)
or opts.data.get("faceswaplab_use_gpu", False) or get_sd_option("faceswaplab_use_gpu", False)
) and sys.platform != "darwin" ) and sys.platform != "darwin"
@ -51,7 +51,7 @@ def use_gpu() -> bool:
def force_install_gpu_providers() -> None: def force_install_gpu_providers() -> None:
# Ugly Ugly hack due to SDNEXT : # Ugly Ugly hack due to SDNEXT :
try: try:
from scripts.configure import check_install from scripts.faceswaplab_utils.install_utils import check_install
logger.warning("Try to reinstall gpu dependencies") logger.warning("Try to reinstall gpu dependencies")
check_install() check_install()
@ -101,8 +101,9 @@ def cosine_similarity_face(face1: Face, face2: Face) -> float:
non-negative similarity score. non-negative similarity score.
""" """
# Reshape the face embeddings to have a shape of (1, -1) # Reshape the face embeddings to have a shape of (1, -1)
vec1 = face1.embedding.reshape(1, -1) assert face1.normed_embedding is not None and face2.normed_embedding is not None
vec2 = face2.embedding.reshape(1, -1) vec1 = face1.normed_embedding.reshape(1, -1)
vec2 = face2.normed_embedding.reshape(1, -1)
# Calculate the cosine similarity between the reshaped embeddings # Calculate the cosine similarity between the reshaped embeddings
similarity = cosine_similarity(vec1, vec2) similarity = cosine_similarity(vec1, vec2)
@ -141,7 +142,7 @@ def batch_process(
src_images: List[Union[PILImage, str]], # image or filename src_images: List[Union[PILImage, str]], # image or filename
save_path: Optional[str], save_path: Optional[str],
units: List[FaceSwapUnitSettings], units: List[FaceSwapUnitSettings],
postprocess_options: PostProcessingOptions, postprocess_options: Optional[PostProcessingOptions],
) -> Optional[List[PILImage]]: ) -> Optional[List[PILImage]]:
""" """
Process a batch of images, apply face swapping according to the given settings, and optionally save the resulting images to a specified path. Process a batch of images, apply face swapping according to the given settings, and optionally save the resulting images to a specified path.
@ -166,6 +167,7 @@ def batch_process(
if src_images is not None and len(units) > 0: if src_images is not None and len(units) > 0:
result_images = [] result_images = []
for src_image in src_images: for src_image in src_images:
path: str = ""
if isinstance(src_image, str): if isinstance(src_image, str):
if save_path: if save_path:
path = os.path.join( path = os.path.join(
@ -180,9 +182,9 @@ def batch_process(
current_images = [] current_images = []
swapped_images = process_images_units( swapped_images = process_images_units(
get_current_model(), images=[(src_image, None)], units=units get_current_swap_model(), images=[(src_image, None)], units=units
) )
if len(swapped_images) > 0: if swapped_images and len(swapped_images) > 0:
current_images += [img for img, _ in swapped_images] current_images += [img for img, _ in swapped_images]
logger.info("%s images generated", len(current_images)) logger.info("%s images generated", len(current_images))
@ -209,7 +211,7 @@ def extract_faces(
images: List[PILImage], images: List[PILImage],
extract_path: Optional[str], extract_path: Optional[str],
postprocess_options: PostProcessingOptions, postprocess_options: PostProcessingOptions,
) -> Optional[List[str]]: ) -> Optional[List[PILImage]]:
""" """
Extracts faces from a list of image files. Extracts faces from a list of image files.
@ -232,14 +234,14 @@ def extract_faces(
os.makedirs(extract_path, exist_ok=True) os.makedirs(extract_path, exist_ok=True)
if images: if images:
result_images = [] result_images: list[PILImage] = []
for img in images: for img in images:
faces = get_faces(pil_to_cv2(img)) faces = get_faces(pil_to_cv2(img))
if faces: if faces:
face_images = [] face_images = []
for face in faces: for face in faces:
bbox = face.bbox.astype(int) bbox = face.bbox.astype(int) # type: ignore
x_min, y_min, x_max, y_max = bbox x_min, y_min, x_max, y_max = bbox
face_image = img.crop((x_min, y_min, x_max, y_max)) face_image = img.crop((x_min, y_min, x_max, y_max))
@ -311,7 +313,9 @@ def capture_stdout() -> Generator[StringIO, None, None]:
@lru_cache(maxsize=3) @lru_cache(maxsize=3)
def getAnalysisModel( def getAnalysisModel(
det_size: Tuple[int, int] = (640, 640), det_thresh: float = 0.5 det_size: Tuple[int, int] = (640, 640),
det_thresh: float = 0.5,
use_gpu: bool = False,
) -> insightface.app.FaceAnalysis: ) -> insightface.app.FaceAnalysis:
""" """
Retrieves the analysis model for face analysis. Retrieves the analysis model for face analysis.
@ -355,7 +359,9 @@ def getAnalysisModel(
@lru_cache(maxsize=1) @lru_cache(maxsize=1)
def getFaceSwapModel(model_path: str) -> upscaled_inswapper.UpscaledINSwapper: def getFaceSwapModel(
model_path: str, use_gpu: bool = False
) -> upscaled_inswapper.UpscaledINSwapper:
""" """
Retrieves the face swap model and initializes it if necessary. Retrieves the face swap model and initializes it if necessary.
