Pishtiwan MoradiandClaude Sonnet 4.5
92e062ff34
Added models: - Wan2.2 diffusion models: T2V, Animate 14B, T2V Low Noise - Wan2.2 LoRAs: Lightning 4-step, Lightx2v, Relight, Distill - Text encoders: UMT5-XXL (fp8 and bf16 variants) - CLIP Vision H for Wan workflows - InsightFace buffalo_l model for face detection - IP-Adapter Plus SDXL VIT-H - RealESRGAN x4 upscaler - Wan 2.1 VAE Updated file permissions to executable for all Python files and documentation. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
661 lines
26 KiB
Python
Executable File
661 lines
26 KiB
Python
Executable File
"""
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EyeStabilizer V2 Node for ComfyUI
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Enhanced with ethnicity-aware presets and specialized optimizations.
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Features:
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1. Ethnicity-specific presets (East Asian, South Asian, African, Caucasian, Middle Eastern, Latino, Auto)
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2. Adaptive thresholds based on eye shape characteristics
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3. Specialized enhancement techniques per ethnicity
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4. Auto-calibration mode that learns from the video
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5. Dense eye landmark detection (MediaPipe Face Mesh - 478 landmarks)
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6. Temporal smoothing using Kalman filtering
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7. Blink detection with adaptive thresholds
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8. Eye region enhancement with ethnicity-aware processing
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Usage: Insert between RMBG (background removal) and control map generation in Wan2.2 workflows.
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"""
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import torch
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import numpy as np
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from PIL import Image, ImageFilter, ImageEnhance
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import cv2
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from typing import Tuple, Optional, List, Dict
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from collections import deque
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# Ethnicity-specific preset configurations
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ETHNICITY_PRESETS = {
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"auto": {
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"name": "Auto-Detect",
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"blink_threshold": 0.2,
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"smoothing_strength": 0.7,
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"enhancement_strength": 1.3,
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"ear_baseline": None, # Will be auto-calibrated
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"description": "Automatically calibrates to the person in the video"
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},
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"east_asian": {
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"name": "East Asian (Chinese, Japanese, Korean)",
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"blink_threshold": 0.12, # Lower for monolids/epicanthic folds
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"smoothing_strength": 0.4, # Less aggressive
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"enhancement_strength": 1.1, # Gentler sharpening
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"ear_baseline": 0.18, # Typical open eye EAR for East Asian
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"iris_visibility_factor": 0.7, # Less visible iris area
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"eyelid_weight": 1.3, # More emphasis on eyelid tracking
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"description": "Optimized for monolids and epicanthic folds"
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},
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"south_asian": {
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"name": "South Asian (Indian, Pakistani, Bangladeshi)",
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"blink_threshold": 0.18,
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"smoothing_strength": 0.6,
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"enhancement_strength": 1.25,
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"ear_baseline": 0.22,
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"iris_visibility_factor": 0.85,
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"eyelid_weight": 1.0,
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"description": "Balanced for varied eye shapes"
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},
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"african": {
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"name": "African / African American",
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"blink_threshold": 0.22,
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"smoothing_strength": 0.65,
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"enhancement_strength": 1.4, # Higher contrast enhancement
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"ear_baseline": 0.25,
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"iris_visibility_factor": 0.9,
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"eyelid_weight": 0.9,
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"contrast_boost": 1.15, # Additional contrast for darker skin tones
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"description": "Enhanced contrast for better landmark detection"
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},
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"caucasian": {
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"name": "Caucasian / European",
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"blink_threshold": 0.20,
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"smoothing_strength": 0.7,
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"enhancement_strength": 1.3,
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"ear_baseline": 0.26,
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"iris_visibility_factor": 1.0,
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"eyelid_weight": 1.0,
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"description": "Standard MediaPipe calibration"
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},
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"middle_eastern": {
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"name": "Middle Eastern / Arab",
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"blink_threshold": 0.19,
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"smoothing_strength": 0.6,
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"enhancement_strength": 1.35,
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"ear_baseline": 0.24,
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"iris_visibility_factor": 0.95,
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"eyelid_weight": 1.1,
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"description": "Optimized for almond-shaped eyes"
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},
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"latino": {
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"name": "Latino / Hispanic",
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"blink_threshold": 0.19,
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"smoothing_strength": 0.65,
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"enhancement_strength": 1.3,
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"ear_baseline": 0.24,
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"iris_visibility_factor": 0.95,
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"eyelid_weight": 1.0,
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"description": "Balanced settings for diverse Latino features"
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}
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}
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class KalmanFilter1D:
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"""Simple 1D Kalman filter for temporal smoothing."""
