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CosmicLaca-ComfyUI_Primere_…/Nodes/Rasterix.py
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from ..components.tree import TREE_RASTERIX
from ..components.tree import PRIMERE_ROOT
import random
import folder_paths
from ..components.images import img_shade_level as img_shade_level
from ..components.images import img_brightness_contrast as img_brightness_contrast
from ..components.images import img_color_balance as img_color_balance
from ..components.images import img_hue_saturation as img_hue_saturation
from ..components.images import img_levels_auto as img_levels_auto
from ..components.images import isgen_detect_ext_full as isgen_detect_ext_full
from ..components.images import img_film_grain as img_film_grain
from ..components.images import img_blur as img_blur
from ..components.images import img_selective_tone as img_selective_tone
from ..components.images import img_smart_lighting as img_smart_lighting
from ..components.images import img_white_balance as img_white_balance
from ..components.images import img_film_rendering as img_film_rendering
from ..components.images.img_film_rendering import FILM_PRESETS
from ..components.images import img_lens_effects as img_lens_effects
from ..components.images import img_levels_compress as img_levels_compress
from ..components.images import img_dithering as img_dithering
from ..components.images import histogram as histogram
from ..components.images import img_posterize as img_posterize
from ..components.images import img_solarization_bw as img_solarization_bw
from ..components.images import img_clarity as img_clarity
from ..components.images import img_dehaze as img_dehaze
from ..components.images import img_local_laplacian as img_local_laplacian
from ..components.images import img_frequency_separation as img_frequency_separation
from ..components.images import img_filmic_curve as img_filmic_curve
from ..components.images import img_lut3d as img_lut3d
from ..components.images import img_edge_jitter as img_edge_jitter
from ..components.images import img_depth_blur as img_depth_blur
from ..components import utility
from .Dashboard import PrimereModelConceptSelector as PrimereModelConceptSelector
import os
from server import PromptServer
FILM_PRESETS_BY_TYPE = img_film_rendering.list_presets_by_type()
FILM_TYPES = ["All"] + sorted(FILM_PRESETS_BY_TYPE.keys())
class PrimereRasterix:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_rasterix"
CATEGORY = TREE_RASTERIX
OUTPUT_NODE = True
MODELLIST = PrimereModelConceptSelector.MODELLIST
CONCEPT_LIST = PrimereModelConceptSelector.CONCEPT_LIST
FILM_TYPES = FILM_TYPES
FILM_PRESETS_BY_TYPE = FILM_PRESETS_BY_TYPE
LUT_DIR = os.path.join(PRIMERE_ROOT, 'components', 'images', 'luts')
SECTION_TITLES = [
{"before": "concepts", "name": "rasterix_main", "title": "🧭 Project Setup", "color": "#5C3D34", "text_color": "#EAF1F8", "label": "Choose model concept/model for save-load profiles and set precision for the full pipeline."},
{"after": "precision", "name": "rasterix_auto_levels", "title": "🎚 Auto Levels & Gamma", "color": "#6A4A2A", "text_color": "#EAF1F8", "label": "Photoshop-style auto levels with threshold protection and optional target gamma alignment. Inspired by Adobe Photoshop."},
{"after": "gamma_target", "name": "rasterix_white_balance", "title": "🔦 White Balance", "color": "#6A4A2A", "text_color": "#EAF1F8", "label": "Correct temperature and tint first to establish a neutral color baseline for later grading. Inspired by Adobe Camera Raw and DxO Photolab."},
{"after": "wb_tint", "name": "rasterix_smart_lighting", "title": "💡 Smart Lighting", "color": "#6A4A2A", "text_color": "#EAF1F8", "label": "Adaptive light shaping to recover perceived depth and readability before local effects. Inspired by DxO Photolab"},
{"after": "smart_lighting", "name": "rasterix_dehaze", "title": "🌫 Atmosphere: Dehaze", "color": "#3E5C4B", "text_color": "#EAF1F8", "label": "Reduce haze and veiling glow while preserving natural contrast and color balance. Inspired by Adobe Lightroom Dehaze."},
{"after": "dehaze_contrast", "name": "rasterix_depth_blur", "title": "🌀 Atmosphere: Depth Blur", "color": "#3E5C4B", "text_color": "#EAF1F8", "label": "Depth-guided lens blur to separate subject and background with controllable focus falloff."},