@ -370,7 +376,7 @@ def getFaceSwapModel(model_path: str) -> upscaled_inswapper.UpscaledINSwapper:
with tqdm(total=1, desc="Loading swap model", unit="model") as pbar: with tqdm(total=1, desc="Loading swap model", unit="model") as pbar:
with capture_stdout() as captured: with capture_stdout() as captured:
model = upscaled_inswapper.UpscaledINSwapper( model = upscaled_inswapper.UpscaledINSwapper(
insightface.model_zoo.get_model(model_path, providers=providers) insightface.model_zoo.get_model(model_path, providers=providers) # type: ignore
) )
pbar.update(1) pbar.update(1)
logger.info("%s", pformat(captured.getvalue())) logger.info("%s", pformat(captured.getvalue()))
@ -402,14 +408,16 @@ def get_faces(
""" """
if det_thresh is None: if det_thresh is None:
det_thresh = opts.data.get("faceswaplab_detection_threshold", 0.5) det_thresh = get_sd_option("faceswaplab_detection_threshold", 0.5)
auto_det_size = opts.data.get("faceswaplab_auto_det_size", True) auto_det_size = get_sd_option("faceswaplab_auto_det_size", True)
if not auto_det_size: if not auto_det_size:
x = opts.data.get("faceswaplab_det_size", 640) x = get_sd_option("faceswaplab_det_size", 640)
det_size = (x, x) det_size = (x, x)
face_analyser = getAnalysisModel(det_size, det_thresh) face_analyser = getAnalysisModel(
det_size=det_size, det_thresh=det_thresh, use_gpu=not is_cpu_provider()
)
# Get the detected faces from the image using the analysis model # Get the detected faces from the image using the analysis model
faces = face_analyser.get(img_data) faces = face_analyser.get(img_data)
@ -433,7 +441,7 @@ def get_faces(
try: try:
# Sort the detected faces based on their x-coordinate of the bounding box # Sort the detected faces based on their x-coordinate of the bounding box
return sorted(faces, key=lambda x: x.bbox[0]) return sorted(faces, key=lambda x: x.bbox[0]) # type: ignore
except Exception as e: except Exception as e:
logger.error("Failed to get faces %s", e) logger.error("Failed to get faces %s", e)
traceback.print_exc() traceback.print_exc()
@ -470,7 +478,7 @@ def filter_faces(
filtered_faces = sorted( filtered_faces = sorted(
all_faces, all_faces,
reverse=True, reverse=True,
key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]), key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]), # type: ignore
) )
if filtering_options.source_gender is not None: if filtering_options.source_gender is not None:
@ -526,7 +534,7 @@ def get_or_default(l: List[Any], index: int, default: Any) -> Any:
return l[index] if index < len(l) else default return l[index] if index < len(l) else default
def get_faces_from_img_files(images: List[PILImage]) -> List[Optional[CV2ImgU8]]: def get_faces_from_img_files(images: List[PILImage]) -> List[Face]:
""" """
Extracts faces from a list of image files. Extracts faces from a list of image files.
@ -538,7 +546,7 @@ def get_faces_from_img_files(images: List[PILImage]) -> List[Optional[CV2ImgU8]]
""" """
faces = [] faces: List[Face] = []
if len(images) > 0: if len(images) > 0:
for img in images: for img in images:
@ -566,7 +574,7 @@ def blend_faces(faces: List[Face]) -> Optional[Face]:
ValueError: If the embeddings have different shapes. ValueError: If the embeddings have different shapes.
""" """
embeddings = [face.embedding for face in faces] embeddings: list[Any] = [face.embedding for face in faces]
if len(embeddings) > 0: if len(embeddings) > 0:
embedding_shape = embeddings[0].shape embedding_shape = embeddings[0].shape
@ -592,19 +600,16 @@ def blend_faces(faces: List[Face]) -> Optional[Face]:
def swap_face( def swap_face(
reference_face: CV2ImgU8,
source_face: Face, source_face: Face,
target_img: PILImage, target_img: PILImage,
target_faces: List[Face], target_faces: List[Face],
model: str, model: str,
swapping_options: Optional[InswappperOptions], swapping_options: Optional[InswappperOptions],
compute_similarity: bool = True,
) -> ImageResult: ) -> ImageResult:
""" """
Swaps faces in the target image with the source face. Swaps faces in the target image with the source face.
Args: Args:
reference_face (CV2ImgU8): The reference face used for similarity comparison.
source_face (CV2ImgU8): The source face to be swapped. source_face (CV2ImgU8): The source face to be swapped.
target_img (PILImage): The target image to swap faces in. target_img (PILImage): The target image to swap faces in.
model (str): Path to the face swap model. model (str): Path to the face swap model.
@ -614,14 +619,16 @@ def swap_face(
""" """
return_result = ImageResult(target_img, {}, {}) return_result = ImageResult(target_img, {}, {})
target_img_cv2: CV2ImgU8 = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR) target_img_cv2: CV2ImgU8 = cv2.cvtColor(
np.array(target_img), cv2.COLOR_RGB2BGR
).astype("uint8")
try: try:
gender = source_face["gender"] gender = source_face["gender"]
logger.info("Source Gender %s", gender) logger.info("Source Gender %s", gender)
if source_face is not None: if source_face is not None:
result = target_img_cv2 result: CV2ImgU8 = target_img_cv2
model_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), model) model_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), model)
face_swapper = getFaceSwapModel(model_path) face_swapper = getFaceSwapModel(model_path, use_gpu=not is_cpu_provider())
logger.info("Target faces count : %s", len(target_faces)) logger.info("Target faces count : %s", len(target_faces))
for i, swapped_face in enumerate(target_faces): for i, swapped_face in enumerate(target_faces):
@ -679,9 +686,9 @@ def process_image_unit(
model: str, model: str,
unit: FaceSwapUnitSettings, unit: FaceSwapUnitSettings,
image: PILImage, image: PILImage,
info: str = None, info: Optional[str] = None,
force_blend: bool = False, force_blend: bool = False,
) -> List[Tuple[PILImage, str]]: ) -> List[Tuple[PILImage, Optional[str]]]:
"""Process one image and return a List of (image, info) (one if blended, many if not). """Process one image and return a List of (image, info) (one if blended, many if not).