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def __init__(self, process_variance=1e-3, measurement_variance=1e-1):
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self.process_variance = process_variance
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self.measurement_variance = measurement_variance
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self.estimate = 0.0
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self.error_estimate = 1.0
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def update(self, measurement):
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"""Update filter with new measurement."""
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# Prediction
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prediction_error = self.error_estimate + self.process_variance
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# Update
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kalman_gain = prediction_error / (prediction_error + self.measurement_variance)
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self.estimate = self.estimate + kalman_gain * (measurement - self.estimate)
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self.error_estimate = (1 - kalman_gain) * prediction_error
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return self.estimate
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class AdaptiveBlinkDetector:
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"""
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Adaptive blink detector with auto-calibration.
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Learns the person's baseline eye opening from the video.
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Supports blink suppression mode to reduce excessive blinking from Wan2.2.
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"""
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def __init__(self, history_size=5, threshold=0.2, ethnicity_preset=None,
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blink_suppression=False, min_blink_duration=3):
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self.history_size = history_size
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self.threshold = threshold
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self.ear_history = deque(maxlen=history_size)
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self.baseline_ear = None
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self.calibration_samples = []
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self.calibrated = False
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self.ethnicity_preset = ethnicity_preset
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# Blink suppression features
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self.blink_suppression = blink_suppression
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self.min_blink_duration = min_blink_duration # Minimum frames for valid blink
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self.blink_counter = 0 # Consecutive blink frames
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self.forced_open_ear = None # EAR to maintain when suppressing
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def calculate_ear(self, eye_landmarks):
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"""
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Calculate Eye Aspect Ratio (EAR).
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Args:
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eye_landmarks: Array of eye landmark points [(x, y), ...]
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Returns:
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float: Eye aspect ratio (0 = closed, ~0.3 = open)
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"""
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if len(eye_landmarks) < 6:
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return 0.3 # Default open eye
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# Vertical distances
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v1 = np.linalg.norm(eye_landmarks[1] - eye_landmarks[5])
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v2 = np.linalg.norm(eye_landmarks[2] - eye_landmarks[4])
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# Horizontal distance
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h = np.linalg.norm(eye_landmarks[0] - eye_landmarks[3])
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# EAR calculation
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ear = (v1 + v2) / (2.0 * h + 1e-6)
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return ear
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def calibrate(self, ear_value):
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"""
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Auto-calibrate baseline EAR from video samples.
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Collects samples from first ~30 frames to establish baseline.
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"""
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if not self.calibrated:
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self.calibration_samples.append(ear_value)
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# After 30 samples, compute baseline
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if len(self.calibration_samples) >= 30:
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# Use 75th percentile as baseline (likely open eyes)
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self.baseline_ear = np.percentile(self.calibration_samples, 75)
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# Adaptive threshold: blink if drops below 65% of baseline
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self.threshold = 0.35 # 35% drop from baseline = blink
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self.calibrated = True
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print(f"[EyeStabilizer] Auto-calibrated baseline EAR: {self.baseline_ear:.3f}")
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print(f"[EyeStabilizer] Blink threshold set to {self.threshold:.2f} (relative)")
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def detect_blink(self, left_ear, right_ear):
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"""
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Detect if a blink is occurring.