{"after": "depth_gamma", "name": "rasterix_blur", "title": "🫗 Atmosphere: Creative Blur", "color": "#3E5C4B", "text_color": "#EAF1F8", "label": "Apply additional blur styles for softness, abstraction, or cinematic diffusion."},
{"after": "edge_threshold", "name": "rasterix_brightness_contrast", "title": "🧊 Tone: Brightness & Contrast", "color": "#405985", "text_color": "#EAF1F8", "label": "Global tone shaping for exposure feel and contrast punch after atmospheric corrections. Inspired by Adobe Photoshop."},
{"after": "use_legacy", "name": "rasterix_portrait_retouch", "title": "🪒 Tone: Portrait Retouch", "color": "#405985", "text_color": "#EAF1F8", "label": "Frequency-based skin and texture workflow for gentle portrait cleanup and separation. Inspired by professional Photoshop retouch workflows."},
{"after": "blend_mode", "name": "rasterix_local_laplacian", "title": "🧱 Tone: Edge-Aware Pyramid", "color": "#405985", "text_color": "#EAF1F8", "label": "Local Laplacian contrast/detail enhancement with strong edge preservation."},
{"after": "levels", "name": "rasterix_analog_film", "title": "🎞 Creative: Analog Film / CCD", "color": "#3B5E68", "text_color": "#EAF1F8", "label": "Stylized film and sensor-era rendering for mood, palette, and texture character. Inspired by DxO."},
{"after": "photo_paper", "name": "rasterix_lut_reader", "title": "📷 Creative: LUT .cube Reader", "color": "#3B5E68", "text_color": "#EAF1F8", "label": "Load and blend LUT looks for fast creative direction and consistent show style. Inspired by Blackmagic DaVinci Resolve and DxO."},
{"after": "color_space", "name": "rasterix_filmic_camera", "title": "🎥 Creative: Filmic Camera Curve", "color": "#3B5E68", "text_color": "#EAF1F8", "label": "Camera-like highlight roll-off and tonal response for cinematic dynamic range behavior. Inspired by Adobe Camera Raw."},
{"after": "pivot", "name": "rasterix_selective_tone", "title": "🎛 Color: Selective Tone Zones", "color": "#6A5636", "text_color": "#EAF1F8", "label": "Zone-based tonal pushes for highlights, midtones, shadows, and blacks. Inspired by DxO Photolab"},
{"after": "selective_tone_strength", "name": "rasterix_color_balance", "title": "⚖ Color: Balance Wheels", "color": "#6A5636", "text_color": "#EAF1F8", "label": "Color-balance style adjustments per tonal range with luminosity preservation options. Inspired by DaVinci Resolve and Photoshop color wheels."},
{"after": "color_balance_separation", "name": "rasterix_hsl", "title": "🌈 Color: HSL Sculpting", "color": "#6A5636", "text_color": "#EAF1F8", "label": "Hue, saturation, lightness, and vibrance targeting by color channel. Inspired by Adobe Lightroom and Photoshop HSL panel."},
{"after": "hsl_skin_protection", "name": "rasterix_shade_detailer", "title": "💎 Detail: Microcontrast", "color": "#554267", "text_color": "#EAF1F8", "label": "Fine local contrast shaping to emphasize texture and perceived detail. Inspired by DxO PhotoLab microcontrast tools."},
{"after": "shade_strength", "name": "rasterix_clarity", "title": "🔍 Detail: Midtone Clarity", "color": "#554267", "text_color": "#EAF1F8", "label": "Midtone-focused clarity enhancement for crispness without excessive global contrast. Inspired by Adobe Lightroom Clarity."},
{"after": "edge_preservation", "name": "rasterix_endpoints", "title": "🔛 Output: Black/White Endpoints", "color": "#5A603E", "text_color": "#EAF1F8", "label": "Set endpoint compression and clipping behavior for final output anchoring. Inspired by Adobe Photoshop Levels."},
{"after": "skip_if_no_clip", "name": "rasterix_dithering", "title": "🧩 Output: Dithering & Diffusion", "color": "#5A603E", "text_color": "#EAF1F8", "label": "Reduce banding and smooth gradients using dither and error diffusion tools. Inspired by Floyd-Steinberg error diffusion."},
{"after": "error_diffusion", "name": "rasterix_histogram", "title": "📊 Analysis: Histogram", "color": "#35586A", "text_color": "#EAF1F8", "label": "View channel histograms for fast clipping, balance, and tonal distribution checks. Inspired by Adobe Photoshop (and all other) Histogram."},
]
@classmethod
def _list_luts(cls):
lut_entries = ["None"]
if not os.path.exists(cls.LUT_DIR):