Args: Args:
@ -722,7 +729,9 @@ def process_image_unit(
sort_by_face_size=unit.sort_by_size, sort_by_face_size=unit.sort_by_size,
) )
target_faces = filter_faces(faces, filtering_options=face_filtering_options) target_faces: List[Face] = filter_faces(
all_faces=faces, filtering_options=face_filtering_options
)
# Apply pre-inpainting to image # Apply pre-inpainting to image
if unit.pre_inpainting.inpainting_denoising_strengh > 0: if unit.pre_inpainting.inpainting_denoising_strengh > 0:
@ -732,13 +741,11 @@ def process_image_unit(
save_img_debug(image, "Before swap") save_img_debug(image, "Before swap")
result: ImageResult = swap_face( result: ImageResult = swap_face(
reference_face=reference_face,
source_face=src_face, source_face=src_face,
target_img=current_image, target_img=current_image,
target_faces=target_faces, target_faces=target_faces,
model=model, model=model,
swapping_options=unit.swapping_options, swapping_options=unit.swapping_options,
compute_similarity=unit.compute_similarity,
) )
# Apply post-inpainting to image # Apply post-inpainting to image
if unit.post_inpainting.inpainting_denoising_strengh > 0: if unit.post_inpainting.inpainting_denoising_strengh > 0:

7
scripts/faceswaplab_swapping/upcaled_inswapper_options.py
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@ -1,20 +1,21 @@
from dataclasses import * from dataclasses import *
from typing import Optional
from client_api import api_utils from client_api import api_utils
@dataclass @dataclass
class InswappperOptions: class InswappperOptions:
face_restorer_name: str = None face_restorer_name: Optional[str] = None
restorer_visibility: float = 1 restorer_visibility: float = 1
codeformer_weight: float = 1 codeformer_weight: float = 1
upscaler_name: str = None upscaler_name: Optional[str] = None
improved_mask: bool = False improved_mask: bool = False
color_corrections: bool = False color_corrections: bool = False
sharpen: bool = False sharpen: bool = False
erosion_factor: float = 1.0 erosion_factor: float = 1.0
@staticmethod @staticmethod
def from_api_dto(dto: api_utils.InswappperOptions) -> "InswappperOptions": def from_api_dto(dto: Optional[api_utils.InswappperOptions]) -> "InswappperOptions":
""" """
Converts a InpaintingOptions object from an API DTO (Data Transfer Object). Converts a InpaintingOptions object from an API DTO (Data Transfer Object).

23
scripts/faceswaplab_swapping/upscaled_inswapper.py
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@ -1,10 +1,9 @@
from typing import Any, Tuple, Union from typing import Any, Optional, Tuple, Union
import cv2 import cv2
import numpy as np import numpy as np
from insightface.model_zoo.inswapper import INSwapper from insightface.model_zoo.inswapper import INSwapper
from insightface.utils import face_align from insightface.utils import face_align
from modules import processing, shared from modules import processing, shared
from modules.shared import opts
from modules.upscaler import UpscalerData from modules.upscaler import UpscalerData
from scripts.faceswaplab_postprocessing import upscaling from scripts.faceswaplab_postprocessing import upscaling
@ -14,13 +13,14 @@ from scripts.faceswaplab_postprocessing.postprocessing_options import (
from scripts.faceswaplab_swapping.facemask import generate_face_mask from scripts.faceswaplab_swapping.facemask import generate_face_mask
from scripts.faceswaplab_swapping.upcaled_inswapper_options import InswappperOptions from scripts.faceswaplab_swapping.upcaled_inswapper_options import InswappperOptions
from scripts.faceswaplab_utils.imgutils import cv2_to_pil, pil_to_cv2 from scripts.faceswaplab_utils.imgutils import cv2_to_pil, pil_to_cv2
from scripts.faceswaplab_utils.sd_utils import get_sd_option
from scripts.faceswaplab_utils.typing import CV2ImgU8, Face from scripts.faceswaplab_utils.typing import CV2ImgU8, Face
from scripts.faceswaplab_utils.faceswaplab_logging import logger from scripts.faceswaplab_utils.faceswaplab_logging import logger
def get_upscaler() -> UpscalerData: def get_upscaler() -> Optional[UpscalerData]:
for upscaler in shared.sd_upscalers: for upscaler in shared.sd_upscalers:
if upscaler.name == opts.data.get( if upscaler.name == get_sd_option(
"faceswaplab_upscaled_swapper_upscaler", "LDSR" "faceswaplab_upscaled_swapper_upscaler", "LDSR"
): ):
return upscaler return upscaler
@ -130,8 +130,14 @@ class UpscaledINSwapper(INSwapper):
self.__dict__.update(inswapper.__dict__) self.__dict__.update(inswapper.__dict__)
def upscale_and_restore( def upscale_and_restore(
self, img: CV2ImgU8, k: int = 2, inswapper_options: InswappperOptions = None self,
img: CV2ImgU8,
k: int = 2,
inswapper_options: Optional[InswappperOptions] = None,
) -> CV2ImgU8: ) -> CV2ImgU8:
if inswapper_options is None:
return img
pil_img = cv2_to_pil(img) pil_img = cv2_to_pil(img)
pp_options = PostProcessingOptions( pp_options = PostProcessingOptions(
upscaler_name=inswapper_options.upscaler_name, upscaler_name=inswapper_options.upscaler_name,
@ -156,7 +162,7 @@ class UpscaledINSwapper(INSwapper):
target_face: Face, target_face: Face,
source_face: Face, source_face: Face,
paste_back: bool = True, paste_back: bool = True,
options: InswappperOptions = None, options: Optional[InswappperOptions] = None,
) -> Union[CV2ImgU8, Tuple[CV2ImgU8, Any]]: ) -> Union[CV2ImgU8, Tuple[CV2ImgU8, Any]]:
aimg, M = face_align.norm_crop2(img, target_face.kps, self.input_size[0]) aimg, M = face_align.norm_crop2(img, target_face.kps, self.input_size[0])
blob = cv2.dnn.blobFromImage( blob = cv2.dnn.blobFromImage(
@ -166,9 +172,10 @@ class UpscaledINSwapper(INSwapper):