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Uses adaptive threshold if calibrated, otherwise uses preset.
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Includes blink suppression logic to reduce excessive blinking.
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Args:
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left_ear: Left eye aspect ratio
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right_ear: Right eye aspect ratio
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Returns:
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bool: True if blink detected (and allowed)
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"""
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avg_ear = (left_ear + right_ear) / 2.0
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# Auto-calibration in progress
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if not self.calibrated and self.ethnicity_preset == "auto":
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self.calibrate(avg_ear)
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self.ear_history.append(avg_ear)
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if len(self.ear_history) < self.history_size:
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return False
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# Determine if technically blinking
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is_blinking_raw = False
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if self.calibrated and self.baseline_ear:
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is_blinking_raw = avg_ear < (self.baseline_ear * (1 - self.threshold))
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else:
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avg_historical = sum(self.ear_history) / len(self.ear_history)
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is_blinking_raw = avg_ear < (avg_historical * (1 - self.threshold))
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# Blink suppression logic
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if self.blink_suppression:
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if is_blinking_raw:
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self.blink_counter += 1
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# Only allow blink if it's sustained for minimum duration
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# This filters out micro-blinks from Wan2.2 jitter
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if self.blink_counter >= self.min_blink_duration:
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return True # Valid blink
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else:
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return False # Suppress - too short, likely noise
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else:
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# Reset counter when eyes open
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self.blink_counter = 0
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return False
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return is_blinking_raw
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def smooth_blink(self, current_ear, is_blinking):
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"""
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Smooth blink transition.
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Args:
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current_ear: Current eye aspect ratio
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is_blinking: Whether blink is detected
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Returns:
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float: Smoothed EAR value
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"""
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if not self.ear_history:
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return current_ear
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if is_blinking:
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# During blink, interpolate smoothly
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avg_historical = sum(self.ear_history) / len(self.ear_history)
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return current_ear * 0.3 + avg_historical * 0.7
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return current_ear
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class EyeStabilizerV2Node:
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"""
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Enhanced Eye Stabilizer with ethnicity-aware presets.
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"""
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def __init__(self):
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self.type = "EyeStabilizerV2Node"
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self.landmark_filters = {}
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self.blink_detector = None
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self.mediapipe_available = False
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self.current_preset = None
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# Try to import MediaPipe
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try:
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import mediapipe as mp
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self.mp_face_mesh = mp.solutions.face_mesh
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self.face_mesh = None
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self.mediapipe_available = True
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print("[EyeStabilizer V2] MediaPipe Face Mesh available")
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except ImportError:
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print("[EyeStabilizer V2] WARNING: MediaPipe not found. Install with: pip install mediapipe")
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print("[EyeStabilizer V2] Falling back to basic stabilization mode")
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@classmethod
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def INPUT_TYPES(cls):
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ethnicity_choices = list(ETHNICITY_PRESETS.keys())
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ethnicity_names = [ETHNICITY_PRESETS[k]["name"] for k in ethnicity_choices]
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return {
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"required": {
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"images": ("IMAGE",),
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"ethnicity_preset": (ethnicity_choices, {
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"default": "auto",
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"tooltip": "Select ethnicity for optimized eye detection and tracking"
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}),
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"enable_temporal_smoothing": ("BOOLEAN", {
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"default": True,
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"label_on": "Enabled",
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"label_off": "Disabled"
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}),
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"enable_blink_detection": ("BOOLEAN", {
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"default": True,
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"label_on": "Enabled",
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"label_off": "Disabled"
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}),
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"enable_eye_enhancement": ("BOOLEAN", {
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"default": True,
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"label_on": "Enabled",
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"label_off": "Disabled"
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}),
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"blink_suppression_mode": (["off", "light", "moderate", "aggressive"], {
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"default": "off",
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"tooltip": "Reduce excessive blinking from Wan2.2 (off=normal, light=filter 1-2 frame blinks, moderate=3+ frames, aggressive=5+ frames)"
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}),
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},
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"optional": {
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"smoothing_override": ("FLOAT", {
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"default": -1.0,
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"min": -1.0,
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"max": 1.0,