return lut_entries
for f in sorted(os.listdir(cls.LUT_DIR)):
full_path = os.path.join(cls.LUT_DIR, f)
if os.path.isfile(full_path) and f.lower().endswith(".cube"):
lut_entries.append(f)
for d in sorted(os.listdir(cls.LUT_DIR)):
subdir = os.path.join(cls.LUT_DIR, d)
if os.path.isdir(subdir):
for f in sorted(os.listdir(subdir)):
if f.lower().endswith(".cube"):
lut_entries.append(f"{d}/{f}")
return lut_entries
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"concepts": (["Auto"] + cls.CONCEPT_LIST,),
"models": (["Auto"] + cls.MODELLIST,),
"image": ("IMAGE", {"forceInput": True}),
"precision": ("BOOLEAN", {"default": False, "label_off": "8 bit", "label_on": "16 bit"}),
"auto_normalize": ("BOOLEAN", {"default": False, "label_off": "No auto levels", "label_on": "Apply auto levels"}),
"auto_levels_threshold": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.1}),
"auto_gamma": ("BOOLEAN", {"default": False, "label_on": "Auto gamma: ON", "label_off": "Auto gamma:: OFF"}),
"gamma_target": ("FLOAT", {"default": 128.0, "min": 0.0, "max": 255.0, "step": 0.1}),
"use_white_balance": ("BOOLEAN", {"default": False, "label_off": "Ignore white balance", "label_on": "Apply white balance"}),
"wb_temperature": ("FLOAT", {"default": 6500, "min": 2000, "max": 12000, "step": 100}),
"wb_tint": ("FLOAT", {"default": 0, "min": -100, "max": 100, "step": 1}),
"use_smart_lighting": ("BOOLEAN", {"default": False, "label_off": "Ignore smart lightning", "label_on": "Apply smart lightning"}),
"smart_lighting": ("FLOAT", {"default": 0, "min": 0, "max": 100, "step": 1}),
"use_dehaze": ("BOOLEAN", {"default": False, "label_off": "Ignore dehaze", "label_on": "Apply dehaze"}),
"strength": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 2.0, "step": 0.01}),
"dehaze_radius": ("INT", {"default": 15, "min": 3, "max": 100, "step": 1}),
"omega": ("FLOAT", {"default": 0.95, "min": 0.5, "max": 1.0, "step": 0.01}),
"t0": ("FLOAT", {"default": 0.1, "min": 0.01, "max": 0.5, "step": 0.01}),
"dehaze_contrast": ("FLOAT", {"default": 1.05, "min": 0.5, "max": 2.0, "step": 0.01}),
"use_depth_blur": ("BOOLEAN", {"default": False, "label_off": "Ignore depth blur", "label_on": "Apply depth blur"}),
"auto_optimize": ("BOOLEAN", {"default": False, "label_off": "Use custom inputs", "label_on": "Optimize settings by focus"}),
"use_DA_v3": ("BOOLEAN", {"default": False, "label_off": "Depth-anything V2", "label_on": "Depth-anything V3"}),
"focus_depth": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"depth_range": ("FLOAT", {"default": 0.200, "min": 0.001, "max": 1.000, "step": 0.001}),
"max_blur": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 50.0, "step": 0.5}),
"depth_gamma": ("FLOAT", {"default": 1.00, "min": 0.10, "max": 5.00, "step": 0.01}),
"use_blur": ("BOOLEAN", {"default": False, "label_off": "Ignore blur", "label_on": "Apply blur"}),
"blur_type": (["gaussian", "box", "motion", "bilateral", "lens"], {"default": "bilateral"}),
"blur_intensity": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 5.0, "step": 0.1}),
"blur_radius": ("FLOAT", {"default": 2.0, "min": 0.5, "max": 50.0, "step": 0.5}),
"angle": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 360.0, "step": 1.0}),
"bilateral_edge_sensitivity": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"blur_edge_only": ("BOOLEAN", {"default": False, "label_off": "Full image blur", "label_on": "Flat areas only, edges protected"}),
"edge_threshold": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"use_brightness_contrast": ("BOOLEAN", {"default": False, "label_off": "Ignore brightness-contrast", "label_on": "Apply brightness-contrast"}),
"brightness": ("FLOAT", {"default": 0, "min": -150, "max": 150, "step": 1}),
"contrast": ("FLOAT", {"default": 0, "min": -50, "max": 100, "step": 1}),
"use_legacy": ("BOOLEAN", {"default": False, "label_off": "Use non-linear shift", "label_on": "Use adaptive offset"}),
"use_frequency_separation": ("BOOLEAN", {"default": False, "label_off": "Ignore frequency separation", "label_on": "Apply frequency separation"}),
"radius": ("FLOAT", {"default": 3.0, "min": 0.5, "max": 20.0, "step": 0.1}),
"low_freq_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 3.0, "step": 0.01}),