(self.input_mean, self.input_mean, self.input_mean), (self.input_mean, self.input_mean, self.input_mean),
swapRB=True, swapRB=True,
) )
latent = source_face.normed_embedding.reshape((1, -1)) latent = source_face.normed_embedding.reshape((1, -1)) # type: ignore
latent = np.dot(latent, self.emap) latent = np.dot(latent, self.emap)
latent /= np.linalg.norm(latent) latent /= np.linalg.norm(latent)
assert self.session is not None
pred = self.session.run( pred = self.session.run(
self.output_names, {self.input_names[0]: blob, self.input_names[1]: latent} self.output_names, {self.input_names[0]: blob, self.input_names[1]: latent}
)[0] )[0]
@ -274,7 +281,7 @@ class UpscaledINSwapper(INSwapper):
mask_h = np.max(mask_h_inds) - np.min(mask_h_inds) mask_h = np.max(mask_h_inds) - np.min(mask_h_inds)
mask_w = np.max(mask_w_inds) - np.min(mask_w_inds) mask_w = np.max(mask_w_inds) - np.min(mask_w_inds)
mask_size = int(np.sqrt(mask_h * mask_w)) mask_size = int(np.sqrt(mask_h * mask_w))
erosion_factor = options.erosion_factor erosion_factor = options.erosion_factor if options else 1
k = max(int(mask_size // 10 * erosion_factor), int(10 * erosion_factor)) k = max(int(mask_size // 10 * erosion_factor), int(10 * erosion_factor))

6
scripts/faceswaplab_ui/faceswaplab_inpainting_ui.py
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@ -1,7 +1,7 @@
from typing import List from typing import List
import gradio as gr import gradio as gr
from modules.shared import opts
from modules import sd_models, sd_samplers from modules import sd_models, sd_samplers
from scripts.faceswaplab_utils.sd_utils import get_sd_option
def face_inpainting_ui( def face_inpainting_ui(
@ -19,14 +19,14 @@ def face_inpainting_ui(
) )
inpainting_denoising_prompt = gr.Textbox( inpainting_denoising_prompt = gr.Textbox(
opts.data.get( get_sd_option(
"faceswaplab_pp_default_inpainting_prompt", "Portrait of a [gender]" "faceswaplab_pp_default_inpainting_prompt", "Portrait of a [gender]"
), ),
elem_id=f"{id_prefix}_pp_inpainting_denoising_prompt", elem_id=f"{id_prefix}_pp_inpainting_denoising_prompt",
label="Inpainting prompt use [gender] instead of men or woman", label="Inpainting prompt use [gender] instead of men or woman",
) )
inpainting_denoising_negative_prompt = gr.Textbox( inpainting_denoising_negative_prompt = gr.Textbox(
opts.data.get( get_sd_option(
"faceswaplab_pp_default_inpainting_negative_prompt", "blurry" "faceswaplab_pp_default_inpainting_negative_prompt", "blurry"
), ),
elem_id=f"{id_prefix}_pp_inpainting_denoising_neg_prompt", elem_id=f"{id_prefix}_pp_inpainting_denoising_neg_prompt",

18
scripts/faceswaplab_ui/faceswaplab_postprocessing_ui.py
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@ -2,8 +2,8 @@ from typing import List
import gradio as gr import gradio as gr
import modules import modules
from modules import shared, sd_models from modules import shared, sd_models
from modules.shared import opts
from scripts.faceswaplab_postprocessing.postprocessing_options import InpaintingWhen from scripts.faceswaplab_postprocessing.postprocessing_options import InpaintingWhen
from scripts.faceswaplab_utils.sd_utils import get_sd_option
def postprocessing_ui() -> List[gr.components.Component]: def postprocessing_ui() -> List[gr.components.Component]:
@ -15,7 +15,7 @@ def postprocessing_ui() -> List[gr.components.Component]:
face_restorer_name = gr.Radio( face_restorer_name = gr.Radio(
label="Restore Face", label="Restore Face",
choices=["None"] + [x.name() for x in shared.face_restorers], choices=["None"] + [x.name() for x in shared.face_restorers],
value=lambda: opts.data.get( value=lambda: get_sd_option(
"faceswaplab_pp_default_face_restorer", "faceswaplab_pp_default_face_restorer",
shared.face_restorers[0].name(), shared.face_restorers[0].name(),
), ),
@ -26,7 +26,7 @@ def postprocessing_ui() -> List[gr.components.Component]:
face_restorer_visibility = gr.Slider( face_restorer_visibility = gr.Slider(
0, 0,
1, 1,
value=lambda: opts.data.get( value=lambda: get_sd_option(
"faceswaplab_pp_default_face_restorer_visibility", 1 "faceswaplab_pp_default_face_restorer_visibility", 1
), ),
step=0.001, step=0.001,
@ -36,7 +36,7 @@ def postprocessing_ui() -> List[gr.components.Component]:
codeformer_weight = gr.Slider( codeformer_weight = gr.Slider(
0, 0,
1, 1,
value=lambda: opts.data.get( value=lambda: get_sd_option(
"faceswaplab_pp_default_face_restorer_weight", 1 "faceswaplab_pp_default_face_restorer_weight", 1
), ),
step=0.001, step=0.001,
@ -45,7 +45,7 @@ def postprocessing_ui() -> List[gr.components.Component]:
) )
upscaler_name = gr.Dropdown( upscaler_name = gr.Dropdown(
choices=[upscaler.name for upscaler in shared.sd_upscalers], choices=[upscaler.name for upscaler in shared.sd_upscalers],
value=lambda: opts.data.get("faceswaplab_pp_default_upscaler", "None"), value=lambda: get_sd_option("faceswaplab_pp_default_upscaler", "None"),
label="Upscaler", label="Upscaler",
elem_id="faceswaplab_pp_upscaler", elem_id="faceswaplab_pp_upscaler",
) )
@ -60,7 +60,7 @@ def postprocessing_ui() -> List[gr.components.Component]:
upscaler_visibility = gr.Slider( upscaler_visibility = gr.Slider(
0, 0,
1, 1,
value=lambda: opts.data.get( value=lambda: get_sd_option(
"faceswaplab_pp_default_upscaler_visibility", 1 "faceswaplab_pp_default_upscaler_visibility", 1
), ),
step=0.1, step=0.1,
@ -87,21 +87,21 @@ def postprocessing_ui() -> List[gr.components.Component]:
) )
inpainting_denoising_prompt = gr.Textbox( inpainting_denoising_prompt = gr.Textbox(
opts.data.get( get_sd_option(
"faceswaplab_pp_default_inpainting_prompt", "Portrait of a [gender]" "faceswaplab_pp_default_inpainting_prompt", "Portrait of a [gender]"
