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Merge branch 'cinematic-enhance' into evolve

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Gourieff | 古仁
2026-10-10 16:05:46 +07:00
parent 0b377b00d4 3f66ebcba6
commit 067ba6de36
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nodes.py
+3
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@@ -67,6 +67,7 @@ import scripts.r_masking.segs as masking_segs
import scripts.reactor_sfw as sfw
from r_dlssnr.dlss5_node import DLSS5FrameEnhancer
from r_nodes.cinematic_enhancer import ReActorCinematicEnhancer
models_dir = folder_paths.models_dir
@@ -1747,6 +1748,7 @@ NODE_CLASS_MAPPINGS = {
"ImageRGBA2RGB": ImageRGBA2RGB,
"ReActorUnload": ReActorUnload,
"DLSS5FrameEnhancer": DLSS5FrameEnhancer,
"ReActorCinematicEnhancer": ReActorCinematicEnhancer,
}
NODE_DISPLAY_NAME_MAPPINGS = {
@@ -1770,4 +1772,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"ImageRGBA2RGB": "Convert RGBA to RGB 🌌 ReActor",
"ReActorUnload": "Unload ReActor Models 🌌 ReActor",
"DLSS5FrameEnhancer": "DLSS5 Frame Enhancer 🌌 ReActor",
"ReActorCinematicEnhancer": "Cinematic Enhancer 🌌 ReActor",
}
r_nodes/__init__.py
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r_nodes/cinematic_enhancer.py
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@@ -0,0 +1,307 @@
import os
import torch
import numpy as np
import cv2
import folder_paths
from pathlib import Path
from PIL import Image, ImageEnhance, ImageCms
import comfy.model_management as model_management
from scripts.reactor_logger import logger
from r_modules.shared import state
from reactor_utils import (
progress_bar,
progress_bar_reset
)
try:
from colour.io.luts.iridas_cube import read_LUT_IridasCube
AVAILABLE = True
except Exception as e:
AVAILABLE = False
logger.error(f"{e}, ReActorCinematicEnhancer is not available, please install `colour` library: `pip install colour-science`")
folder_paths.add_model_folder_path("luts", os.path.join(folder_paths.models_dir, "luts"))
# Профили для HDR (создаются один раз при импорте для экономии ресурсов)
sRGB_profile = ImageCms.createProfile('sRGB')
Lab_profile = ImageCms.createProfile('LAB')
def get_luts():
luts = folder_paths.get_filename_list("luts")
luts = [x for x in luts if x.lower().endswith(".cube") or x.lower().endswith(".spi1d") or x.lower().endswith(".spi3d") or x.lower().endswith(".spimtx") or x.lower().endswith(".csp")]
luts.sort(key=str.lower)
luts.insert(0, "none")
return luts
class ReActorCinematicEnhancer:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", ),
# --- Global Blend ---
"intensity": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "0..1, def: 0.5"}),
# --- LUT ---
"lut_file": (get_luts(),),
"lut_strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.1, "tooltip": "0..1, def: 0.5"}),
"lut_gamma_correction": ("BOOLEAN", { "default": True }),
# --- Film Effect ---
"film_grain_method": (["advanced","fast"], {"default": "advanced"}),
"film_grain_power": ("FLOAT", {"default": 0.1, "min": 0, "max": 1, "step": 0.01, "tooltip": "0..1, def: 0.1"}),
"film_grain_scale": ("FLOAT", {"default": 1, "min": 0.1, "max": 10, "step": 0.1, "tooltip": "0.1..1, def: 0.1"}),
"film_grain_sat": ("FLOAT", {"default": 0.15, "min": 0, "max": 1, "step": 0.01, "tooltip": "(advanced only) 0..1, def: 0.15"}),