"high_freq_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 3.0, "step": 0.01}),
"blend_mode": (["add", "multiply", "overlay"], {"default": "add"}),
"use_local_laplacian": ("BOOLEAN", {"default": False, "label_off": "Ignore local laplacian", "label_on": "Apply local laplacian"}),
"sigma": ("FLOAT", {"default": 1.0, "min": 0.5, "max": 5.0, "step": 0.1}),
"laplacian_contrast": ("FLOAT", {"default": 1.2, "min": 0.5, "max": 3.0, "step": 0.01}),
"detail": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 3.0, "step": 0.01}),
"levels": ("INT", {"default": 8, "min": 4, "max": 32, "step": 1}),
"use_film_rendering": ("BOOLEAN", {"default": False, "label_off": "Ignore film rendering", "label_on": "Apply film rendering"}),
"film_type": (cls.FILM_TYPES, {"default": "All"}),
"film_rendering": (list(FILM_PRESETS.keys()), {"default": list(FILM_PRESETS.keys())[0]}),
"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"}),
"expiration_years": ("INT", {"default": 0, "min": 0, "max": 30, "step": 1}),
"use_photo_paper": ("BOOLEAN", {"default": False, "label_off": "Ignore H/B photo paper", "label_on": "Apply H/B photo paper"}),
"photo_paper": ("BOOLEAN", {"default": False, "label_off": "Soft paper", "label_on": "Hard paper"}),
"use_lut": ("BOOLEAN", {"default": False, "label_off": "Ignore LUT", "label_on": "Apply LUT"}),
"lut_file": (cls._list_luts(),),
"intensity": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.01}),
"color_space": (["sRGB", "linear"], {"default": "sRGB"}),
"use_filmic": ("BOOLEAN", {"default": False, "label_off": "Ignore filmic", "label_on": "Apply filmic"}),
"curve_type": (["filmic", "log"], {"default": "filmic"}),
"filmic_contrast": ("FLOAT", {"default": 1.0, "min": 0.5, "max": 2.0, "step": 0.01}),
"highlight_rolloff": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"shadow_lift": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 0.5, "step": 0.01}),
"pivot": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"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}),
"selective_tone_zone": (["highlights", "midtones", "shadows", "blacks"], {"default": "midtones"}),
"selective_tone_separation": ("FLOAT", {"default": 50, "min": 0, "max": 100, "step": 1}),
"selective_tone_strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"use_color_balance": ("BOOLEAN", {"default": False, "label_off": "Ignore color balance", "label_on": "Apply color balance"}),
"color_balance_cyan_red": ("FLOAT", {"default": 0, "min": -100, "max": 100, "step": 1}),
"color_balance_magenta_green": ("FLOAT", {"default": 0, "min": -100, "max": 100, "step": 1}),
"color_balance_yellow_blue": ("FLOAT", {"default": 0, "min": -100, "max": 100, "step": 1}),
"color_balance_tone": (["highlights", "midtones", "shadows"], {"default": "midtones"}),
"color_balance_preserve_luminosity": ("BOOLEAN", {"default": False, "label_off": "Modify luminosity", "label_on": "Restore original luminosity"}),
"color_balance_separation": ("FLOAT", {"default": 50, "min": 0, "max": 100, "step": 1}),
"use_hsl": ("BOOLEAN", {"default": False, "label_off": "Ignore HSL", "label_on": "Apply HSL"}),
"hsl_hue": ("FLOAT", {"default": 0, "min": -180, "max": 180, "step": 1}),
"hsl_saturation": ("FLOAT", {"default": 0, "min": -100, "max": 100, "step": 1}),
"hsl_lightness": ("FLOAT", {"default": 0, "min": -100, "max": 100, "step": 1}),
"hsl_vibrance": ("FLOAT", {"default": 0, "min": -100, "max": 100, "step": 1}),
"hsl_channel": (["master", "red", "green", "blue"], {"default": "master"}),
"hsl_channel_width": ("FLOAT", {"default": 50, "min": 0, "max": 100, "step": 1}),
"hsl_skin_protection": ("BOOLEAN", {"default": True, "label_off": "Vibrance affects skin tones", "label_on": "Skin tones protected from vibrance"}),
"use_shade_detailer": ("BOOLEAN", {"default": False, "label_off": "Ignore shade detailer", "label_on": "Apply shade detailer"}),
"shade_level": ("FLOAT", {"default": 0, "min": -100, "max": 100, "step": 1}),
"shade_radius": ("FLOAT", {"default": 0, "min": 0, "max": 50, "step": 0.5}),
"detail_mode": (["fine", "medium", "broad"], {"default": "medium"}),
"shade_strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"use_clarity": ("BOOLEAN", {"default": False, "label_off": "Ignore clarity", "label_on": "Apply clarity"}),