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"step": 0.05,
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"display": "slider",
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"tooltip": "Override preset smoothing (-1 = use preset)"
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}),
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"enhancement_override": ("FLOAT", {
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"default": -1.0,
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"min": -1.0,
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"max": 2.0,
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"step": 0.1,
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"display": "slider",
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"tooltip": "Override preset enhancement (-1 = use preset)"
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}),
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"blink_threshold_override": ("FLOAT", {
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"default": -1.0,
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"min": -1.0,
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"max": 0.5,
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"step": 0.05,
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"display": "slider",
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"tooltip": "Override preset blink threshold (-1 = use preset)"
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}),
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"eye_region_dilation": ("INT", {
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"default": 10,
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"min": 0,
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"max": 50,
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"step": 5,
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"tooltip": "Pixels to expand eye mask region"
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}),
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK", "IMAGE", "STRING")
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RETURN_NAMES = ("stabilized_images", "eye_mask", "debug_visualization", "preset_info")
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FUNCTION = "stabilize_eyes"
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CATEGORY = "PMA Utils/Video Processing"
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def stabilize_eyes(self, images, ethnicity_preset, enable_temporal_smoothing,
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enable_blink_detection, enable_eye_enhancement, blink_suppression_mode,
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smoothing_override=-1.0, enhancement_override=-1.0,
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blink_threshold_override=-1.0, eye_region_dilation=10):
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"""
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Main function with ethnicity-aware processing.
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"""
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# Load preset
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preset = ETHNICITY_PRESETS[ethnicity_preset]
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self.current_preset = preset
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# Apply overrides or use preset values
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smoothing_strength = smoothing_override if smoothing_override >= 0 else preset["smoothing_strength"]
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enhancement_strength = enhancement_override if enhancement_override >= 0 else preset["enhancement_strength"]
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blink_threshold = blink_threshold_override if blink_threshold_override >= 0 else preset["blink_threshold"]
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# Configure blink suppression
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blink_suppression_settings = {
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"off": (False, 0),
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"light": (True, 2), # Filter 1-2 frame micro-blinks
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"moderate": (True, 3), # Filter 1-3 frame blinks
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"aggressive": (True, 5) # Only allow 5+ frame sustained blinks
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}
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suppress_enabled, min_duration = blink_suppression_settings[blink_suppression_mode]
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print(f"[EyeStabilizer V2] Processing {images.shape[0]} frames...")
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print(f"[EyeStabilizer V2] Preset: {preset['name']}")
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print(f"[EyeStabilizer V2] Smoothing: {smoothing_strength:.2f}")
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print(f"[EyeStabilizer V2] Enhancement: {enhancement_strength:.2f}")
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print(f"[EyeStabilizer V2] Blink Threshold: {blink_threshold:.2f}")
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print(f"[EyeStabilizer V2] Blink Suppression: {blink_suppression_mode} (min {min_duration} frames)")
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# Reset state for new sequence
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self.landmark_filters = {}
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self.blink_detector = AdaptiveBlinkDetector(
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threshold=blink_threshold,
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ethnicity_preset=ethnicity_preset,
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blink_suppression=suppress_enabled,
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min_blink_duration=min_duration
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)
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# Process frames
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batch_size = images.shape[0]
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stabilized_frames = []
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eye_masks = []
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debug_frames = []
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for i in range(batch_size):
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frame = images[i]
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stabilized, eye_mask, debug_viz = self._process_frame(
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frame,
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preset,
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enable_temporal_smoothing,
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enable_blink_detection,
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enable_eye_enhancement,
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smoothing_strength,
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enhancement_strength,
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eye_region_dilation
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)
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stabilized_frames.append(stabilized)
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eye_masks.append(eye_mask)
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debug_frames.append(debug_viz)
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if (i + 1) % 10 == 0:
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print(f"[EyeStabilizer V2] Processed {i + 1}/{batch_size} frames")
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# Stack frames
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stabilized_batch = torch.stack(stabilized_frames, dim=0)
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eye_mask_batch = torch.stack(eye_masks, dim=0)
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debug_batch = torch.stack(debug_frames, dim=0)
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# Preset info string
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preset_info = f"{preset['name']}: {preset['description']}"
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if self.blink_detector.calibrated:
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preset_info += f" | Auto-calibrated EAR: {self.blink_detector.baseline_ear:.3f}"
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print(f"[EyeStabilizer V2] Completed: {preset_info}")
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return (stabilized_batch, eye_mask_batch, debug_batch, preset_info)
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def _process_frame(self, frame, preset, enable_smoothing, enable_blink,
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enable_enhancement, smoothing_strength, enhancement_strength, dilation):
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"""Process a single frame with ethnicity-specific optimizations."""
