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V1.2.2 : Install speed fix (wip) #46

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glucauze merged 8 commits from v1.2.2 into main 2023-08-16 23:58:22 +02:00
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32 changed files with 359 additions and 218 deletions
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README.md
+33 -2
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@@ -14,7 +14,7 @@ While FaceSwapLab is still under development, it has reached a good level of sta
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.
+ **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
## 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
+ **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**
+ **GPU**
+ **Inpainting Fixes** : supports “only masked” and mask inpainting.
+ **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.
+ **API with typing support** :
+ **API with typing support**
## Installation
check.sh
+1 -1
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@@ -1,4 +1,4 @@
#!/bin/bash
autoflake --in-place --remove-unused-variables -r --remove-all-unused-imports .
mypy --install-types
mypy --non-interactive --install-types
pre-commit run --all-files
docs/documentation.markdown
+15
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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 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
Here is the interface for FaceSwap Lab. It is available in the form of an accordion in both img2img and txt2img.
docs/index.markdown
+1 -3
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@@ -20,7 +20,7 @@ While FaceSwapLab is still under development, it has reached a good level of sta
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.
+ **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.
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.
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.
install.py
+10 -5
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@@ -7,15 +7,15 @@ from packaging.version import parse
def check_install() -> None:
use_gpu = getattr(
shared.cmd_opts, "faceswaplab_gpu", False
) or shared.opts.data.get("faceswaplab_use_gpu", False)
use_gpu = not getattr(shared.cmd_opts, "use-cpu", False)
if use_gpu and sys.platform != "darwin":
print("Faceswaplab : Use GPU requirements")
req_file = os.path.join(
os.path.dirname(os.path.realpath(__file__)), "requirements-gpu.txt"
)
else:
print("Faceswaplab : Use CPU requirements")
req_file = os.path.join(
os.path.dirname(os.path.realpath(__file__)), "requirements.txt"
)
@@ -36,6 +36,8 @@ def check_install() -> None:
required_version = parse(package.split(">=")[1])
return installed_version >= required_version
else:
if package_name == "opencv-python":
return launch.is_installed(package_name) or launch.is_installed("cv2")
return launch.is_installed(package_name)
print("Checking faceswaplab requirements")
@@ -54,9 +56,12 @@ def check_install() -> None:
except Exception as e:
print(e)
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
check_install()
import timeit
check_time = timeit.timeit(check_install, number=1)
print(check_time)
models.json
+1
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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"}]
requirements-gpu.txt
+1
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@@ -3,6 +3,7 @@ dill
ifnude
insightface==0.7.3
onnx>=1.14.0
protobuf>=3.20.2
opencv-python
pandas
pydantic
requirements.txt
+1
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@@ -1,3 +1,4 @@
protobuf>=3.20.2
cython
dill
ifnude
scripts/configure.py
+2 -44
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@@ -1,51 +1,15 @@
import os
from tqdm import tqdm
import traceback
import urllib.request
from scripts.faceswaplab_utils.faceswaplab_logging import logger
from scripts.faceswaplab_globals import *
from packaging import version
import pkg_resources
import hashlib
from scripts.faceswaplab_utils.models_utils import check_model
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:
global ALREADY_DONE
@@ -83,13 +47,7 @@ def check_configuration() -> None:
if not os.path.exists(model_path):
download(model_url, model_path)
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,
