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optimization with mac

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Hoping this would solve the mac issues, if you're a mac user, please report if there is an improvement
This commit is contained in:
Kenneth Estanislao 2025-11-16 20:09:12 +08:00
parent 2411f1e9b1
commit b3c4ed9250
1 changed files with 92 additions and 44 deletions
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136
modules/processors/frame/face_swapper.py
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@ -1,8 +1,9 @@
from typing import Any, List
from typing import Any, List, Optional
import cv2
import insightface
import threading
import numpy as np
import platform
import modules.globals
import modules.processors.frame.core
from modules.core import update_status
@ -14,9 +15,9 @@ from modules.utilities import (
is_video,
)
from modules.cluster_analysis import find_closest_centroid
# Removed modules.globals.face_swapper_enabled - assuming controlled elsewhere or implicitly true if used
# Removed modules.globals.opacity - accessed via getattr
import os
from collections import deque
import time
FACE_SWAPPER = None
THREAD_LOCK = threading.Lock()
@ -26,6 +27,16 @@ NAME = "DLC.FACE-SWAPPER"
PREVIOUS_FRAME_RESULT = None # Stores the final processed frame from the previous step
# --- END: Added for Interpolation ---
# --- START: Mac M1-M5 Optimizations ---
IS_APPLE_SILICON = platform.system() == 'Darwin' and platform.machine() == 'arm64'
FRAME_CACHE = deque(maxlen=3) # Cache for frame reuse
FACE_DETECTION_CACHE = {} # Cache face detections
LAST_DETECTION_TIME = 0
DETECTION_INTERVAL = 0.033 # ~30 FPS detection rate for live mode
FRAME_SKIP_COUNTER = 0
ADAPTIVE_QUALITY = True
# --- END: Mac M1-M5 Optimizations ---
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"
@ -69,34 +80,34 @@ def get_face_swapper() -> Any:
model_path = os.path.join(models_dir, model_name)
update_status(f"Loading face swapper model from: {model_path}", NAME)
try:
# Ensure the providers list is correctly passed
# Apply CoreML optimization for Mac systems
# Optimized provider configuration for Apple Silicon
providers_config = []
for p in modules.globals.execution_providers:
if p == "CoreMLExecutionProvider" and IS_APPLE_SILICON:
# Enhanced CoreML configuration for M1-M5
providers_config.append((
"CoreMLExecutionProvider",
{
"ModelFormat": "MLProgram",
"MLComputeUnits": "ALL", # Use Neural Engine + GPU + CPU
"SpecializationStrategy": "FastPrediction",
"AllowLowPrecisionAccumulationOnGPU": 1,
"EnableOnSubgraphs": 1,
"RequireStaticShapes": 0,
"MaximumCacheSize": 1024 * 1024 * 512, # 512MB cache
}
))
else:
providers_config.append(p)
FACE_SWAPPER = insightface.model_zoo.get_model(
model_path,
providers=[
(
(
"CoreMLExecutionProvider",
{
"ModelFormat": "MLProgram",
"MLComputeUnits": "CPUAndGPU",
"SpecializationStrategy": "FastPrediction",
"AllowLowPrecisionAccumulationOnGPU": 1,
},
)
if p == "CoreMLExecutionProvider"
else p
)
for p in modules.globals.execution_providers
],
providers=providers_config,
)
update_status("Face swapper model loaded successfully.", NAME)
except Exception as e:
update_status(f"Error loading face swapper model: {e}", NAME)
# print traceback maybe?
# import traceback
# traceback.print_exc()
FACE_SWAPPER = None # Ensure it remains None on failure
FACE_SWAPPER = None
return None
return FACE_SWAPPER
@ -105,19 +116,22 @@ def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
face_swapper = get_face_swapper()
if face_swapper is None:
update_status("Face swapper model not loaded or failed to load. Skipping swap.", NAME)
return temp_frame # Return original frame if model failed or not loaded
return temp_frame
# Store a copy of the original frame before swapping for opacity blending
original_frame = temp_frame.copy()
# --- Pre-swap Input Check (Optional but good practice) ---
# Pre-swap Input Check with optimization
if temp_frame.dtype != np.uint8:
# print(f"Warning: Input frame is {temp_frame.dtype}, converting to uint8 before swap.")
temp_frame = np.clip(temp_frame, 0, 255).astype(np.uint8)
# --- End Input Check ---
# Apply the face swap
# Apply the face swap with optimized memory handling
try:
# For Apple Silicon, use optimized inference
if IS_APPLE_SILICON:
# Ensure contiguous memory layout for better performance
temp_frame = np.ascontiguousarray(temp_frame)
swapped_frame_raw = face_swapper.get(
temp_frame, target_face, source_face, paste_back=True
)
@ -195,14 +209,50 @@ def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
return final_swapped_frame
# --- START: Mac M1-M5 Optimized Face Detection ---
def get_faces_optimized(frame: Frame, use_cache: bool = True) -> Optional[List[Face]]:
"""Optimized face detection for live mode on Apple Silicon"""
global LAST_DETECTION_TIME, FACE_DETECTION_CACHE
if not use_cache or not IS_APPLE_SILICON:
# Standard detection
if modules.globals.many_faces:
return get_many_faces(frame)
else:
face = get_one_face(frame)
return [face] if face else None
# Adaptive detection rate for live mode
current_time = time.time()
time_since_last = current_time - LAST_DETECTION_TIME
# Skip detection if too soon (adaptive frame skipping)
if time_since_last < DETECTION_INTERVAL and FACE_DETECTION_CACHE:
return FACE_DETECTION_CACHE.get('faces')
# Perform detection
LAST_DETECTION_TIME = current_time
if modules.globals.many_faces:
faces = get_many_faces(frame)
else:
face = get_one_face(frame)
faces = [face] if face else None
# Cache results
FACE_DETECTION_CACHE['faces'] = faces
FACE_DETECTION_CACHE['timestamp'] = current_time
return faces
# --- END: Mac M1-M5 Optimized Face Detection ---
# --- START: Helper function for interpolation and sharpening ---
def apply_post_processing(current_frame: Frame, swapped_face_bboxes: List[np.ndarray]) -> Frame:
"""Applies sharpening and interpolation."""
"""Applies sharpening and interpolation with Apple Silicon optimizations."""
global PREVIOUS_FRAME_RESULT
processed_frame = current_frame.copy()
# 1. Apply Sharpening (if enabled)
# 1. Apply Sharpening (if enabled) with optimized kernel for Apple Silicon
sharpness_value = getattr(modules.globals, "sharpness", 0.0)
if sharpness_value > 0.0 and swapped_face_bboxes:
height, width = processed_frame.shape[:2]
@ -225,23 +275,21 @@ def apply_post_processing(current_frame: Frame, swapped_face_bboxes: List[np.nda
continue
face_region = processed_frame[y1:y2, x1:x2]
if face_region.size == 0: continue # Skip empty regions
if face_region.size == 0: continue
# Apply sharpening using addWeighted for smoother control
# Use try-except for GaussianBlur and addWeighted as they can fail on invalid inputs
# Apply sharpening with optimized parameters for Apple Silicon
try:
blurred = cv2.GaussianBlur(face_region, (0, 0), 3) # sigma=3, kernel size auto
sharpened_region = cv2.addWeighted(
# Use smaller sigma for faster processing on Apple Silicon
sigma = 2 if IS_APPLE_SILICON else 3
blurred = cv2.GaussianBlur(face_region, (0, 0), sigma)
sharpened_region = cv2.addWeighted(
face_region, 1.0 + sharpness_value,
blurred, -sharpness_value,
0
)
# Ensure the sharpened region doesn't have invalid values
sharpened_region = np.clip(sharpened_region, 0, 255).astype(np.uint8)
processed_frame[y1:y2, x1:x2] = sharpened_region
except cv2.error as sharpen_e:
# print(f"Warning: OpenCV error during sharpening: {sharpen_e} for bbox {bbox}") # Debug
# Skip sharpening for this region if it fails
)
sharpened_region = np.clip(sharpened_region, 0, 255).astype(np.uint8)
processed_frame[y1:y2, x1:x2] = sharpened_region
except cv2.error:
pass