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V 2.0.0 - Rasterix - film halation

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DESKTOP-TVBJISQ\Primere
2026-03-24 21:30:46 +01:00
parent 6dd0417fab
commit 2ab2e39a53
2 changed files with 109 additions and 34 deletions
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Nodes/Dashboard.py
+3 -2
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@@ -2214,6 +2214,7 @@ class PrimereRasterix:
"film_rendering": (list(FILM_PRESETS.keys()), {"default": "kodak_kodachrome_64_CF"}),
"film_rendering_intensity": ("FLOAT", {"default": 100, "min": 0, "max": 200, "step": 1}),
"iso_grain": ("BOOLEAN", {"default": False, "label_off": "Ignore ISO grain", "label_on": "Add ISO grain"}),
"halation": ("BOOLEAN", {"default": False, "label_off": "Ignore halation", "label_on": "Add halation"}),
"use_selective_tone": ("BOOLEAN", {"default": False, "label_off": "Ignore selective tone", "label_on": "Apply selective tone"}),
"selective_tone_value": ("FLOAT", {"default": 0, "min": -100, "max": 100, "step": 1}),
@@ -2273,7 +2274,7 @@ class PrimereRasterix:
}
}
def primere_rasterix(self, concepts, models, image, precision, auto_normalize, auto_levels_threshold, normalize_midpeaks, peak_width, auto_gamma, gamma_target, use_white_balance, wb_temperature, wb_tint, use_blur, blur_type, blur_intensity, blur_radius, angle, bilateral_edge_sensitivity, blur_edge_only, edge_threshold, use_smart_lighting, smart_lighting, use_brightness_contrast, brightness, contrast, use_legacy, use_film_rendering, film_rendering, film_rendering_intensity, iso_grain, use_selective_tone, selective_tone_value, selective_tone_zone, selective_tone_separation, selective_tone_strength, use_color_balance, color_balance_cyan_red, color_balance_magenta_green, color_balance_yellow_blue, color_balance_tone, color_balance_preserve_luminosity, color_balance_separation, use_hsl, hsl_hue, hsl_saturation, hsl_lightness, hsl_vibrance, hsl_channel, hsl_channel_width, hsl_skin_protection, use_shade_detailer, shade_level, shade_radius, detail_mode, shade_strength, use_ai_detection_bypasser, adb_freq_strength, adb_variance_strength, adb_unsharp_percent, adb_jpeg_cycles, use_level_endpoints, black_offset, white_offset, skip_if_no_clip, normalize_gaps, dither_quantization, adaptive_dither_strength, error_diffusion, show_histogram=False, histogram_source=False, histogram_channel="RGB", histogram_style="gradient", model_concept=None, model_name=None):
def primere_rasterix(self, concepts, models, image, precision, auto_normalize, auto_levels_threshold, normalize_midpeaks, peak_width, auto_gamma, gamma_target, use_white_balance, wb_temperature, wb_tint, use_blur, blur_type, blur_intensity, blur_radius, angle, bilateral_edge_sensitivity, blur_edge_only, edge_threshold, use_smart_lighting, smart_lighting, use_brightness_contrast, brightness, contrast, use_legacy, use_film_rendering, film_rendering, film_rendering_intensity, iso_grain, halation, use_selective_tone, selective_tone_value, selective_tone_zone, selective_tone_separation, selective_tone_strength, use_color_balance, color_balance_cyan_red, color_balance_magenta_green, color_balance_yellow_blue, color_balance_tone, color_balance_preserve_luminosity, color_balance_separation, use_hsl, hsl_hue, hsl_saturation, hsl_lightness, hsl_vibrance, hsl_channel, hsl_channel_width, hsl_skin_protection, use_shade_detailer, shade_level, shade_radius, detail_mode, shade_strength, use_ai_detection_bypasser, adb_freq_strength, adb_variance_strength, adb_unsharp_percent, adb_jpeg_cycles, use_level_endpoints, black_offset, white_offset, skip_if_no_clip, normalize_gaps, dither_quantization, adaptive_dither_strength, error_diffusion, show_histogram=False, histogram_source=False, histogram_channel="RGB", histogram_style="gradient", model_concept=None, model_name=None):
pil_img = utility.tensor_to_image(image)
pil_img_input = pil_img.copy()
@@ -2296,7 +2297,7 @@ class PrimereRasterix:
pil_img = img_brightness_contrast.img_brightness_contrast(image=pil_img, brightness=brightness, contrast=contrast, use_legacy=use_legacy)
if use_film_rendering and film_rendering_intensity != 0:
pil_img = img_film_rendering.img_film_rendering(image=pil_img, rendering=film_rendering, intensity=film_rendering_intensity, add_grain=iso_grain)
pil_img = img_film_rendering.img_film_rendering(image=pil_img, rendering=film_rendering, intensity=film_rendering_intensity, add_grain=iso_grain, add_halation=halation)
st_data = rasterix_data.get('selective_tone', {})
if use_selective_tone and st_data:
components/images/img_film_rendering.py
+106 -32
View File
@@ -652,6 +652,7 @@ def img_film_rendering(
rendering: str = "kodak_kodachrome_64_CF",
intensity: float = 100,
add_grain: bool = False,
add_halation: bool = False,
) -> Image.Image:
if intensity == 0:
return image.convert("RGB")
@@ -701,6 +702,11 @@ def img_film_rendering(
result = orig + blend * (arr_out - orig)
result = np.clip(result, 0.0, 1.0)
# Apply Halation first (before grain) because light scatters in the emulsion
# before the physical grain structure is fully developed/perceived
if add_halation:
result = _apply_halation(result, preset_base, H, W)
if add_grain:
result = _apply_grain(result, preset_base, H, W)
@@ -753,18 +759,18 @@ def _analyse_image(arr: np.ndarray) -> dict:
"is_desaturated": mean_saturation < 0.08,
}
def _adapt_preset(preset: dict, analysis: dict) -> dict:
def _adapt_preset(preset: dict, analysis: dict) -> dict:
import copy
p = copy.deepcopy(preset)
median = analysis["median_lum"]
std = analysis["lum_std"]
median = analysis["median_lum"]
std = analysis["lum_std"]
hi_frac = analysis["highlight_fraction"]
sh_frac = analysis["shadow_fraction"]
cast = analysis["dominant_cast"]
desat = analysis["is_desaturated"]
flat = analysis["is_flat"]
cast = analysis["dominant_cast"]
desat = analysis["is_desaturated"]
flat = analysis["is_flat"]
if "hd" in p:
if flat:
@@ -805,15 +811,26 @@ def _adapt_preset(preset: dict, analysis: dict) -> dict:
if "bias" in p and not p.get("bw", False):
bias = np.array(p["bias"], dtype=np.float32)
compensation = cast * 0.30
bias = np.clip(bias - compensation, 0.7, 1.4)
p["bias"] = tuple(float(v) for v in bias)
if desat and "bias" in p and not p.get("bw", False):
bias = np.array(p["bias"], dtype=np.float32)
deviation = bias - 1.0
bias = np.clip(1.0 + deviation * 1.5, 0.7, 1.4)
p["bias"] = tuple(float(v) for v in bias)
# 1. Cast compensation
compensation = cast * 0.30
bias = bias - compensation
# 2. LAB SCANNER AUTO-EXPOSURE (Using the 'median' variable)
# Gently push dark images and pull bright images
if median < 0.35:
push = (0.35 - median) * 0.75
bias += push
elif median > 0.65:
pull = (median - 0.65) * 0.75
bias -= pull
# 3. Desaturation compensation
if desat:
deviation = bias - 1.0
bias = 1.0 + deviation * 1.5
p["bias"] = tuple(float(v) for v in np.clip(bias, 0.7, 1.4))
return p
@@ -866,24 +883,24 @@ def _make_grain_params(preset: dict, H: int, W: int) -> dict:
"midtone_peak": 0.4,
}
def _apply_grain(arr: np.ndarray, preset: dict, H: int, W: int) -> np.ndarray:
def _apply_grain(arr: np.ndarray, preset: dict, H: int, W: int) -> np.ndarray:
from scipy.ndimage import gaussian_filter
params = _make_grain_params(preset, H, W)
iso = preset.get("iso", 400)
gt = params["grain_type"]
cm = params["color_mode"]
params = _make_grain_params(preset, H, W)
iso = preset.get("iso", 400) # Now actively used for chroma correlation
gt = params["grain_type"]
cm = params["color_mode"]
rng = np.random.default_rng(None)
sigma = (params["intensity"] / 255.0 * 40.0) / 255.0
gs = params["grain_size"]
mp = params["midtone_peak"]
gs = params["grain_size"]
mp = params["midtone_peak"]
lum = 0.299*arr[...,0] + 0.587*arr[...,1] + 0.114*arr[...,2]
lum = 0.299 * arr[..., 0] + 0.587 * arr[..., 1] + 0.114 * arr[..., 2]
bell = np.exp(-0.5 * ((lum - mp) / 0.28) ** 2)
shadow_mask = np.clip(1.0 - lum / (mp + 1e-6), 0, 1)
shadow_mask = np.clip(1.0 - lum / (mp + 1e-6), 0, 1)
highlight_mask = np.clip((lum - mp) / (1.0 - mp + 1e-6), 0, 1)