)
check_model()
gradio_version = pkg_resources.get_distribution("gradio").version
scripts/faceswaplab.py
+12 -11
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@@ -1,4 +1,5 @@
from scripts.configure import check_configuration
from scripts.faceswaplab_utils.sd_utils import get_sd_option
check_configuration()
@@ -12,7 +13,7 @@ from scripts.faceswaplab_settings import faceswaplab_settings
from scripts.faceswaplab_swapping import swapper
from scripts.faceswaplab_ui import faceswaplab_tab, faceswaplab_unit_ui
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.ui_utils import dataclasses_from_flat_list
from scripts.faceswaplab_utils.faceswaplab_logging import logger, save_img_debug
@@ -76,7 +77,7 @@ class FaceSwapScript(scripts.Script):
@property
def units_count(self) -> int:
return opts.data.get("faceswaplab_units_count", 3)
return get_sd_option("faceswaplab_units_count", 3)
@property
def enabled(self) -> bool:
@@ -85,7 +86,7 @@ class FaceSwapScript(scripts.Script):
@property
def keep_original_images(self) -> bool:
return opts.data.get("faceswaplab_keep_original", False)
return get_sd_option("faceswaplab_keep_original", False)
@property
def swap_in_generated_units(self) -> List[FaceSwapUnitSettings]:
@@ -99,7 +100,7 @@ class FaceSwapScript(scripts.Script):
return f"faceswaplab"
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]:
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
]
new_inits = swapper.process_images_units(
get_current_model(),
get_current_swap_model(),
self.swap_in_source_units,
images=init_images,
force_blend=True,
@@ -181,7 +182,7 @@ class FaceSwapScript(scripts.Script):
for i, (img, info) in enumerate(zip(orig_images, orig_infotexts)):
batch_index = i % p.batch_size
swapped_images = swapper.process_images_units(
get_current_model(),
get_current_swap_model(),
self.swap_in_generated_units,
images=[(img, info)],
)
@@ -213,8 +214,8 @@ class FaceSwapScript(scripts.Script):
swp_img,
p.outpath_samples,
"",
p.all_seeds[batch_index],
p.all_prompts[batch_index],
p.all_seeds[batch_index], # type: ignore
p.all_prompts[batch_index], # type: ignore
opts.samples_format,
info=new_info,
p=p,
@@ -231,7 +232,7 @@ class FaceSwapScript(scripts.Script):
text = processed.infotexts[0]
infotexts.insert(0, text)
if opts.enable_pnginfo:
grid.info["parameters"] = text
grid.info["parameters"] = text # type: ignore
images.insert(0, grid)
if opts.grid_save:
@@ -239,8 +240,8 @@ class FaceSwapScript(scripts.Script):
grid,
p.outpath_grids,
"swapped-grid",
p.all_seeds[0],
p.all_prompts[0],
p.all_seeds[0], # type: ignore
p.all_prompts[0], # type: ignore
opts.grid_format,
info=text,
short_filename=not opts.grid_extended_filename,
scripts/faceswaplab_api/faceswaplab_api.py
+3 -2
View File
@@ -18,9 +18,10 @@ from scripts.faceswaplab_postprocessing.postprocessing_options import (
PostProcessingOptions,
)
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,
)
from scripts.faceswaplab_utils.typing import PILImage
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
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
)
scripts/faceswaplab_globals.py
+9 -3
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@@ -1,18 +1,24 @@
import os
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"))
# Defining the absolute path for the 'analysers' directory inside 'MODELS_DIR'
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"))
# Defining the absolute path for the 'faces' directory inside 'MODELS_DIR'
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(
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")
# 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
# Defining the expected SHA1 hash value for 'INSWAPPER'
EXPECTED_INSWAPPER_SHA1 = "17a64851eaefd55ea597ee41e5c18409754244c5"
scripts/faceswaplab_inpainting/faceswaplab_inpainting.py
+3 -3
View File
@@ -1,5 +1,5 @@
from dataclasses import dataclass
from typing import List
from typing import List, Optional
import gradio as gr
from client_api import api_utils
@@ -15,10 +15,10 @@ class InpaintingOptions:
@staticmethod
def from_gradio(components: List[gr.components.Component]) -> "InpaintingOptions":
return InpaintingOptions(*components)
return InpaintingOptions(*components) # type: ignore
@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).
