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250 lines
9.0 KiB
Python
250 lines
9.0 KiB
Python
import glob
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import os
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from typing import *
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from insightface.app.common import Face
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from safetensors.torch import save_file, safe_open
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import torch
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import modules.scripts as scripts
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from modules import scripts
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from scripts.faceswaplab_swapping.upcaled_inswapper_options import InswappperOptions
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from scripts.faceswaplab_utils.faceswaplab_logging import logger
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from scripts.faceswaplab_utils.typing import *
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from scripts.faceswaplab_utils import imgutils
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from scripts.faceswaplab_utils.models_utils import get_swap_models
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import traceback
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from scripts.faceswaplab_swapping import swapper
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from pprint import pformat
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import re
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from client_api import api_utils
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import tempfile
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def sanitize_name(name: str) -> str:
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"""
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Sanitize the input name by removing special characters and replacing spaces with underscores.
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Parameters:
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name (str): The input name to be sanitized.
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Returns:
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str: The sanitized name with special characters removed and spaces replaced by underscores.
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"""
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name = re.sub("[^A-Za-z0-9_. ]+", "", name)
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name = name.replace(" ", "_")
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return name[:255]
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def build_face_checkpoint_and_save(
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images: List[PILImage],
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name: str,
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overwrite: bool = False,
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path: Optional[str] = None,
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) -> Optional[PILImage]:
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"""
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Builds a face checkpoint using the provided image files, performs face swapping,
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and saves the result to a file. If a blended face is successfully obtained and the face swapping
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process succeeds, the resulting image is returned. Otherwise, None is returned.
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Args:
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batch_files (list): List of image file paths used to create the face checkpoint.
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name (str): The name assigned to the face checkpoint.
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Returns:
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PIL.PILImage or None: The resulting swapped face image if the process is successful; None otherwise.
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"""
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try:
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name = sanitize_name(name)
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images = images or []
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logger.info("Build %s with %s images", name, len(images))
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faces: List[Face] = swapper.get_faces_from_img_files(images=images)
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if faces is None or len(faces) == 0:
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logger.error("No source faces found")
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return None
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blended_face: Optional[Face] = swapper.blend_faces(faces)
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preview_path = os.path.join(
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scripts.basedir(), "extensions", "sd-webui-faceswaplab", "references"
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)
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reference_preview_img: PILImage
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if blended_face:
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if blended_face["gender"] == 0:
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reference_preview_img = Image.open(
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os.path.join(preview_path, "woman.png")
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)
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else:
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reference_preview_img = Image.open(
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os.path.join(preview_path, "man.png")
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)
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if name == "":
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name = "default_name"
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logger.debug("Face %s", pformat(blended_face))
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target_face = swapper.get_or_default(
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swapper.get_faces(imgutils.pil_to_cv2(reference_preview_img)), 0, None
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)
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if target_face is None:
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logger.error(
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"Failed to open reference image, cannot create preview : That should not happen unless you deleted the references folder or change the detection threshold."