"film_grain_shadows": ("FLOAT", {"default": 0.2, "min": 0, "max": 1, "step": 0.01, "tooltip": "(advanced only) 0..1, def: 0.2"}),
"film_grain_highs": ("FLOAT", {"default": 0.2, "min": 0, "max": 1, "step": 0.01, "tooltip": "(advanced only) 0..1, def: 0.2"}),
"film_blur_strength": ("INT", {"default": 1, "min": 0, "max": 256, "step": 1, "tooltip": "(by depth_map) 0..256, def: 1"}),
"film_focal_depth": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 1, "step": 0.01, "tooltip": "(by depth_map) 0..1, def: 0.9"}),
"film_focus_spread": ("FLOAT", {"default": 2.2, "min": 0.1, "max": 8, "step": 0.1, "tooltip": "(by depth_map) 0..8, def: 2.2"}),
"film_vignette_intensity": ("FLOAT", {"default": 0.1, "min": 0, "max": 1, "step": 0.01, "tooltip": "0..1, def: 0.1"}),
# --- Color Correct ---
"cc_temperature": ("FLOAT", {"default": 0, "min": -100, "max": 100, "step": 5, "tooltip": "-100..100, def: 0"}),
"cc_hue": ("FLOAT", {"default": 0, "min": -90, "max": 90, "step": 5, "tooltip": "-90..90, def: 0"}),
"cc_brightness": ("FLOAT", {"default": 0, "min": -100, "max": 100, "step": 5, "tooltip": "-100..100, def: 0"}),
"cc_contrast": ("FLOAT", {"default": 0, "min": -100, "max": 100, "step": 5, "tooltip": "-100..100, def: 0"}),
"cc_saturation": ("FLOAT", {"default": 0, "min": -100, "max": 100, "step": 5, "tooltip": "-100..100, def: 0"}),
"cc_gamma": ("FLOAT", {"default": 1, "min": 0.2, "max": 2.2, "step": 0.1, "tooltip": "0.2..2.2, def: 1.0"}),
# --- HDR Effects ---
"hdr_intensity": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 5.0, "step": 0.01, "tooltip": "0..5, def: 0.5"}),
"hdr_shadow_intensity": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "0..1, def: 0.25"}),
"hdr_highlight_intensity": ("FLOAT", {"default": 0.75, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "0..1, def: 0.75"}),
"hdr_contrast": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "0..1, def: 0.1"}),
"hdr_enhance_color": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "0..1, def: 0.25"}),
},
"optional": {
"depth_map": ("IMAGE",),
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "execute"
CATEGORY = "🌌 ReActor/PostProcessing"
DESCRIPTION = (
"Requirements:\n"
"- pip install colour-science\n"
"- put LUT-files in models/luts\n"
"depth_map is optional"
)
def execute(self, image, intensity, lut_file, lut_strength, lut_gamma_correction,
film_grain_method, film_grain_power, film_grain_scale, film_grain_sat, film_grain_shadows, film_grain_highs,film_blur_strength, film_focal_depth, film_focus_spread, film_vignette_intensity,
cc_temperature, cc_hue, cc_brightness, cc_contrast, cc_saturation, cc_gamma,
hdr_intensity, hdr_shadow_intensity, hdr_highlight_intensity, hdr_contrast, hdr_enhance_color,
depth_map=None):
if not AVAILABLE:
logger.error("ReActorCinematicEnhancer is not available, please install `colour` library: `pip install colour-science`")
return (image,)
# 1. Загрузка LUT
if lut_file != "none":
lut_file_path = folder_paths.get_full_path("luts", lut_file)
lut = read_LUT_IridasCube(lut_file_path) if lut_file_path and Path(lut_file_path).exists() else None
if lut:
lut.name = lut_file
logger.status(f"LUT {lut_file} selected")
else:
lut = None
batch_size = image.shape[0]
result_batch = []
pbar = progress_bar(batch_size)
logger.status(f"Enhancing...")