scripts/faceswaplab_postprocessing/postprocessing_options.py
+4 -3
View File
@@ -1,3 +1,4 @@
from typing import Optional
from modules.face_restoration import FaceRestoration
from modules.upscaler import UpscalerData
from dataclasses import dataclass
@@ -27,17 +28,17 @@ class PostProcessingOptions:
inpainting_when: InpaintingWhen = InpaintingWhen.BEFORE_UPSCALING
# (Don't use optional for this or gradio parsing will fail) :
inpainting_options: InpaintingOptions = None
inpainting_options: InpaintingOptions = None # type: ignore
@property
def upscaler(self) -> UpscalerData:
def upscaler(self) -> Optional[UpscalerData]:
for upscaler in shared.sd_upscalers:
if upscaler.name == self.upscaler_name:
return upscaler
return None
@property
def face_restorer(self) -> FaceRestoration:
def face_restorer(self) -> Optional[FaceRestoration]:
for face_restorer in shared.face_restorers:
if face_restorer.name() == self.face_restorer_name:
return face_restorer
scripts/faceswaplab_postprocessing/upscaling.py
+1 -1
View File
@@ -17,7 +17,7 @@ def upscale_img(image: PILImage, pp_options: PostProcessingOptions) -> PILImage:
pp_options.scale,
)
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)
scripts/faceswaplab_settings/faceswaplab_settings.py
+12 -2
View File
@@ -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
import gradio as gr
def on_ui_settings() -> None:
section = ("faceswaplab", "FaceSwapLab")
models = get_models()
models = get_swap_models()
shared.opts.add_option(
"faceswaplab_model",
shared.OptionInfo(
@@ -46,6 +46,16 @@ def on_ui_settings() -> None:
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(
"faceswaplab_det_size",
scripts/faceswaplab_utils/face_checkpoints_utils.py → scripts/faceswaplab_swapping/face_checkpoints.py
+49 -32
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.typing import *
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 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(
images: List[PILImage], name: str, overwrite: bool = False, path: str = None
) -> PILImage:
images: List[PILImage],
name: str,
overwrite: bool = False,
path: Optional[str] = None,
) -> Optional[PILImage]:
"""
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
@@ -57,13 +60,17 @@ def build_face_checkpoint_and_save(
name = sanitize_name(name)
images = images or []
logger.info("Build %s with %s images", name, len(images))
faces = swapper.get_faces_from_img_files(images)
blended_face = swapper.blend_faces(faces)
faces: List[Face] = swapper.get_faces_from_img_files(images=images)
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(
scripts.basedir(), "extensions", "sd-webui-faceswaplab", "references"
)
reference_preview_img: PILImage = None
reference_preview_img: PILImage
if blended_face:
if blended_face["gender"] == 0:
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."
)
else:
result = swapper.swap_face(
reference_face=blended_face,
result: swapper.ImageResult = swapper.swap_face(
target_faces=[target_face],
source_face=blended_face,
target_img=reference_preview_img,
model=get_models()[0],
swapping_options=InswappperOptions(face_restorer_name="Codeformer"),
model=get_swap_models()[0],
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
if path:
file_path = path
else:
file_path = os.path.join(get_checkpoint_path(), f"{name}.safetensors")
if not overwrite:
file_number = 1
while os.path.exists(file_path):
file_path = os.path.join(
get_checkpoint_path(), f"{name}_{file_number}.safetensors"
)
file_number += 1
save_face(filename=file_path, face=blended_face)
preview_image.save(file_path + ".png")
try:
data = load_face(file_path)
logger.debug(data)
except Exception as e:
logger.error("Error loading checkpoint, after creation %s", e)
traceback.print_exc()
if path:
file_path = path
else:
file_path = os.path.join(
get_checkpoint_path(), f"{name}.safetensors"
)
if not overwrite:
file_number = 1
while os.path.exists(file_path):
file_path = os.path.join(
get_checkpoint_path(),
f"{name}_{file_number}.safetensors",
)
file_number += 1
save_face(filename=file_path, face=blended_face)
preview_image.save(file_path + ".png")
try:
data = load_face(file_path)
logger.debug(data)
except Exception as e:
logger.error("Error loading checkpoint, after creation %s", e)
traceback.print_exc()
return preview_image
return preview_image
else:
logger.error("No face found")
return None
return None # type: ignore
except Exception as e:
logger.error("Failed to build checkpoint %s", e)
traceback.print_exc()
@@ -140,7 +157,7 @@ def save_face(face: Face, filename: str) -> None:
raise e
def load_face(name: str) -> Face:
def load_face(name: str) -> Optional[Face]:
if name.startswith("data:application/face;base64,"):
with tempfile.NamedTemporaryFile(delete=True) as temp_file:
api_utils.base64_to_safetensors(name, temp_file.name)
scripts/faceswaplab_swapping/facemask.py
+1 -1
View File
@@ -83,7 +83,7 @@ def generate_face_mask(face_image: np.ndarray, device: torch.device) -> np.ndarr
convert_bgr_to_rgb=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)
face_input = torch.unsqueeze(face_input, 0).to(device)
scripts/faceswaplab_swapping/swapper.py
+42 -35
View File
@@ -26,24 +26,24 @@ from scripts.faceswaplab_utils.imgutils import (
)
from scripts.faceswaplab_utils.faceswaplab_logging import logger, save_img_debug
from scripts import faceswaplab_globals
from modules.shared import opts
from functools import lru_cache
from scripts.faceswaplab_ui.faceswaplab_unit_settings import FaceSwapUnitSettings
from scripts.faceswaplab_postprocessing.postprocessing import enhance_image
from scripts.faceswaplab_postprocessing.postprocessing_options import (
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_inpainting.i2i_pp import img2img_diffusion
from modules import shared
import onnxruntime
from scripts.faceswaplab_utils.sd_utils import get_sd_option
def use_gpu() -> bool:
return (
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"
@@ -51,7 +51,7 @@ def use_gpu() -> bool:
def force_install_gpu_providers() -> None:
# Ugly Ugly hack due to SDNEXT :
try:
from scripts.configure import check_install
from scripts.faceswaplab_utils.install_utils import check_install
logger.warning("Try to reinstall gpu dependencies")
check_install()
@@ -101,8 +101,9 @@ def cosine_similarity_face(face1: Face, face2: Face) -> float:
non-negative similarity score.