lum_mask = bell * (1.0 + shadow_mask * (params["shadow_strength"] - 1.0) + highlight_mask * (params["highlight_strength"] - 1.0))
lum_mask = np.clip(lum_mask, 0, None)
@@ -896,8 +913,8 @@ def _apply_grain(arr: np.ndarray, preset: dict, H: int, W: int) -> np.ndarray:
elif gt == "organic":
coarse = gaussian_filter(
rng.standard_normal(shape).astype(np.float32), sigma=gs * 2.0)
fine = gaussian_filter(raw, sigma=gs * 0.3)
raw = coarse * 0.6 + fine * 0.4
fine = gaussian_filter(raw, sigma=gs * 0.3)
raw = coarse * 0.6 + fine * 0.4
elif gt == "fine":
raw = gaussian_filter(raw, sigma=max(0.3, gs * 0.2))
return raw
@@ -906,13 +923,21 @@ def _apply_grain(arr: np.ndarray, preset: dict, H: int, W: int) -> np.ndarray:
base = make_noise((H, W))
nr = ng = nb = base
else:
nr = make_noise((H, W))
nb = make_noise((H, W))
if gs > 0.6 and gt == "gaussian":
ng = gaussian_filter(rng.standard_normal((H,W)).astype(np.float32),
sigma=gs * 0.35)
else:
ng = make_noise((H, W))
# ISO-BASED CHROMA CORRELATION (Using the 'iso' variable)
# Low ISO = highly correlated channels (monochromatic-ish grain)
# High ISO = uncorrelated channels (colorful, blotchy dye clouds)
correlation = float(np.clip(1.0 - (iso / 1600.0), 0.3, 0.9))
base_lum = make_noise((H, W))
nr = base_lum * correlation + make_noise((H, W)) * (1.0 - correlation)
ng = base_lum * correlation + make_noise((H, W)) * (1.0 - correlation)
nb = base_lum * correlation + make_noise((H, W)) * (1.0 - correlation)
# Normalize variance so the overall intensity matches the sigma requested
norm_factor = 1.0 / np.sqrt(correlation ** 2 + (1.0 - correlation) ** 2)
nr *= norm_factor
ng *= norm_factor
nb *= norm_factor
if params["color_tint"] == "cool":
tr, tg, tb = 0.80, 0.95, 1.25
@@ -925,6 +950,55 @@ def _apply_grain(arr: np.ndarray, preset: dict, H: int, W: int) -> np.ndarray:
out[..., 2] = np.clip(arr[..., 2] + nb * sigma * lum_mask * tb, 0, 1)
return out
def _apply_halation(arr: np.ndarray, preset: dict, H: int, W: int) -> np.ndarray:
from scipy.ndimage import gaussian_filter
# Dynamically scale the glow radius based on image size (crucial for ComfyUI upscales)
area_scale = np.sqrt((H * W) / (1920 * 1280))
base_radius = 8.0 * area_scale
# Check the preset to determine halation character
desc = preset.get("desc", "").lower()
is_cinema = "cinema" in desc or "vision3" in desc
is_bw = preset.get("bw", False)
# Cinema films usually have removed rem-jet backings when cross-processed,
# or naturally stronger halation. Standard film has less.
strength = 0.55 if is_cinema else 0.25
# 1. Isolate the absolute brightest spots (luminance > 80%)
lum = 0.299 * arr[..., 0] + 0.587 * arr[..., 1] + 0.114 * arr[..., 2]
threshold = 0.80
# Soft mask to ensure smooth roll-off into the glow
hi_mask = np.clip((lum - threshold) / (1.0 - threshold + 1e-6), 0.0, 1.0)
# Square the mask to tightly restrict the core of the halation
bright_spots = arr * (hi_mask ** 2)[..., None]
out = arr.copy()
if is_bw:
# B&W halation is just a diffuse white/luma glow in the silver halides
blur = gaussian_filter(bright_spots[..., 0], sigma=base_radius)
out[..., 0] = np.clip(out[..., 0] + blur * strength, 0.0, 1.0)
out[..., 1] = np.clip(out[..., 1] + blur * strength, 0.0, 1.0)
out[..., 2] = np.clip(out[..., 2] + blur * strength, 0.0, 1.0)
else:
# Color halation is predominantly red, with a tiny bit of green for a warm orange roll-off.
# Blue scatters the least.
r_blur = gaussian_filter(bright_spots[..., 0], sigma=base_radius)
g_blur = gaussian_filter(bright_spots[..., 1], sigma=base_radius * 0.6)
b_blur = gaussian_filter(bright_spots[..., 2], sigma=base_radius * 0.2)
# Additive blend back onto the original image
out[..., 0] = np.clip(out[..., 0] + r_blur * strength * 1.2, 0.0, 1.0)
out[..., 1] = np.clip(out[..., 1] + g_blur * strength * 0.3, 0.0, 1.0)
out[..., 2] = np.clip(out[..., 2] + b_blur * strength * 0.0, 0.0, 1.0)
return out
def _hd_curve(x: np.ndarray, toe: float, gamma: float, shoulder: float) -> np.ndarray:
x = np.clip(x, 0.0, 1.0)