Files
hacksider-Deep-Live-Cam/modules/processors/frame/face_enhancer_gpen256.py
T
Kenneth Estanislao bd500d54b4 auto download some models
Retarget download url for safer model controls
2026-08-23 02:55:32 +08:00

147 lines
4.5 KiB
Python

"""GPEN-BFR-256 face enhancer — ONNX-based face restoration at 256x256."""
from typing import Any, List
import os
import threading
import modules.globals
import modules.processors.frame.core
from modules import imread_unicode, imwrite_unicode
from modules.core import update_status
from modules.face_analyser import get_one_face
from modules.typing import Frame, Face
from modules.utilities import (
is_image,
is_video,
)
from modules.processors.frame._onnx_enhancer import (
create_onnx_session,
warmup_session,
enhance_face_onnx,
)
NAME = "DLC.FACE-ENHANCER-GPEN256"
INPUT_SIZE = 256
MODEL_MIRROR_URL = "https://github.com/harisreedhar/Face-Upscalers-ONNX/releases/download/GPEN-BFR/GPEN-BFR-256.onnx"
MODEL_FILE = "GPEN-BFR-256.onnx"
ENHANCER = None
THREAD_LOCK = threading.Lock()
abs_dir = os.path.dirname(os.path.abspath(__file__))
models_dir = os.path.join(
os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
)
def _obtain_model():
from modules.model_downloader import ensure_model
model_path = ensure_model(MODEL_FILE)
if model_path is not None:
return model_path
update_status(f"Retrying {MODEL_FILE} from the mirror...", NAME)
from modules.utilities import conditional_download
try:
conditional_download(models_dir, [MODEL_MIRROR_URL])
except Exception as error:
update_status(f"Mirror download failed: {error}", NAME)
return None
fallback = os.path.join(models_dir, MODEL_FILE)
return fallback if os.path.exists(fallback) else None
def pre_check() -> bool:
if _obtain_model() is None:
update_status(
f"Could not obtain {MODEL_FILE}. Place it in the models folder "
"manually or check your internet connection.",
NAME,
)
return False
return True
def pre_start() -> bool:
if not is_image(modules.globals.target_path) and not is_video(modules.globals.target_path):
update_status("Select an image or video for target path.", NAME)
return False
return True
def get_enhancer() -> Any:
global ENHANCER
with THREAD_LOCK:
if ENHANCER is None:
model_path = _obtain_model()
if model_path is None:
raise FileNotFoundError(
f"Model file not found: {os.path.join(models_dir, MODEL_FILE)}"
)
print(f"{NAME}: Loading ONNX model from {model_path}")
ENHANCER = create_onnx_session(model_path)
warmup_session(ENHANCER)
print(f"{NAME}: Model loaded successfully.")
return ENHANCER
def enhance_face(temp_frame: Frame, face: Face) -> Frame:
try:
session = get_enhancer()
except Exception as e:
print(f"{NAME}: {e}")
return temp_frame
try:
return enhance_face_onnx(temp_frame, face, session, INPUT_SIZE)
except Exception as e:
print(f"{NAME}: Error during face enhancement: {e}")
return temp_frame
def process_frame(source_face: Face | None, temp_frame: Frame, detected_faces=None) -> Frame:
if detected_faces:
target_face = detected_faces[0]
else:
target_face = get_one_face(temp_frame)
if target_face is None:
return temp_frame
return enhance_face(temp_frame, target_face)
def process_frame_v2(temp_frame: Frame) -> Frame:
target_face = get_one_face(temp_frame)
if target_face:
temp_frame = enhance_face(temp_frame, target_face)
return temp_frame
def process_frames(
source_path: str | None, temp_frame_paths: List[str], progress: Any = None
) -> None:
for temp_frame_path in temp_frame_paths:
temp_frame = imread_unicode(temp_frame_path)
if temp_frame is None:
if progress:
progress.update(1)
continue
result = process_frame(None, temp_frame)
imwrite_unicode(temp_frame_path, result)
if progress:
progress.update(1)
def process_image(source_path: str | None, target_path: str, output_path: str) -> None:
target_frame = imread_unicode(target_path)
if target_frame is None:
print(f"{NAME}: Error: Failed to read target image {target_path}")
return
result_frame = process_frame(None, target_frame)
imwrite_unicode(output_path, result_frame)
print(f"{NAME}: Enhanced image saved to {output_path}")
def process_video(source_path: str | None, temp_frame_paths: List[str]) -> None:
modules.processors.frame.core.process_video(source_path, temp_frame_paths, process_frames)