"""
# Reshape the face embeddings to have a shape of (1, -1)
vec1 = face1.embedding.reshape(1, -1)
vec2 = face2.embedding.reshape(1, -1)
assert face1.normed_embedding is not None and face2.normed_embedding is not None
vec1 = face1.normed_embedding.reshape(1, -1)
vec2 = face2.normed_embedding.reshape(1, -1)
# Calculate the cosine similarity between the reshaped embeddings
similarity = cosine_similarity(vec1, vec2)
@@ -141,7 +142,7 @@ def batch_process(
src_images: List[Union[PILImage, str]], # image or filename
save_path: Optional[str],
units: List[FaceSwapUnitSettings],
postprocess_options: PostProcessingOptions,
postprocess_options: Optional[PostProcessingOptions],
) -> 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.
@@ -166,6 +167,7 @@ def batch_process(
if src_images is not None and len(units) > 0:
result_images = []
for src_image in src_images:
path: str = ""
if isinstance(src_image, str):
if save_path:
path = os.path.join(
@@ -180,9 +182,9 @@ def batch_process(
current_images = []
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]
logger.info("%s images generated", len(current_images))
@@ -209,7 +211,7 @@ def extract_faces(
images: List[PILImage],
extract_path: Optional[str],
postprocess_options: PostProcessingOptions,
) -> Optional[List[str]]:
) -> Optional[List[PILImage]]:
"""
Extracts faces from a list of image files.
@@ -232,14 +234,14 @@ def extract_faces(
os.makedirs(extract_path, exist_ok=True)
if images:
result_images = []
result_images: list[PILImage] = []
for img in images:
faces = get_faces(pil_to_cv2(img))
if faces:
face_images = []
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
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)
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:
"""
Retrieves the analysis model for face analysis.
@@ -355,7 +359,9 @@ def getAnalysisModel(
@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.
@@ -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 capture_stdout() as captured:
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)
logger.info("%s", pformat(captured.getvalue()))
@@ -402,14 +408,16 @@ def get_faces(
"""
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:
x = opts.data.get("faceswaplab_det_size", 640)
x = get_sd_option("faceswaplab_det_size", 640)
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
faces = face_analyser.get(img_data)
@@ -433,7 +441,7 @@ def get_faces(
try:
# 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:
logger.error("Failed to get faces %s", e)
traceback.print_exc()
@@ -470,7 +478,7 @@ def filter_faces(
filtered_faces = sorted(
all_faces,
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:
@@ -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
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.
@@ -538,7 +546,7 @@ def get_faces_from_img_files(images: List[PILImage]) -> List[Optional[CV2ImgU8]]
"""
faces = []
faces: List[Face] = []
if len(images) > 0:
for img in images:
@@ -566,7 +574,7 @@ def blend_faces(faces: List[Face]) -> Optional[Face]:
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:
embedding_shape = embeddings[0].shape
@@ -592,19 +600,16 @@ def blend_faces(faces: List[Face]) -> Optional[Face]:
def swap_face(
reference_face: CV2ImgU8,
source_face: Face,
target_img: PILImage,
target_faces: List[Face],
model: str,
swapping_options: Optional[InswappperOptions],
compute_similarity: bool = True,
) -> ImageResult:
"""
Swaps faces in the target image with the source face.