), ),
elem_id="faceswaplab_pp_inpainting_denoising_prompt", elem_id="faceswaplab_pp_inpainting_denoising_prompt",
label="Inpainting prompt use [gender] instead of men or woman", label="Inpainting prompt use [gender] instead of men or woman",
) )
inpainting_denoising_negative_prompt = gr.Textbox( inpainting_denoising_negative_prompt = gr.Textbox(
opts.data.get( get_sd_option(
"faceswaplab_pp_default_inpainting_negative_prompt", "blurry" "faceswaplab_pp_default_inpainting_negative_prompt", "blurry"
), ),
elem_id="faceswaplab_pp_inpainting_denoising_neg_prompt", elem_id="faceswaplab_pp_inpainting_denoising_neg_prompt",
label="Inpainting negative prompt use [gender] instead of men or woman", label="Inpainting negative prompt use [gender] instead of men or woman",
) )
with gr.Row(): with gr.Row():
samplers_names = [s.name for s in modules.sd_samplers.all_samplers] samplers_names = [s.name for s in modules.sd_samplers.all_samplers] # type: ignore
inpainting_sampler = gr.Dropdown( inpainting_sampler = gr.Dropdown(
choices=samplers_names, choices=samplers_names,
value=[samplers_names[0]], value=[samplers_names[0]],

52
scripts/faceswaplab_ui/faceswaplab_tab.py
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@ -1,11 +1,12 @@
import traceback import traceback
from pprint import pformat from pprint import pformat
from typing import * from typing import *
from scripts.faceswaplab_swapping import face_checkpoints
from scripts.faceswaplab_utils.sd_utils import get_sd_option
from scripts.faceswaplab_utils.typing import * from scripts.faceswaplab_utils.typing import *
import gradio as gr import gradio as gr
import onnx import onnx
import pandas as pd import pandas as pd
from modules.shared import opts
from PIL import Image from PIL import Image
import scripts.faceswaplab_swapping.swapper as swapper import scripts.faceswaplab_swapping.swapper as swapper
@ -15,9 +16,9 @@ from scripts.faceswaplab_postprocessing.postprocessing_options import (
from scripts.faceswaplab_ui.faceswaplab_postprocessing_ui import postprocessing_ui from scripts.faceswaplab_ui.faceswaplab_postprocessing_ui import postprocessing_ui
from scripts.faceswaplab_ui.faceswaplab_unit_settings import FaceSwapUnitSettings from scripts.faceswaplab_ui.faceswaplab_unit_settings import FaceSwapUnitSettings
from scripts.faceswaplab_ui.faceswaplab_unit_ui import faceswap_unit_ui from scripts.faceswaplab_ui.faceswaplab_unit_ui import faceswap_unit_ui
from scripts.faceswaplab_utils import face_checkpoints_utils, imgutils from scripts.faceswaplab_utils import imgutils
from scripts.faceswaplab_utils.faceswaplab_logging import logger from scripts.faceswaplab_utils.faceswaplab_logging import logger
from scripts.faceswaplab_utils.models_utils import get_models from scripts.faceswaplab_utils.models_utils import get_swap_models
from scripts.faceswaplab_utils.ui_utils import dataclasses_from_flat_list from scripts.faceswaplab_utils.ui_utils import dataclasses_from_flat_list
@ -74,7 +75,7 @@ def extract_faces(
[PostProcessingOptions], components [PostProcessingOptions], components
).pop() ).pop()
images = [ images = [
Image.open(file.name) for file in files Image.open(file.name) for file in files # type: ignore
] # potentially greedy but Image.open is supposed to be lazy ] # potentially greedy but Image.open is supposed to be lazy
result_images = swapper.extract_faces( result_images = swapper.extract_faces(
images, extract_path=extract_path, postprocess_options=postprocess_options images, extract_path=extract_path, postprocess_options=postprocess_options
@ -136,7 +137,7 @@ def analyse_faces(image: PILImage, det_threshold: float = 0.5) -> Optional[str]:
def build_face_checkpoint_and_save( def build_face_checkpoint_and_save(
batch_files: gr.File, name: str, overwrite: bool batch_files: List[gr.File], name: str, overwrite: bool
) -> PILImage: ) -> PILImage:
""" """
Builds a face checkpoint using the provided image files, performs face swapping, Builds a face checkpoint using the provided image files, performs face swapping,
@ -154,17 +155,19 @@ def build_face_checkpoint_and_save(
try: try:
if not batch_files: if not batch_files:
logger.error("No face found") logger.error("No face found")
return None return None # type: ignore (Optional not really supported by old gradio)
images = [Image.open(file.name) for file in batch_files] images: list[PILImage] = [Image.open(file.name) for file in batch_files] # type: ignore
preview_image = face_checkpoints_utils.build_face_checkpoint_and_save( preview_image: PILImage | None = (
images, name, overwrite=overwrite face_checkpoints.build_face_checkpoint_and_save(
images=images, name=name, overwrite=overwrite
)
) )
except Exception as e: except Exception as e:
logger.error("Failed to build checkpoint %s", e) logger.error("Failed to build checkpoint %s", e)
traceback.print_exc() traceback.print_exc()
return None return None # type: ignore
return preview_image return preview_image # type: ignore
def explore_onnx_faceswap_model(model_path: str) -> pd.DataFrame: def explore_onnx_faceswap_model(model_path: str) -> pd.DataFrame:
@ -197,7 +200,7 @@ def explore_onnx_faceswap_model(model_path: str) -> pd.DataFrame:
logger.error("Failed to explore model %s", e) logger.error("Failed to explore model %s", e)
traceback.print_exc() traceback.print_exc()
return None return None # type: ignore
return df return df
@ -205,7 +208,7 @@ def batch_process(
files: List[gr.File], save_path: str, *components: Tuple[Any, ...] files: List[gr.File], save_path: str, *components: Tuple[Any, ...]