for b in range(batch_size):
if state.interrupted or model_management.processing_interrupted():
logger.status("Interrupted by User")
break
# --- БАЗА: NumPy (0-1) ---
orig_np = image[b].cpu().numpy().copy()
img_np = orig_np.copy()
# --- 1. LUT ---
if lut:
lut_img = img_np.copy()
if lut_gamma_correction:
lut_img = np.clip(lut_img ** (1/2.2), 0, 1)
lut_img = lut.apply(lut_img)
if lut_gamma_correction:
lut_img = np.clip(lut_img ** 2.2, 0, 1)
if lut_strength < 1.0:
img_np = lut_strength * lut_img + (1 - lut_strength) * img_np
else:
img_np = lut_img
# Подготовка маски глубины (если есть)
depth_np = None
if depth_map is not None:
d_tensor = depth_map[b if depth_map.shape[0] > 1 else 0].cpu().numpy()
if d_tensor.shape[:2] != img_np.shape[:2]:
d_tensor = cv2.resize(d_tensor, (img_np.shape[1], img_np.shape[0]), interpolation=cv2.INTER_LINEAR)
# Если глубина RGB, берем один канал
depth_np = d_tensor[:,:,0] if d_tensor.ndim == 3 else d_tensor
# --- 2. FILM EFFECT ---
# Виньетка (OpenCV)
if film_vignette_intensity > 0:
h, w = img_np.shape[:2]
Y, X = np.ogrid[:h, :w]
center_y, center_x = h / 2, w / 2
dist_from_center = np.sqrt((X - center_x)**2 + (Y - center_y)**2)
max_dist = np.sqrt(center_x**2 + center_y**2)
vignette = 1 - (dist_from_center / max_dist) * film_vignette_intensity
img_np = np.clip(img_np * vignette[..., np.newaxis], 0, 1)
# Глубинное размытие (пошаговая реализация)
if film_blur_strength > 0 and depth_np is not None:
# Нормализуем разницу глубины относительно точки фокуса
depth_diff = np.abs(depth_np - film_focal_depth) * film_focus_spread
depth_diff = np.clip(depth_diff, 0, 1)
# Пошаговый блендинг (5 шагов для плавности)
steps = 5
blurred_fg = np.zeros_like(img_np)
for step in range(steps + 1):
step_blur = int((film_blur_strength * step / steps) // 2 * 2 + 1)
if step_blur > 1:
blur_layer = cv2.GaussianBlur(img_np, (step_blur, step_blur), 0)
else:
blur_layer = img_np
# Маска весов для текущего шага
mask = np.clip(1.0 - np.abs(depth_diff * steps - step), 0, 1)
blurred_fg += blur_layer * mask[..., np.newaxis]
img_np = blurred_fg
# --- 3. COLOR CORRECT ---
fg_img = Image.fromarray((img_np * 255).astype(np.uint8))
# Яркость и Контраст (PIL)
if cc_brightness != 0:
fg_img = ImageEnhance.Brightness(fg_img).enhance(1 + (cc_brightness / 100))
if cc_contrast != 0:
fg_img = ImageEnhance.Contrast(fg_img).enhance(1 + (cc_contrast / 100))
fg_cv = np.array(fg_img).astype(np.float32) / 255.0
# Температура
temp_val = cc_temperature / 100
if temp_val > 0:
fg_cv[:, :, 0] = np.clip(fg_cv[:, :, 0] * (1 + temp_val), 0, 1)
fg_cv[:, :, 1] = np.clip(fg_cv[:, :, 1] * (1 + temp_val * 0.4), 0, 1)
elif temp_val < 0:
fg_cv[:, :, 2] = np.clip(fg_cv[:, :, 2] * (1 - temp_val), 0, 1)
# Гамма
if cc_gamma != 1.0:
fg_cv = np.clip(np.power(fg_cv, cc_gamma), 0, 1)
# HSV для Hue и Saturation
if cc_hue != 0 or cc_saturation != 0:
hsv = cv2.cvtColor((fg_cv * 255).astype(np.uint8), cv2.COLOR_RGB2HSV).astype(np.float32)
if cc_hue != 0:
hsv[:, :, 0] = (hsv[:, :, 0] + (cc_hue / 2)) % 180 # В OpenCV Hue до 180
if cc_saturation != 0:
hsv[:, :, 1] = np.clip(hsv[:, :, 1] * (1 + cc_saturation / 100), 0, 255)
fg_cv = cv2.cvtColor(hsv.astype(np.uint8), cv2.COLOR_HSV2RGB).astype(np.float32) / 255.0
img_np = fg_cv
# --- 4. FILM GRAIN ---
if film_grain_power > 0:
# --- 4.1 ADVANCED NATIVE GRAIN ---
if film_grain_method == "advanced":
# 1) Генерируем базовый цветной шум
noise_rgb = np.random.normal(0, film_grain_power, img_np.shape).astype(np.float32)
# 2) Генерируем монохромный шум (усреднение по каналам)
noise_mono = np.mean(noise_rgb, axis=-1, keepdims=True)
# 3) Смешиваем цветность зерна (grain_sat)
noise = noise_rgb * film_grain_sat + noise_mono * (1.0 - film_grain_sat)
# 4) Масштабирование зерна
if film_grain_scale != 1.0:
h, w = img_np.shape[:2]
small_noise = cv2.resize(noise, (int(w / film_grain_scale), int(h / film_grain_scale)))
noise = cv2.resize(small_noise, (w, h), interpolation=cv2.INTER_LINEAR)
# 5) Кинематографичная маскировка по яркости (Luminance)
# Вычисляем воспринимаемую яркость пикселей
lum = 0.299 * img_np[:,:,0] + 0.587 * img_np[:,:,1] + 0.114 * img_np[:,:,2]
lum = lum[..., np.newaxis]