Args:
reference_face (CV2ImgU8): The reference face used for similarity comparison.
source_face (CV2ImgU8): The source face to be swapped.
target_img (PILImage): The target image to swap faces in.
model (str): Path to the face swap model.
@@ -614,14 +619,16 @@ def swap_face(
"""
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:
gender = source_face["gender"]
logger.info("Source Gender %s", gender)
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)
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))
for i, swapped_face in enumerate(target_faces):
@@ -679,9 +686,9 @@ def process_image_unit(
model: str,
unit: FaceSwapUnitSettings,
image: PILImage,
info: str = None,
info: Optional[str] = None,
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).
Args:
@@ -722,7 +729,9 @@ def process_image_unit(
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
if unit.pre_inpainting.inpainting_denoising_strengh > 0:
@@ -732,13 +741,11 @@ def process_image_unit(
save_img_debug(image, "Before swap")
result: ImageResult = swap_face(
reference_face=reference_face,
source_face=src_face,
target_img=current_image,
target_faces=target_faces,
model=model,
swapping_options=unit.swapping_options,
compute_similarity=unit.compute_similarity,
)
# Apply post-inpainting to image
if unit.post_inpainting.inpainting_denoising_strengh > 0:
scripts/faceswaplab_swapping/upcaled_inswapper_options.py
+4 -3
View File
@@ -1,20 +1,21 @@
from dataclasses import *
from typing import Optional
from client_api import api_utils
@dataclass
class InswappperOptions:
face_restorer_name: str = None
face_restorer_name: Optional[str] = None
restorer_visibility: float = 1
codeformer_weight: float = 1
upscaler_name: str = None
upscaler_name: Optional[str] = None
improved_mask: bool = False
color_corrections: bool = False
sharpen: bool = False
erosion_factor: float = 1.0
@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).
scripts/faceswaplab_swapping/upscaled_inswapper.py
+15 -8
View File
@@ -1,10 +1,9 @@
from typing import Any, Tuple, Union
from typing import Any, Optional, Tuple, Union
import cv2
import numpy as np
from insightface.model_zoo.inswapper import INSwapper
from insightface.utils import face_align
from modules import processing, shared
from modules.shared import opts
from modules.upscaler import UpscalerData
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.upcaled_inswapper_options import InswappperOptions
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.faceswaplab_logging import logger
def get_upscaler() -> UpscalerData:
def get_upscaler() -> Optional[UpscalerData]:
for upscaler in shared.sd_upscalers:
if upscaler.name == opts.data.get(
if upscaler.name == get_sd_option(
"faceswaplab_upscaled_swapper_upscaler", "LDSR"
):
return upscaler
@@ -130,8 +130,14 @@ class UpscaledINSwapper(INSwapper):
self.__dict__.update(inswapper.__dict__)
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:
if inswapper_options is None:
return img
pil_img = cv2_to_pil(img)
pp_options = PostProcessingOptions(
upscaler_name=inswapper_options.upscaler_name,
@@ -156,7 +162,7 @@ class UpscaledINSwapper(INSwapper):
target_face: Face,
source_face: Face,
paste_back: bool = True,
options: InswappperOptions = None,
options: Optional[InswappperOptions] = None,
) -> Union[CV2ImgU8, Tuple[CV2ImgU8, Any]]:
aimg, M = face_align.norm_crop2(img, target_face.kps, self.input_size[0])
blob = cv2.dnn.blobFromImage(
@@ -166,9 +172,10 @@ class UpscaledINSwapper(INSwapper):
(self.input_mean, self.input_mean, self.input_mean),
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.linalg.norm(latent)
assert self.session is not None
pred = self.session.run(
self.output_names, {self.input_names[0]: blob, self.input_names[1]: latent}
)[0]
@@ -274,7 +281,7 @@ class UpscaledINSwapper(INSwapper):
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_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))
scripts/faceswaplab_ui/faceswaplab_inpainting_ui.py
+3 -3
View File
@@ -1,7 +1,7 @@
from typing import List
import gradio as gr
from modules.shared import opts
from modules import sd_models, sd_samplers
from scripts.faceswaplab_utils.sd_utils import get_sd_option
def face_inpainting_ui(
@@ -19,14 +19,14 @@ def face_inpainting_ui(
)
inpainting_denoising_prompt = gr.Textbox(
opts.data.get(
get_sd_option(
"faceswaplab_pp_default_inpainting_prompt", "Portrait of a [gender]"
),