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# Convert to numpy and PIL
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frame_np = (frame.cpu().numpy() * 255).astype(np.uint8)
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frame_pil = Image.fromarray(frame_np)
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# Initialize outputs
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stabilized_pil = frame_pil.copy()
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eye_mask_pil = Image.new('L', frame_pil.size, 0)
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debug_pil = frame_pil.copy()
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if self.mediapipe_available:
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stabilized_pil, eye_mask_pil, debug_pil = self._process_with_mediapipe(
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frame_pil, preset, enable_smoothing, enable_blink, enable_enhancement,
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smoothing_strength, enhancement_strength, dilation
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)
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else:
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if enable_enhancement:
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stabilized_pil = self._enhance_image(frame_pil, enhancement_strength)
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# Convert back to tensors
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stabilized_tensor = self._pil_to_tensor(stabilized_pil)
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eye_mask_tensor = self._pil_to_tensor(eye_mask_pil.convert('RGB'))[:, :, 0:1]
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debug_tensor = self._pil_to_tensor(debug_pil)
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return stabilized_tensor, eye_mask_tensor, debug_tensor
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def _process_with_mediapipe(self, frame_pil, preset, enable_smoothing, enable_blink,
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enable_enhancement, smoothing_strength, enhancement_strength, dilation):
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"""Process with MediaPipe and ethnicity-specific optimizations."""
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# Initialize face mesh
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if self.face_mesh is None:
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self.face_mesh = self.mp_face_mesh.FaceMesh(
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static_image_mode=False,
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max_num_faces=1,
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refine_landmarks=True,
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min_detection_confidence=0.5,
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min_tracking_confidence=0.5
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)
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|
|
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# Ethnicity-specific preprocessing
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frame_pil = self._ethnicity_preprocess(frame_pil, preset)
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|
|
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# Convert to RGB for MediaPipe
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frame_rgb = np.array(frame_pil.convert('RGB'))
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height, width = frame_rgb.shape[:2]
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|
|
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# Process with MediaPipe