) -> List[PILImage]: ) -> List[PILImage]:
try: try:
units_count = opts.data.get("faceswaplab_units_count", 3) units_count = get_sd_option("faceswaplab_units_count", 3)
classes: List[Any] = dataclasses_from_flat_list( classes: List[Any] = dataclasses_from_flat_list(
[FaceSwapUnitSettings] * units_count + [PostProcessingOptions], [FaceSwapUnitSettings] * units_count + [PostProcessingOptions],
@ -216,13 +219,16 @@ def batch_process(
] ]
postprocess_options = classes[-1] postprocess_options = classes[-1]
images_paths = [file.name for file in files] images_paths = [file.name for file in files] # type: ignore
return swapper.batch_process( return (
images_paths, swapper.batch_process(
save_path=save_path, images_paths,
units=units, save_path=save_path,
postprocess_options=postprocess_options, units=units,
postprocess_options=postprocess_options,
)
or []
) )
except Exception as e: except Exception as e:
logger.error("Batch Process error : %s", e) logger.error("Batch Process error : %s", e)
@ -232,7 +238,7 @@ def batch_process(
def tools_ui() -> None: def tools_ui() -> None:
models = get_models() models = get_swap_models()
with gr.Tab("Tools"): with gr.Tab("Tools"):
with gr.Tab("Build"): with gr.Tab("Build"):
gr.Markdown( gr.Markdown(
@ -304,7 +310,7 @@ def tools_ui() -> None:
label="Extracted faces", label="Extracted faces",
show_label=False, show_label=False,
elem_id="faceswaplab_extract_results", elem_id="faceswaplab_extract_results",
).style(columns=[2], rows=[2]) )
extract_save_path = gr.Textbox( extract_save_path = gr.Textbox(
label="Destination Directory", label="Destination Directory",
value="", value="",
@ -360,7 +366,7 @@ def tools_ui() -> None:
label="Batch result", label="Batch result",
show_label=False, show_label=False,
elem_id="faceswaplab_batch_results", elem_id="faceswaplab_batch_results",
).style(columns=[2], rows=[2]) )
batch_save_path = gr.Textbox( batch_save_path = gr.Textbox(
label="Destination Directory", label="Destination Directory",
value="outputs/faceswap/", value="outputs/faceswap/",
@ -370,7 +376,7 @@ def tools_ui() -> None:
"Process & Save", elem_id="faceswaplab_extract_btn" "Process & Save", elem_id="faceswaplab_extract_btn"
) )
unit_components = [] unit_components = []
for i in range(1, opts.data.get("faceswaplab_units_count", 3) + 1): for i in range(1, get_sd_option("faceswaplab_units_count", 3) + 1):
unit_components += faceswap_unit_ui(False, i, id_prefix="faceswaplab_tab") unit_components += faceswap_unit_ui(False, i, id_prefix="faceswaplab_tab")
upscale_options = postprocessing_ui() upscale_options = postprocessing_ui()

6
scripts/faceswaplab_ui/faceswaplab_unit_settings.py
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@ -9,7 +9,7 @@ from PIL import Image
from scripts.faceswaplab_swapping.upcaled_inswapper_options import InswappperOptions from scripts.faceswaplab_swapping.upcaled_inswapper_options import InswappperOptions
from scripts.faceswaplab_utils.imgutils import pil_to_cv2 from scripts.faceswaplab_utils.imgutils import pil_to_cv2
from scripts.faceswaplab_utils.faceswaplab_logging import logger from scripts.faceswaplab_utils.faceswaplab_logging import logger
from scripts.faceswaplab_utils import face_checkpoints_utils from scripts.faceswaplab_swapping import face_checkpoints
from scripts.faceswaplab_inpainting.faceswaplab_inpainting import InpaintingOptions from scripts.faceswaplab_inpainting.faceswaplab_inpainting import InpaintingOptions
from client_api import api_utils from client_api import api_utils
@ -124,7 +124,7 @@ class FaceSwapUnitSettings:
if self.source_face and self.source_face != "None": if self.source_face and self.source_face != "None":
try: try:
logger.info(f"loading face {self.source_face}") logger.info(f"loading face {self.source_face}")
face = face_checkpoints_utils.load_face(self.source_face) face = face_checkpoints.load_face(self.source_face)
self._reference_face = face self._reference_face = face
except Exception as e: except Exception as e:
logger.error("Failed to load checkpoint : %s", e) logger.error("Failed to load checkpoint : %s", e)
@ -169,7 +169,7 @@ class FaceSwapUnitSettings:
if isinstance(file, Image.Image): if isinstance(file, Image.Image):
img = file img = file
else: else:
img = Image.open(file.name) img = Image.open(file.name) # type: ignore
face = swapper.get_or_default( face = swapper.get_or_default(
swapper.get_faces(pil_to_cv2(img)), 0, None swapper.get_faces(pil_to_cv2(img)), 0, None

20
scripts/faceswaplab_ui/faceswaplab_unit_ui.py
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@ -1,9 +1,9 @@
from typing import List from typing import List
from scripts.faceswaplab_ui.faceswaplab_inpainting_ui import face_inpainting_ui from scripts.faceswaplab_ui.faceswaplab_inpainting_ui import face_inpainting_ui