"clarity_strength": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 3.0, "step": 0.01}),
"clarity_radius": ("FLOAT", {"default": 2.0, "min": 0.5, "max": 10.0, "step": 0.1}),
"midtone_range": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 1.0, "step": 0.01}),
"edge_preservation": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.01}),
"use_level_endpoints": ("BOOLEAN", {"default": False, "label_off": "Ignore endpoint offset", "label_on": "Apply endpoint offset"}),
"black_offset": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 25.0, "step": 0.1}),
"white_offset": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 25.0, "step": 0.1}),
"skip_if_no_clip": ("BOOLEAN", {"default": False, "label_off": "Offset all values", "label_on": "Skip if no clips"}),
"normalize_gaps": ("BOOLEAN", {"default": False, "label_on": "Anti-comb filter: ON", "label_off": "Anti-comb filter: OFF"}),
"normalize_midpeaks": ("BOOLEAN", {"default": False, "label_on": "Anti-spike filter: ON", "label_off": "Anti-spike filter: OFF"}),
"peak_width": ("INT", {"default": 3, "min": 1, "max": 10, "step": 1}),
"dither_quantization": ("BOOLEAN", {"default": False, "label_off": "Dither quantization OFF", "label_on": "Dither quantization ON"}),
"adaptive_dither_strength": ("BOOLEAN", {"default": False, "label_off": "Keep dither strength", "label_on": "Increase dither strength"}),
"error_diffusion": ("BOOLEAN", {"default": False, "label_off": "Error diffusion OFF", "label_on": "Error diffusion ON"}),
"show_histogram": ("BOOLEAN", {"default": False, "label_off": "Ignore histogram", "label_on": "Create histogram"}),
"histogram_source": ("BOOLEAN", {"default": False, "label_off": "Show output histogram", "label_on": "Show input histogram"}),
"histogram_channel": (["RGB", "RED", "GREEN", "BLUE"], {"default": "RGB"}),
"histogram_style": (["bars", "lines", "waveform", "heatmap", "stacked", "luma", "parade", "gradient", "glow", "dots", "step", "log", "percentile", "inverse"], {"default": "bars"}),
},
"optional": {
"model_concept": ("STRING", {"default": None, "forceInput": True}),
"model_name": ("CHECKPOINT_NAME", {"default": None, "forceInput": True}),
"seed": ("INT", {"default": 0, "min": 0, "max": utility.MAX_SEED, "forceInput": True}),
},
"hidden": {
"id": "UNIQUE_ID",
}
}
def primere_rasterix(self, **kwargs):
concepts = kwargs.get('concepts', 'Auto')
models = kwargs.get('models', 'Auto')
model_concept = kwargs.get('model_concept', None)
model_name = kwargs.get('model_name', None)
active_concept = model_concept if concepts == "Auto" else concepts
active_display = active_concept
auto_runtime_mode = concepts == "Auto" and models == "Auto"
if auto_runtime_mode:
raw_model = model_name
model_key = os.path.splitext(os.path.basename(raw_model))[0] if raw_model else None
json_path = os.path.join(PRIMERE_ROOT, 'front_end', 'rasterix_settings.json')
concept_data = utility.json2tuple(json_path)
if model_key and concept_data and model_key in concept_data:
lookup_key = model_key
active_display = model_key
else:
lookup_key = active_concept
active_display = active_concept
if not concept_data or lookup_key not in concept_data:
PromptServer.instance.send_sync("primere.rasterix_setting", {"status": "missing", "concept": active_concept})
else:
saved = concept_data[lookup_key]
for k, v in saved.items():
if k in kwargs:
kwargs[k] = v
image = kwargs.get('image')
precision = kwargs.get('precision', False)
seed = kwargs.get('seed', 0)
auto_normalize = kwargs.get('auto_normalize', False)
auto_levels_threshold = kwargs.get('auto_levels_threshold', 0.2)
normalize_midpeaks = kwargs.get('normalize_midpeaks', False)
peak_width = kwargs.get('peak_width', 3)
auto_gamma = kwargs.get('auto_gamma', False)
gamma_target = kwargs.get('gamma_target', 128.0)
use_white_balance = kwargs.get('use_white_balance', False)
wb_temperature = kwargs.get('wb_temperature', 6500)
wb_tint = kwargs.get('wb_tint', 0)
use_depth_blur = kwargs.get('use_depth_blur', False)
auto_optimize = kwargs.get('auto_optimize', False)
use_DA_v3 = kwargs.get('use_DA_v3', False)
focus_depth = kwargs.get('focus_depth', 0.5)
depth_range = kwargs.get('depth_range', 0.200)
max_blur = kwargs.get('bilateral_edge_sensitivity', 8.0)
depth_gamma = kwargs.get('depth_gamma', 1.0)
use_blur = kwargs.get('use_blur', False)