elem_id=f"{id_prefix}_pp_inpainting_denoising_prompt",
label="Inpainting prompt use [gender] instead of men or woman",
)
inpainting_denoising_negative_prompt = gr.Textbox(
opts.data.get(
get_sd_option(
"faceswaplab_pp_default_inpainting_negative_prompt", "blurry"
),
elem_id=f"{id_prefix}_pp_inpainting_denoising_neg_prompt",
scripts/faceswaplab_ui/faceswaplab_postprocessing_ui.py
+9 -9
View File
@@ -2,8 +2,8 @@ from typing import List
import gradio as gr
import modules
from modules import shared, sd_models
from modules.shared import opts
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]:
@@ -15,7 +15,7 @@ def postprocessing_ui() -> List[gr.components.Component]:
face_restorer_name = gr.Radio(
label="Restore Face",
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",
shared.face_restorers[0].name(),
),
@@ -26,7 +26,7 @@ def postprocessing_ui() -> List[gr.components.Component]:
face_restorer_visibility = gr.Slider(
0,
1,
value=lambda: opts.data.get(
value=lambda: get_sd_option(
"faceswaplab_pp_default_face_restorer_visibility", 1
),
step=0.001,
@@ -36,7 +36,7 @@ def postprocessing_ui() -> List[gr.components.Component]:
codeformer_weight = gr.Slider(
0,
1,
value=lambda: opts.data.get(
value=lambda: get_sd_option(
"faceswaplab_pp_default_face_restorer_weight", 1
),
step=0.001,
@@ -45,7 +45,7 @@ def postprocessing_ui() -> List[gr.components.Component]:
)
upscaler_name = gr.Dropdown(
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",
elem_id="faceswaplab_pp_upscaler",
)
@@ -60,7 +60,7 @@ def postprocessing_ui() -> List[gr.components.Component]:
upscaler_visibility = gr.Slider(
0,
1,
value=lambda: opts.data.get(
value=lambda: get_sd_option(
"faceswaplab_pp_default_upscaler_visibility", 1
),
step=0.1,
@@ -87,21 +87,21 @@ def postprocessing_ui() -> List[gr.components.Component]:
)
inpainting_denoising_prompt = gr.Textbox(
opts.data.get(
get_sd_option(
"faceswaplab_pp_default_inpainting_prompt", "Portrait of a [gender]"
),
elem_id="faceswaplab_pp_inpainting_denoising_prompt",
label="Inpainting prompt use [gender] instead of men or woman",
)
inpainting_denoising_negative_prompt = gr.Textbox(
opts.data.get(
get_sd_option(
"faceswaplab_pp_default_inpainting_negative_prompt", "blurry"
),
elem_id="faceswaplab_pp_inpainting_denoising_neg_prompt",
label="Inpainting negative prompt use [gender] instead of men or woman",
)
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(
choices=samplers_names,
value=[samplers_names[0]],
scripts/faceswaplab_ui/faceswaplab_tab.py
+29 -23
View File
@@ -1,11 +1,12 @@
import traceback
from pprint import pformat
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 *
import gradio as gr
import onnx
import pandas as pd
from modules.shared import opts
from PIL import Image
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_unit_settings import FaceSwapUnitSettings
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.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
@@ -74,7 +75,7 @@ def extract_faces(
[PostProcessingOptions], components
).pop()
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
result_images = swapper.extract_faces(
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(
batch_files: gr.File, name: str, overwrite: bool
batch_files: List[gr.File], name: str, overwrite: bool
) -> PILImage:
"""
Builds a face checkpoint using the provided image files, performs face swapping,
@@ -154,17 +155,19 @@ def build_face_checkpoint_and_save(
try:
if not batch_files:
logger.error("No face found")
return None
images = [Image.open(file.name) for file in batch_files]
preview_image = face_checkpoints_utils.build_face_checkpoint_and_save(
images, name, overwrite=overwrite
return None # type: ignore (Optional not really supported by old gradio)
images: list[PILImage] = [Image.open(file.name) for file in batch_files] # type: ignore
preview_image: PILImage | None = (
face_checkpoints.build_face_checkpoint_and_save(
images=images, name=name, overwrite=overwrite
)
)
except Exception as e:
logger.error("Failed to build checkpoint %s", e)
traceback.print_exc()
return None
return preview_image
return None # type: ignore
return preview_image # type: ignore
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)
traceback.print_exc()
return None
return None # type: ignore
return df
@@ -205,7 +208,7 @@ def batch_process(
files: List[gr.File], save_path: str, *components: Tuple[Any, ...]