from scripts.faceswaplab_utils.face_checkpoints_utils import get_face_checkpoints from scripts.faceswaplab_swapping.face_checkpoints import get_face_checkpoints
import gradio as gr import gradio as gr
from modules.shared import opts
from modules import shared from modules import shared
from scripts.faceswaplab_utils.sd_utils import get_sd_option
def faceswap_unit_advanced_options( def faceswap_unit_advanced_options(
@ -17,7 +17,7 @@ def faceswap_unit_advanced_options(
face_restorer_name = gr.Radio( face_restorer_name = gr.Radio(
label="Restore Face", label="Restore Face",
choices=["None"] + [x.name() for x in shared.face_restorers], choices=["None"] + [x.name() for x in shared.face_restorers],
value=lambda: opts.data.get( value=lambda: get_sd_option(
"faceswaplab_default_upscaled_swapper_face_restorer", "faceswaplab_default_upscaled_swapper_face_restorer",
"None", "None",
), ),
@ -28,7 +28,7 @@ def faceswap_unit_advanced_options(
face_restorer_visibility = gr.Slider( face_restorer_visibility = gr.Slider(
0, 0,
1, 1,
value=lambda: opts.data.get( value=lambda: get_sd_option(
"faceswaplab_default_upscaled_swapper_face_restorer_visibility", "faceswaplab_default_upscaled_swapper_face_restorer_visibility",
1.0, 1.0,
), ),
@ -39,7 +39,7 @@ def faceswap_unit_advanced_options(
codeformer_weight = gr.Slider( codeformer_weight = gr.Slider(
0, 0,
1, 1,
value=lambda: opts.data.get( value=lambda: get_sd_option(
"faceswaplab_default_upscaled_swapper_face_restorer_weight", 1.0 "faceswaplab_default_upscaled_swapper_face_restorer_weight", 1.0
), ),
step=0.001, step=0.001,
@ -48,7 +48,7 @@ def faceswap_unit_advanced_options(
) )
upscaler_name = gr.Dropdown( upscaler_name = gr.Dropdown(
choices=[upscaler.name for upscaler in shared.sd_upscalers], choices=[upscaler.name for upscaler in shared.sd_upscalers],
value=lambda: opts.data.get( value=lambda: get_sd_option(
"faceswaplab_default_upscaled_swapper_upscaler", "" "faceswaplab_default_upscaled_swapper_upscaler", ""
), ),
label="Upscaler", label="Upscaler",
@ -56,7 +56,7 @@ def faceswap_unit_advanced_options(
) )
improved_mask = gr.Checkbox( improved_mask = gr.Checkbox(
lambda: opts.data.get( lambda: get_sd_option(
"faceswaplab_default_upscaled_swapper_improved_mask", False "faceswaplab_default_upscaled_swapper_improved_mask", False
), ),
interactive=True, interactive=True,
@ -64,7 +64,7 @@ def faceswap_unit_advanced_options(
elem_id=f"{id_prefix}_face{unit_num}_improved_mask", elem_id=f"{id_prefix}_face{unit_num}_improved_mask",
) )
color_corrections = gr.Checkbox( color_corrections = gr.Checkbox(
lambda: opts.data.get( lambda: get_sd_option(
"faceswaplab_default_upscaled_swapper_fixcolor", False "faceswaplab_default_upscaled_swapper_fixcolor", False
), ),
interactive=True, interactive=True,
@ -72,7 +72,7 @@ def faceswap_unit_advanced_options(
elem_id=f"{id_prefix}_face{unit_num}_color_corrections", elem_id=f"{id_prefix}_face{unit_num}_color_corrections",
) )
sharpen_face = gr.Checkbox( sharpen_face = gr.Checkbox(
lambda: opts.data.get( lambda: get_sd_option(
"faceswaplab_default_upscaled_swapper_sharpen", False "faceswaplab_default_upscaled_swapper_sharpen", False
), ),
interactive=True, interactive=True,
@ -82,7 +82,7 @@ def faceswap_unit_advanced_options(
erosion_factor = gr.Slider( erosion_factor = gr.Slider(
0.0, 0.0,
10.0, 10.0,
lambda: opts.data.get("faceswaplab_default_upscaled_swapper_erosion", 1.0), lambda: get_sd_option("faceswaplab_default_upscaled_swapper_erosion", 1.0),
step=0.01, step=0.01,
label="Upscaled swapper mask erosion factor, 1 = default behaviour.", label="Upscaled swapper mask erosion factor, 1 = default behaviour.",
elem_id=f"{id_prefix}_face{unit_num}_erosion_factor", elem_id=f"{id_prefix}_face{unit_num}_erosion_factor",

15
scripts/faceswaplab_utils/imgutils.py
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@ -6,10 +6,10 @@ import numpy as np
from math import isqrt, ceil from math import isqrt, ceil
import torch import torch
from ifnude import detect from ifnude import detect
from scripts.faceswaplab_globals import NSFW_SCORE_THRESHOLD
from modules import processing from modules import processing
import base64 import base64
from collections import Counter from collections import Counter
from scripts.faceswaplab_utils.sd_utils import get_sd_option
from scripts.faceswaplab_utils.typing import BoxCoords, CV2ImgU8, PILImage from scripts.faceswaplab_utils.typing import BoxCoords, CV2ImgU8, PILImage
from scripts.faceswaplab_utils.faceswaplab_logging import logger from scripts.faceswaplab_utils.faceswaplab_logging import logger
@ -25,16 +25,25 @@ def check_against_nsfw(img: PILImage) -> bool:
bool: True if any part of the image is considered NSFW, False otherwise. bool: True if any part of the image is considered NSFW, False otherwise.