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results = self.face_mesh.process(frame_rgb)
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|
|
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stabilized_pil = frame_pil.copy()
|
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eye_mask_pil = Image.new('L', frame_pil.size, 0)
|
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debug_pil = frame_pil.copy()
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|
|
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if results.multi_face_landmarks:
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face_landmarks = results.multi_face_landmarks[0]
|
|
|
|
# Extract eye landmarks
|
|
left_eye_indices = [33, 160, 158, 133, 153, 144]
|
|
right_eye_indices = [362, 385, 387, 263, 373, 380]
|
|
left_iris_indices = [468, 469, 470, 471, 472]
|
|
right_iris_indices = [473, 474, 475, 476, 477]
|
|
|
|
# Get landmark coordinates
|
|
left_eye = self._get_landmarks(face_landmarks, left_eye_indices, width, height)
|
|
right_eye = self._get_landmarks(face_landmarks, right_eye_indices, width, height)
|
|
left_iris = self._get_landmarks(face_landmarks, left_iris_indices, width, height)
|
|
right_iris = self._get_landmarks(face_landmarks, right_iris_indices, width, height)
|
|
|
|
# Apply temporal smoothing with ethnicity weight
|
|
if enable_smoothing:
|
|
eyelid_weight = preset.get("eyelid_weight", 1.0)
|
|
left_eye = self._apply_smoothing(left_eye, "left_eye", smoothing_strength * eyelid_weight)
|
|
right_eye = self._apply_smoothing(right_eye, "right_eye", smoothing_strength * eyelid_weight)
|
|
|
|
iris_weight = preset.get("iris_visibility_factor", 1.0)
|
|
left_iris = self._apply_smoothing(left_iris, "left_iris", smoothing_strength * iris_weight)
|
|
right_iris = self._apply_smoothing(right_iris, "right_iris", smoothing_strength * iris_weight)
|
|
|
|
# Blink detection
|
|
is_blinking = False
|
|
if enable_blink:
|
|
left_ear = self.blink_detector.calculate_ear(left_eye)
|
|
right_ear = self.blink_detector.calculate_ear(right_eye)
|
|
is_blinking = self.blink_detector.detect_blink(left_ear, right_ear)
|
|
|
|
# Create eye mask
|
|
eye_mask_np = np.zeros((height, width), dtype=np.uint8)
|
|
left_eye_int = left_eye.astype(np.int32)
|
|
right_eye_int = right_eye.astype(np.int32)
|
|
|
|
cv2.fillPoly(eye_mask_np, [left_eye_int], 255)
|
|
cv2.fillPoly(eye_mask_np, [right_eye_int], 255)
|
|
|
|
if dilation > 0:
|
|
kernel = np.ones((dilation, dilation), np.uint8)
|
|
eye_mask_np = cv2.dilate(eye_mask_np, kernel, iterations=1)
|
|
|
|
eye_mask_pil = Image.fromarray(eye_mask_np)
|
|
|
|
# Ethnicity-specific enhancement
|
|
if enable_enhancement:
|
|
stabilized_pil = self._ethnicity_enhance_eyes(
|
|
frame_pil, eye_mask_pil, preset, enhancement_strength
|
|
)
|
|
|
|
# Debug visualization
|
|
debug_np = np.array(debug_pil)
|
|
|
|
# Draw landmarks
|
|
for point in left_eye_int:
|
|
cv2.circle(debug_np, tuple(point), 2, (0, 255, 0), -1)
|
|
for point in right_eye_int:
|
|
cv2.circle(debug_np, tuple(point), 2, (0, 255, 0), -1)
|
|
for point in left_iris.astype(np.int32):
|
|
cv2.circle(debug_np, tuple(point), 1, (255, 0, 0), -1)
|
|
for point in right_iris.astype(np.int32):
|
|
cv2.circle(debug_np, tuple(point), 1, (255, 0, 0), -1)
|
|
|
|
if is_blinking:
|
|
cv2.putText(debug_np, "BLINK", (10, 30),
|
|
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
|
|
|
|
# Show preset name
|
|
cv2.putText(debug_np, preset["name"], (10, 60),
|
|
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 0), 2)
|
|
|
|
debug_pil = Image.fromarray(debug_np)
|
|
|
|
return stabilized_pil, eye_mask_pil, debug_pil
|
|
|
|
def _ethnicity_preprocess(self, frame_pil, preset):
|
|
"""Apply ethnicity-specific preprocessing for better landmark detection."""