blur_type = kwargs.get('blur_type', "bilateral")
blur_intensity = kwargs.get('blur_intensity', 0.0)
blur_radius = kwargs.get('blur_radius', 2.0)
angle = kwargs.get('angle', 0.0)
bilateral_edge_sensitivity = kwargs.get('bilateral_edge_sensitivity', 0.5)
blur_edge_only = kwargs.get('blur_edge_only', False)
edge_threshold = kwargs.get('edge_threshold', 0.0)
use_smart_lighting = kwargs.get('use_smart_lighting', False)
smart_lighting = kwargs.get('smart_lighting', 0)
use_dehaze = kwargs.get('use_dehaze', False)
strength = kwargs.get('strength', 0.7)
dehaze_radius = kwargs.get('dehaze_radius', 15)
omega = kwargs.get('omega', 0.95)
t0 = kwargs.get('t0', 0.1)
dehaze_contrast = kwargs.get('dehaze_contrast', 1.05)
use_brightness_contrast = kwargs.get('use_brightness_contrast', False)
brightness = kwargs.get('brightness', 0)
contrast = kwargs.get('contrast', 0)
use_legacy = kwargs.get('use_legacy', False)
use_frequency_separation = kwargs.get('use_frequency_separation', False)
radius = kwargs.get('radius', 3.0)
low_freq_strength = kwargs.get('low_freq_strength', 1.0)
high_freq_strength = kwargs.get('high_freq_strength', 1.0)
blend_mode = kwargs.get('blend_mode', 'add')
use_local_laplacian = kwargs.get('use_local_laplacian', False)
sigma = kwargs.get('sigma', 1.0)
laplacian_contrast = kwargs.get('laplacian_contrast', 1.2)
detail = kwargs.get('detail', 1.0)
levels = kwargs.get('levels', 8)
use_film_rendering = kwargs.get('use_film_rendering', False)
film_type = "All" if auto_runtime_mode else kwargs.get('film_type', "All")
film_rendering = kwargs.get('film_rendering', list(FILM_PRESETS.keys())[0])
film_rendering_intensity = kwargs.get('film_rendering_intensity', 100)
iso_grain = kwargs.get('iso_grain', False)
halation = kwargs.get('halation', False)
expiration_years = kwargs.get('expiration_years', 0)
use_photo_paper = kwargs.get('use_photo_paper', False)
photo_paper = kwargs.get('photo_paper', False)
use_filmic = kwargs.get('use_filmic', False)
curve_type = kwargs.get('curve_type', "filmic")
filmic_contrast = kwargs.get('filmic_contrast', 1.0)
highlight_rolloff = kwargs.get('highlight_rolloff', 0.5)
shadow_lift = kwargs.get('shadow_lift', 0.0)
pivot = kwargs.get('pivot', 0.5)
use_selective_tone = kwargs.get('use_selective_tone', False)
selective_tone_separation = kwargs.get('selective_tone_separation', 50)
selective_tone_strength = kwargs.get('selective_tone_strength', 0.5)
use_color_balance = kwargs.get('use_color_balance', False)
color_balance_preserve_luminosity = kwargs.get('color_balance_preserve_luminosity', False)
color_balance_separation = kwargs.get('color_balance_separation', 50)
use_lut = kwargs.get('use_lut', False)
lut_file = kwargs.get('lut_file', "None")
intensity = kwargs.get('intensity', 1.0)
color_space = kwargs.get('color_space', "sRGB")
use_hsl = kwargs.get('use_hsl', False)
hsl_channel_width = kwargs.get('hsl_channel_width', 50)
hsl_skin_protection = kwargs.get('hsl_skin_protection', True)
use_shade_detailer = kwargs.get('use_shade_detailer', False)
shade_strength = kwargs.get('shade_strength', 0.5)
use_clarity = kwargs.get('use_clarity', False)
clarity_strength = kwargs.get('clarity_strength', 0.5)
clarity_radius = kwargs.get('clarity_radius', 2.0)
midtone_range = kwargs.get('midtone_range', 0.5)
edge_preservation = kwargs.get('edge_preservation', 0.8)
use_level_endpoints = kwargs.get('use_level_endpoints', False)
black_offset = kwargs.get('black_offset', 0.0)
white_offset = kwargs.get('white_offset', 0.0)
skip_if_no_clip = kwargs.get('skip_if_no_clip', False)
normalize_gaps = kwargs.get('normalize_gaps', False)
dither_quantization = kwargs.get('dither_quantization', False)
adaptive_dither_strength = kwargs.get('adaptive_dither_strength', False)
error_diffusion = kwargs.get('error_diffusion', False)
show_histogram = kwargs.get('show_histogram', False)
histogram_source = kwargs.get('histogram_source', False)
histogram_channel = kwargs.get('histogram_channel', "RGB")
histogram_style = kwargs.get('histogram_style', "bars")
node_id = kwargs.get('id', None)
pil_img = utility.tensor_to_image(image)
pil_img_input = pil_img.copy()
rasterix_json_path = os.path.join(PRIMERE_ROOT, 'front_end', 'rasterix.json')
rasterix_data = utility.json2tuple(rasterix_json_path) or {}
if auto_normalize:
pil_img = img_levels_auto.img_levels_auto(image=pil_img, auto_normalize=auto_normalize, threshold=auto_levels_threshold, normalize_gaps=normalize_gaps, normalize_midpeaks=False, peak_width=peak_width, auto_gamma=auto_gamma, gamma_target=gamma_target, precision=precision, seed=seed)
if use_white_balance and (wb_temperature != 6500 or wb_tint != 0):
pil_img = img_white_balance.img_white_balance(image=pil_img, temperature=wb_temperature, tint=wb_tint)
if use_smart_lighting and smart_lighting != 0:
pil_img = img_smart_lighting.img_smart_lighting(image=pil_img, intensity=smart_lighting)
if use_dehaze and strength > 0:
pil_img = img_dehaze.img_dehaze(image=pil_img, strength=strength, radius=dehaze_radius, omega=omega, t0=t0, contrast=dehaze_contrast, precision=precision)
if use_depth_blur and focus_depth > 0 and max_blur > 0:
pil_img = img_depth_blur.img_depth_blur(image=pil_img, focus_depth=focus_depth, depth_range=depth_range, max_blur=max_blur, depth_gamma=depth_gamma, auto_optimize=auto_optimize, use_v3=use_DA_v3)
if use_blur and blur_intensity != 0:
pil_img = img_blur.img_blur(image=pil_img, blur_type=blur_type, intensity=blur_intensity, radius=blur_radius, angle=angle, edge_only=blur_edge_only, bilateral_edge_sensitivity=bilateral_edge_sensitivity, edge_threshold=edge_threshold)
if use_brightness_contrast and (brightness != 0 or contrast != 0):
pil_img = img_brightness_contrast.img_brightness_contrast(image=pil_img, brightness=brightness, contrast=contrast, use_legacy=use_legacy)
if use_frequency_separation:
pil_img = img_frequency_separation.img_frequency_separation(image=pil_img, radius=radius, low_freq_strength=low_freq_strength, high_freq_strength=high_freq_strength, blend_mode=blend_mode)
if use_local_laplacian:
pil_img = img_local_laplacian.img_local_laplacian(image=pil_img, sigma=sigma, contrast=laplacian_contrast, detail=detail, levels=levels)
if film_type != "All":
allowed_presets = self.FILM_PRESETS_BY_TYPE.get(film_type, [])
if allowed_presets and film_rendering not in allowed_presets:
film_rendering = allowed_presets[0]
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, add_halation=halation, expiration_years=expiration_years)
if use_lut and lut_file != "None":
lut_path = os.path.join(self.LUT_DIR, lut_file)
pil_img = img_lut3d.img_lut3d(image=pil_img, lut_path=lut_path, intensity=intensity, input_space=color_space, output_space=color_space)
if use_filmic:
pil_img = img_filmic_curve.img_filmic_curve(image=pil_img, curve_type=curve_type, contrast=filmic_contrast, highlight_rolloff=highlight_rolloff, shadow_lift=shadow_lift, pivot=pivot)
if use_photo_paper:
pil_img = img_solarization_bw.img_solarization_bw(image=pil_img, color_mode=False, strength=0.00, pivot=0.00, sigma=0.01, edge_boost=0.00, edge_radius=0.5, contrast=1, precision=precision, hard_paper=photo_paper, grain_modulation=False, grain_strength=0, grain_scale=1, seed=1)
st_data = rasterix_data.get('selective_tone', {})
if use_selective_tone and st_data:
pil_img = img_selective_tone.img_selective_tone(image=pil_img, channels_data=st_data, separation=selective_tone_separation, strength=selective_tone_strength)
cb_data = rasterix_data.get('color_balance', {})
if use_color_balance and cb_data:
pil_img = img_color_balance.img_color_balance(image=pil_img, channels_data=cb_data, preserve_luminosity=color_balance_preserve_luminosity, separation=color_balance_separation)
hs_data = rasterix_data.get('hue_saturation', {})
if use_hsl and hs_data:
pil_img = img_hue_saturation.img_hue_saturation(image=pil_img, channels_data=hs_data, channel_width=hsl_channel_width, skin_protection=hsl_skin_protection)
shade_data = rasterix_data.get('shade', {})
if use_shade_detailer and shade_data:
for mode, vals in shade_data.items():
lvl = vals.get('shade_level', 0)
if lvl != 0:
rad = vals.get('shade_radius', 0)
pil_img = img_shade_level.img_shade_level(image=pil_img, shade_level=lvl, radius=rad, strength=shade_strength)
if use_clarity and strength != 0:
pil_img = img_clarity.img_clarity(image=pil_img, strength=clarity_strength, radius=clarity_radius, midtone_range=midtone_range, edge_preservation=edge_preservation, precision=precision)
if use_level_endpoints and (black_offset != 0 or white_offset != 0):