) -> List[PILImage]:
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(
[FaceSwapUnitSettings] * units_count + [PostProcessingOptions],
@@ -216,13 +219,16 @@ def batch_process(
]
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(
images_paths,
save_path=save_path,
units=units,
postprocess_options=postprocess_options,
return (
swapper.batch_process(
images_paths,
save_path=save_path,
units=units,
postprocess_options=postprocess_options,
)
or []
)
except Exception as e:
logger.error("Batch Process error : %s", e)
@@ -232,7 +238,7 @@ def batch_process(
def tools_ui() -> None:
models = get_models()
models = get_swap_models()
with gr.Tab("Tools"):
with gr.Tab("Build"):
gr.Markdown(
@@ -304,7 +310,7 @@ def tools_ui() -> None:
label="Extracted faces",
show_label=False,
elem_id="faceswaplab_extract_results",
).style(columns=[2], rows=[2])
)
extract_save_path = gr.Textbox(
label="Destination Directory",
value="",
@@ -360,7 +366,7 @@ def tools_ui() -> None:
label="Batch result",
show_label=False,
elem_id="faceswaplab_batch_results",
).style(columns=[2], rows=[2])
)
batch_save_path = gr.Textbox(
label="Destination Directory",
value="outputs/faceswap/",
@@ -370,7 +376,7 @@ def tools_ui() -> None:
"Process & Save", elem_id="faceswaplab_extract_btn"
)
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")
upscale_options = postprocessing_ui()
scripts/faceswaplab_ui/faceswaplab_unit_settings.py
+3 -3
View File
@@ -9,7 +9,7 @@ from PIL import Image
from scripts.faceswaplab_swapping.upcaled_inswapper_options import InswappperOptions
from scripts.faceswaplab_utils.imgutils import pil_to_cv2
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 client_api import api_utils
@@ -124,7 +124,7 @@ class FaceSwapUnitSettings:
if self.source_face and self.source_face != "None":
try:
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
except Exception as e:
logger.error("Failed to load checkpoint : %s", e)
@@ -169,7 +169,7 @@ class FaceSwapUnitSettings:
if isinstance(file, Image.Image):
img = file
else:
img = Image.open(file.name)
img = Image.open(file.name) # type: ignore
face = swapper.get_or_default(
swapper.get_faces(pil_to_cv2(img)), 0, None
scripts/faceswaplab_ui/faceswaplab_unit_ui.py
+10 -10
View File
@@ -1,9 +1,9 @@
from typing import List
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
from modules.shared import opts
from modules import shared
from scripts.faceswaplab_utils.sd_utils import get_sd_option
def faceswap_unit_advanced_options(
@@ -17,7 +17,7 @@ def faceswap_unit_advanced_options(
face_restorer_name = gr.Radio(
label="Restore Face",
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",
"None",
),
@@ -28,7 +28,7 @@ def faceswap_unit_advanced_options(
face_restorer_visibility = gr.Slider(
0,
1,
value=lambda: opts.data.get(
value=lambda: get_sd_option(
"faceswaplab_default_upscaled_swapper_face_restorer_visibility",
1.0,
),
@@ -39,7 +39,7 @@ def faceswap_unit_advanced_options(
codeformer_weight = gr.Slider(
0,
1,
value=lambda: opts.data.get(
value=lambda: get_sd_option(
"faceswaplab_default_upscaled_swapper_face_restorer_weight", 1.0
),
step=0.001,
@@ -48,7 +48,7 @@ def faceswap_unit_advanced_options(
)
upscaler_name = gr.Dropdown(
choices=[upscaler.name for upscaler in shared.sd_upscalers],
value=lambda: opts.data.get(
value=lambda: get_sd_option(
"faceswaplab_default_upscaled_swapper_upscaler", ""
),
label="Upscaler",
@@ -56,7 +56,7 @@ def faceswap_unit_advanced_options(
)
improved_mask = gr.Checkbox(
lambda: opts.data.get(
lambda: get_sd_option(
"faceswaplab_default_upscaled_swapper_improved_mask", False
),