# Создаем карту весов шума:
# Средние тона (0.5) получают вес 1.0.
# Тени (0.0) получают вес film_grain_shadows.
# Блики (1.0) получают вес film_grain_highs.
grain_mask = np.where(
lum < 0.5,
film_grain_shadows + (1.0 - film_grain_shadows) * (lum * 2.0), # Плавный переход от теней к средним
1.0 - (1.0 - film_grain_highs) * ((lum - 0.5) * 2.0) # Плавный переход от средних к бликам
)
# Накладываем шум с учетом яркостной маски
img_np = np.clip(img_np + noise * grain_mask, 0, 1)
# --- 4.2 FAST GRAIN ---
else:
noise = np.random.normal(0, film_grain_power, img_np.shape).astype(np.float32)
# Масштабирование шума
if film_grain_scale != 1.0:
h, w = img_np.shape[:2]
small_noise = cv2.resize(noise, (int(w / film_grain_scale), int(h / film_grain_scale)))
noise = cv2.resize(small_noise, (w, h), interpolation=cv2.INTER_LINEAR)
img_np = np.clip(img_np + noise * 0.5, 0, 1)
# --- 5. HDR EFFECTS ---
if hdr_intensity > 0:
final_pil = Image.fromarray((img_np * 255).astype(np.uint8))
# Основная математика тонирования LAB
img_lab = ImageCms.profileToProfile(final_pil, sRGB_profile, Lab_profile, outputMode='LAB')
luminance, a, b = img_lab.split()
base = np.array(luminance, dtype=np.float32)
scaled_shadow = (hdr_shadow_intensity ** 2) * hdr_intensity
scaled_highlight = (hdr_highlight_intensity ** 2) * hdr_intensity
shadow_mask = np.clip((1 - (base / 255)) ** 2, 0, 1)
highlight_mask = np.clip((base / 255) ** 2, 0, 1)
adj_shadows = np.clip(base * (1 - shadow_mask * scaled_shadow), 0, 255)
adj_highlights = np.clip(base + (255 - base) * highlight_mask * scaled_highlight, 0, 255)
adj_lum = np.clip(adj_shadows + adj_highlights - base, 0, 255)
final_lum = np.clip(base * (1 - hdr_intensity) + adj_lum * hdr_intensity, 0, 255).astype(np.uint8)
merged_lab = Image.merge('LAB', (Image.fromarray(final_lum), a, b))
final_pil = ImageCms.profileToProfile(merged_lab, Lab_profile, sRGB_profile, outputMode='RGB')
if hdr_contrast > 0:
final_pil = ImageEnhance.Contrast(final_pil).enhance(1 + hdr_contrast)
if hdr_enhance_color > 0:
final_pil = ImageEnhance.Color(final_pil).enhance(1 + hdr_enhance_color * 0.2)
# Возвращаем HDR в рабочий массив для финального смешивания
img_np = np.array(final_pil).astype(np.float32) / 255.0
# --- 6. GLOBAL INTENSITY BLEND ---
if intensity < 1.0:
img_np = orig_np * (1.0 - intensity) + img_np * intensity
# Возврат в тензор
out_tensor = torch.from_numpy(img_np)
result_batch.append(out_tensor)
pbar.update(1)
progress_bar_reset(pbar)
return (torch.stack(result_batch),)
requirements.txt
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@@ -4,3 +4,4 @@ opencv-python>=4.7.0.72
numpy
segment_anything
ultralytics
colour-science