""" """
NSFW_SCORE_THRESHOLD = get_sd_option("faceswaplab_nsfw_threshold", 0.7)
# For testing purpose :
if NSFW_SCORE_THRESHOLD >= 1:
return False
shapes: List[bool] = [] shapes: List[bool] = []
chunks: List[Dict[str, Union[int, float]]] = detect(img) chunks: List[Dict[str, Union[int, float]]] = detect(img)
for chunk in chunks: for chunk in chunks:
logger.debug(
f"chunck score {chunk['score']}, threshold : {NSFW_SCORE_THRESHOLD}"
)
shapes.append(chunk["score"] > NSFW_SCORE_THRESHOLD) shapes.append(chunk["score"] > NSFW_SCORE_THRESHOLD)
return any(shapes) return any(shapes)
def pil_to_cv2(pil_img: PILImage) -> CV2ImgU8: # type: ignore def pil_to_cv2(pil_img: PILImage) -> CV2ImgU8:
""" """
Convert a PIL Image into an OpenCV image (cv2). Convert a PIL Image into an OpenCV image (cv2).
@ -44,7 +53,7 @@ def pil_to_cv2(pil_img: PILImage) -> CV2ImgU8: # type: ignore
Returns: Returns:
CV2ImgU8: The input image converted to OpenCV format (BGR). CV2ImgU8: The input image converted to OpenCV format (BGR).
""" """
return cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR) return cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR).astype("uint8")
def cv2_to_pil(cv2_img: CV2ImgU8) -> PILImage: # type: ignore def cv2_to_pil(cv2_img: CV2ImgU8) -> PILImage: # type: ignore

18
scripts/faceswaplab_utils/install_utils.py
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@ -0,0 +1,18 @@
from types import ModuleType
def check_install() -> None:
# Very ugly hack :( due to sdnext optimization not calling install.py every time if git log has not changed
import importlib.util
import sys
import os
current_dir = os.path.dirname(os.path.realpath(__file__))
check_install_path = os.path.join(current_dir, "..", "..", "install.py")
spec = importlib.util.spec_from_file_location("check_install", check_install_path)
if spec != None:
check_install: ModuleType = importlib.util.module_from_spec(spec)
sys.modules["check_install"] = check_install
spec.loader.exec_module(check_install) # type: ignore
check_install.check_install() # type: ignore
#### End of ugly hack :( !

48
scripts/faceswaplab_utils/models_utils.py
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@ -3,12 +3,47 @@ import os
from typing import List from typing import List
import modules.scripts as scripts import modules.scripts as scripts
from modules import scripts from modules import scripts
from scripts.faceswaplab_globals import EXTENSION_PATH from scripts.faceswaplab_globals import EXPECTED_INSWAPPER_SHA1, EXTENSION_PATH
from modules.shared import opts from modules.shared import opts
from scripts.faceswaplab_utils.faceswaplab_logging import logger from scripts.faceswaplab_utils.faceswaplab_logging import logger
import traceback
import hashlib
def get_models() -> List[str]: def is_sha1_matching(file_path: str, expected_sha1: str) -> bool:
sha1_hash = hashlib.sha1(usedforsecurity=False)
try:
with open(file_path, "rb") as file:
for byte_block in iter(lambda: file.read(4096), b""):
sha1_hash.update(byte_block)
if sha1_hash.hexdigest() == expected_sha1:
return True
else:
return False
except Exception as e:
logger.error(
"Failed to check model hash, check the model is valid or has been downloaded adequately : %e",
e,
)
traceback.print_exc()
return False
def check_model() -> bool:
model_path = get_current_swap_model()
if not is_sha1_matching(
file_path=model_path, expected_sha1=EXPECTED_INSWAPPER_SHA1
):
logger.error(
"Suspicious sha1 for model %s, check the model is valid or has been downloaded adequately. Should be %s",
model_path,
EXPECTED_INSWAPPER_SHA1,
)
return False
return True
def get_swap_models() -> List[str]:
""" """
Retrieve a list of swap model files. Retrieve a list of swap model files.
@ -31,15 +66,16 @@ def get_models() -> List[str]:
return models return models
def get_current_model() -> str: def get_current_swap_model() -> str:
model = opts.data.get("faceswaplab_model", None) model = opts.data.get("faceswaplab_model", None) # type: ignore
if model is None: if model is None:
models = get_models() models = get_swap_models()
model = models[0] if len(models) else None model = models[0] if len(models) else None
logger.info("Try to use model : %s", model) logger.info("Try to use model : %s", model)
if not os.path.isfile(model): if not os.path.isfile(model): # type: ignore
logger.error("The model %s cannot be found or loaded", model) logger.error("The model %s cannot be found or loaded", model)
raise FileNotFoundError( raise FileNotFoundError(
"No faceswap model found. Please add it to the faceswaplab directory." "No faceswap model found. Please add it to the faceswaplab directory."
) )
assert model is not None
return model return model

7
scripts/faceswaplab_utils/sd_utils.py
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@ -0,0 +1,7 @@
from typing import Any
from modules.shared import opts
def get_sd_option(name: str, default: Any) -> Any:
assert opts.data is not None
return opts.data.get(name, default)

4
scripts/faceswaplab_utils/typing.py
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@ -1,10 +1,10 @@
from typing import Tuple from typing import Tuple
from numpy import uint8 from numpy import uint8
from numpy.typing import NDArray
from insightface.app.common import Face as IFace from insightface.app.common import Face as IFace
from PIL import Image from PIL import Image
import numpy as np
PILImage = Image.Image PILImage = Image.Image
CV2ImgU8 = NDArray[uint8] CV2ImgU8 = np.ndarray[int, np.dtype[uint8]]
Face = IFace Face = IFace
BoxCoords = Tuple[int, int, int, int] BoxCoords = Tuple[int, int, int, int]

4
scripts/faceswaplab_utils/ui_utils.py
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@ -14,6 +14,10 @@ def dataclass_from_flat_list(cls: type, values: Tuple[Any, ...]) -> Any:
init_values[field.name] = field.type(*inner_values) init_values[field.name] = field.type(*inner_values)
idx += len(inner_values) idx += len(inner_values)
else: else:
if idx >= len(values):
raise IndexError(
f"Expected more values for dataclass {cls}. Current index: {idx}, values length: {len(values)}"
)
value = values[idx] value = values[idx]
init_values[field.name] = value init_values[field.name] = value
idx += 1 idx += 1

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