interactive=True,
@@ -64,7 +64,7 @@ def faceswap_unit_advanced_options(
elem_id=f"{id_prefix}_face{unit_num}_improved_mask",
)
color_corrections = gr.Checkbox(
lambda: opts.data.get(
lambda: get_sd_option(
"faceswaplab_default_upscaled_swapper_fixcolor", False
),
interactive=True,
@@ -72,7 +72,7 @@ def faceswap_unit_advanced_options(
elem_id=f"{id_prefix}_face{unit_num}_color_corrections",
)
sharpen_face = gr.Checkbox(
lambda: opts.data.get(
lambda: get_sd_option(
"faceswaplab_default_upscaled_swapper_sharpen", False
),
interactive=True,
@@ -82,7 +82,7 @@ def faceswap_unit_advanced_options(
erosion_factor = gr.Slider(
0.0,
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,
label="Upscaled swapper mask erosion factor, 1 = default behaviour.",
elem_id=f"{id_prefix}_face{unit_num}_erosion_factor",
scripts/faceswaplab_utils/imgutils.py
+12 -3
View File
@@ -6,10 +6,10 @@ import numpy as np
from math import isqrt, ceil
import torch
from ifnude import detect
from scripts.faceswaplab_globals import NSFW_SCORE_THRESHOLD
from modules import processing
import base64
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.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.
"""
NSFW_SCORE_THRESHOLD = get_sd_option("faceswaplab_nsfw_threshold", 0.7)
# For testing purpose :
if NSFW_SCORE_THRESHOLD >= 1:
return False
shapes: List[bool] = []
chunks: List[Dict[str, Union[int, float]]] = detect(img)
for chunk in chunks:
logger.debug(
f"chunck score {chunk['score']}, threshold : {NSFW_SCORE_THRESHOLD}"
)
shapes.append(chunk["score"] > NSFW_SCORE_THRESHOLD)
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).
@@ -44,7 +53,7 @@ def pil_to_cv2(pil_img: PILImage) -> CV2ImgU8: # type: ignore
Returns:
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
scripts/faceswaplab_utils/install_utils.py
+18
View File
@@ -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 :( !
scripts/faceswaplab_utils/models_utils.py
+42 -6
View File
@@ -3,12 +3,47 @@ import os
from typing import List
import modules.scripts as 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 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.
@@ -31,15 +66,16 @@ def get_models() -> List[str]:
return models
def get_current_model() -> str:
model = opts.data.get("faceswaplab_model", None)
def get_current_swap_model() -> str:
model = opts.data.get("faceswaplab_model", None) # type: ignore
if model is None:
models = get_models()
models = get_swap_models()
model = models[0] if len(models) else None
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)
raise FileNotFoundError(
"No faceswap model found. Please add it to the faceswaplab directory."
)
assert model is not None
return model
scripts/faceswaplab_utils/sd_utils.py
+7
View File
@@ -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)
scripts/faceswaplab_utils/typing.py
+2 -2
View File
@@ -1,10 +1,10 @@
from typing import Tuple
from numpy import uint8
from numpy.typing import NDArray
from insightface.app.common import Face as IFace
from PIL import Image
import numpy as np
PILImage = Image.Image
CV2ImgU8 = NDArray[uint8]
CV2ImgU8 = np.ndarray[int, np.dtype[uint8]]
Face = IFace
BoxCoords = Tuple[int, int, int, int]
scripts/faceswaplab_utils/ui_utils.py
+4
View File
@@ -14,6 +14,10 @@ def dataclass_from_flat_list(cls: type, values: Tuple[Any, ...]) -> Any:
init_values[field.name] = field.type(*inner_values)
idx += len(inner_values)
else:
if idx >= len(values):
raise IndexError(
f"Expected more values for dataclass {cls}. Current index: {idx}, values length: {len(values)}"
)
value = values[idx]
init_values[field.name] = value
idx += 1