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)
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else:
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result: swapper.ImageResult = swapper.swap_face(
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target_faces=[target_face],
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source_face=blended_face,
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target_img=reference_preview_img,
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model=get_swap_models()[0],
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swapping_options=InswappperOptions(
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face_restorer_name="CodeFormer",
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restorer_visibility=1,
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upscaler_name="Lanczos",
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codeformer_weight=1,
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improved_mask=True,
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color_corrections=False,
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sharpen=True,
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),
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)
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preview_image = result.image
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if path:
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file_path = path
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else:
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file_path = os.path.join(
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get_checkpoint_path(), f"{name}.safetensors"
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)
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if not overwrite:
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file_number = 1
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while os.path.exists(file_path):
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file_path = os.path.join(
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get_checkpoint_path(),
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f"{name}_{file_number}.safetensors",
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)
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file_number += 1
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save_face(filename=file_path, face=blended_face)
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preview_image.save(file_path + ".png")
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try:
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data = load_face(file_path)
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logger.debug(data)
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except Exception as e:
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logger.error("Error loading checkpoint, after creation %s", e)
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traceback.print_exc()
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return preview_image
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else:
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logger.error("No face found")
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return None # type: ignore
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except Exception as e:
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logger.error("Failed to build checkpoint %s", e)
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traceback.print_exc()
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return None
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def save_face(face: Face, filename: str) -> None:
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try:
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tensors = {
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"embedding": torch.tensor(face["embedding"]),
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"gender": torch.tensor(face["gender"]),
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"age": torch.tensor(face["age"]),
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}
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save_file(tensors, filename)
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except Exception as e:
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traceback.print_exc
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logger.error("Failed to save checkpoint %s", e)
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raise e
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def load_face(name: str) -> Optional[Face]:
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if name.startswith("data:application/face;base64,"):
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with tempfile.NamedTemporaryFile(delete=True) as temp_file:
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api_utils.base64_to_safetensors(name, temp_file.name)
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face = {}
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with safe_open(temp_file.name, framework="pt", device="cpu") as f:
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for k in f.keys():
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logger.debug("load key %s", k)
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face[k] = f.get_tensor(k).numpy()
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return Face(face)
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filename = matching_checkpoint(name)
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if filename is None:
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return None
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if filename.endswith(".pkl"):
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logger.warning(
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"Pkl files for faces are deprecated to enhance safety, you need to convert them"
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)
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logger.warning("The file will be converted to .safetensors")
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logger.warning(
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"You can also use this script https://gist.github.com/glucauze/4a3c458541f2278ad801f6625e5b9d3d"
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)
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return None
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elif filename.endswith(".safetensors"):
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face = {}
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with safe_open(filename, framework="pt", device="cpu") as f:
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for k in f.keys():
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logger.debug("load key %s", k)
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face[k] = f.get_tensor(k).numpy()
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return Face(face)
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raise NotImplementedError("Unknown file type, face extraction not implemented")
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def get_checkpoint_path() -> str:
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checkpoint_path = os.path.join(scripts.basedir(), "models", "faceswaplab", "faces")
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os.makedirs(checkpoint_path, exist_ok=True)
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return checkpoint_path
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def matching_checkpoint(name: str) -> Optional[str]:
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"""
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Retrieve the full path of a checkpoint file matching the given name.
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If the name already includes a path separator, it is returned as-is. Otherwise, the function looks for a matching
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file with the extensions ".safetensors" or ".pkl" in the checkpoint directory.
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Args:
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name (str): The name or path of the checkpoint file.
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Returns:
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Optional[str]: The full path of the matching checkpoint file, or None if no match is found.
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"""
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# If the name already includes a path separator, return it as is
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if os.path.sep in name:
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return name
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# If the name doesn't end with the specified extensions, look for a matching file
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if not (name.endswith(".safetensors") or name.endswith(".pkl")):
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# Try appending each extension and check if the file exists in the checkpoint path
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for ext in [".safetensors", ".pkl"]:
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full_path = os.path.join(get_checkpoint_path(), name + ext)
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if os.path.exists(full_path):
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return full_path
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# If no matching file is found, return None
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return None
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# If the name already ends with the specified extensions, simply complete the path
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return os.path.join(get_checkpoint_path(), name)
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def get_face_checkpoints() -> List[str]:
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"""
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Retrieve a list of face checkpoint paths.
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This function searches for face files with the extension ".safetensors" in the specified directory and returns a list
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containing the paths of those files.
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Returns:
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list: A list of face paths, including the string "None" as the first element.
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"""
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faces_path = os.path.join(get_checkpoint_path(), "*.safetensors")
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faces = glob.glob(faces_path)
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faces_path = os.path.join(get_checkpoint_path(), "*.pkl")
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faces += glob.glob(faces_path)
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return ["None"] + [os.path.basename(face) for face in sorted(faces)]
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