|
|
|
|
# African/darker skin tones: Boost contrast
|
|
if "contrast_boost" in preset:
|
|
enhancer = ImageEnhance.Contrast(frame_pil)
|
|
frame_pil = enhancer.enhance(preset["contrast_boost"])
|
|
|
|
return frame_pil
|
|
|
|
def _ethnicity_enhance_eyes(self, frame_pil, eye_mask_pil, preset, base_strength):
|
|
"""Apply ethnicity-specific eye enhancement."""
|
|
|
|
frame_np = np.array(frame_pil)
|
|
mask_np = np.array(eye_mask_pil) / 255.0
|
|
|
|
# Base sharpening
|
|
frame_sharp = frame_pil.filter(ImageFilter.SHARPEN)
|
|
enhancer = ImageEnhance.Sharpness(frame_sharp)
|
|
frame_sharp = enhancer.enhance(base_strength)
|
|
frame_sharp_np = np.array(frame_sharp)
|
|
|
|
# Additional contrast boost for African ethnicity
|
|
if "contrast_boost" in preset:
|
|
contrast_enhancer = ImageEnhance.Contrast(Image.fromarray(frame_sharp_np))
|
|
frame_sharp = contrast_enhancer.enhance(1.1)
|
|
frame_sharp_np = np.array(frame_sharp)
|
|
|
|
# Blend using mask
|
|
mask_3ch = np.stack([mask_np] * 3, axis=-1)
|
|
blended_np = (frame_sharp_np * mask_3ch + frame_np * (1 - mask_3ch)).astype(np.uint8)
|
|
|
|
return Image.fromarray(blended_np)
|
|
|
|
def _get_landmarks(self, face_landmarks, indices, width, height):
|
|
"""Extract landmark coordinates."""
|
|
landmarks = []
|
|
for idx in indices:
|
|
landmark = face_landmarks.landmark[idx]
|
|
x = int(landmark.x * width)
|
|
y = int(landmark.y * height)
|
|
landmarks.append([x, y])
|
|
return np.array(landmarks, dtype=np.float32)
|
|
|
|
def _apply_smoothing(self, landmarks, key, strength):
|
|
"""Apply Kalman filtering with ethnicity-adjusted strength."""
|
|
smoothed = []
|
|
|
|
for i, (x, y) in enumerate(landmarks):
|
|
filter_key_x = f"{key}_{i}_x"
|
|
filter_key_y = f"{key}_{i}_y"
|
|
|
|
if filter_key_x not in self.landmark_filters:
|
|
variance = 1e-3 * (1 - strength)
|
|
self.landmark_filters[filter_key_x] = KalmanFilter1D(
|
|
process_variance=variance,
|
|
measurement_variance=1e-1
|
|
)
|
|
self.landmark_filters[filter_key_y] = KalmanFilter1D(
|
|
process_variance=variance,
|
|
measurement_variance=1e-1
|
|
)
|
|
|
|
smoothed_x = self.landmark_filters[filter_key_x].update(x)
|
|
smoothed_y = self.landmark_filters[filter_key_y].update(y)
|
|
smoothed.append([smoothed_x, smoothed_y])
|
|
|
|
return np.array(smoothed, dtype=np.float32)
|
|
|
|
def _enhance_image(self, image_pil, strength):
|
|
"""Basic image enhancement fallback."""
|
|
enhancer = ImageEnhance.Sharpness(image_pil)
|
|
return enhancer.enhance(strength)
|
|
|
|
def _pil_to_tensor(self, pil_image):
|
|
"""Convert PIL Image to ComfyUI tensor."""
|
|
np_image = np.array(pil_image).astype(np.float32) / 255.0
|
|
tensor = torch.from_numpy(np_image)
|
|
return tensor
|
|
|
|
|
|
# Node registration
|
|
NODE_CLASS_MAPPINGS = {
|
|
"PMAEyeStabilizerV2": EyeStabilizerV2Node,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"PMAEyeStabilizerV2": "PMA Eye Stabilizer V2 (Ethnicity-Aware)",
|
|
}
|