pil_img = img_levels_compress.img_levels_compress(image=pil_img, black_offset=black_offset, white_offset=white_offset, skip_if_no_clip=skip_if_no_clip, high_precision=precision)
if dither_quantization or error_diffusion or normalize_midpeaks:
pil_img = img_dithering.img_dithering(image=pil_img, dither_quantization=dither_quantization, adaptive_dither_strength=adaptive_dither_strength, error_diffusion=error_diffusion, normalize_midpeaks=normalize_midpeaks, peak_width=peak_width, high_precision=precision, seed=seed)
histogram.rasterix_hist_cache_store(pil_img_input, pil_img, precision, node_id=node_id)
if show_histogram:
histogram.rasterix_hist_cache_store(pil_img_input, pil_img, precision, node_id=node_id)
active_hist = histogram.rasterix_hist_render_selected(pil_img_input, pil_img, precision, histogram_source, histogram_channel, histogram_style, node_id=node_id)
suffix = ''.join(random.choice("abcdefghijklmnopqrstuvwxyz0123456789") for _ in range(8))
temp_file = f"rasterix_hist_{suffix}.png"
active_hist.save(os.path.join(folder_paths.temp_directory, temp_file), compress_level=1)
return {"ui": {"images": [{"filename": temp_file, "subfolder": "", "type": "temp"}], "active_concept": [active_display]}, "result": (utility.image_to_tensor(pil_img),), }
else:
INVALID_IMAGE_PATH = os.path.join(PRIMERE_ROOT, 'front_end', 'images')
INVALID_IMAGE = os.path.join(INVALID_IMAGE_PATH, "No_histogram_08.jpg")
images = utility.ImageLoaderFromPath(INVALID_IMAGE)
r1 = random.randint(1000, 9999)
temp_filename = f"Primere_ComfyUI_{r1}.png"
os.makedirs(folder_paths.get_temp_directory(), exist_ok=True)
TEMP_FILE = os.path.join(folder_paths.get_temp_directory(), temp_filename)
utility.tensor_to_image(images[0]).save(TEMP_FILE)
return {"ui": {"images": [{"filename": temp_filename, "subfolder": "", "type": "temp"}], "active_concept": [active_display]}, "result": (utility.image_to_tensor(pil_img),),}
class PrimereAutoNormalize:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_auto_normalize"
CATEGORY = TREE_RASTERIX
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"precision": ("BOOLEAN", {"default": False, "label_off": "8 bit", "label_on": "16 bit"}),
"auto_normalize": ("BOOLEAN", {"default": False, "label_off": "No auto levels", "label_on": "Apply auto levels"}),
"auto_levels_threshold": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.1}),
"auto_gamma": ("BOOLEAN", {"default": False, "label_on": "Auto gamma: ON", "label_off": "Auto gamma:: OFF"}),
"gamma_target": ("FLOAT", {"default": 128.0, "min": 0.0, "max": 255.0, "step": 0.1}),
"normalize_gaps": ("BOOLEAN", {"default": False, "label_on": "Anti-comb filter: ON", "label_off": "Anti-comb filter: OFF"}),
"normalize_midpeaks": ("BOOLEAN", {"default": False, "label_on": "Anti-spike filter: ON", "label_off": "Anti-spike filter: OFF"}),
"peak_width": ("INT", {"default": 3, "min": 1, "max": 10, "step": 1}),
},
"optional": {
"seed": ("INT", {"default": 0, "min": 0, "max": utility.MAX_SEED, "forceInput": True}),
}
}
def primere_auto_normalize(self, image, precision, auto_normalize, auto_levels_threshold, auto_gamma, gamma_target, normalize_gaps, normalize_midpeaks, peak_width, seed = None):
pil_img = utility.tensor_to_image(image)
if auto_normalize:
pil_img = img_levels_auto.img_levels_auto(image=pil_img, auto_normalize=auto_normalize, threshold=auto_levels_threshold, normalize_gaps=normalize_gaps, normalize_midpeaks=normalize_midpeaks, peak_width=peak_width, auto_gamma=auto_gamma, gamma_target=gamma_target, precision=precision, seed=seed)
return (utility.image_to_tensor(pil_img),)
class PrimereWhiteBalance:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_white_balance"
CATEGORY = TREE_RASTERIX
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"use_white_balance": ("BOOLEAN", {"default": False, "label_off": "Ignore white balance", "label_on": "Apply white balance"}),
"wb_temperature": ("FLOAT", {"default": 6500, "min": 2000, "max": 12000, "step": 100}),
"wb_tint": ("FLOAT", {"default": 0, "min": -100, "max": 100, "step": 1}),
}
}
def primere_white_balance(self, image, use_white_balance, wb_temperature, wb_tint):
pil_img = utility.tensor_to_image(image)
if use_white_balance and (wb_temperature != 6500 or wb_tint != 0):
pil_img = img_white_balance.img_white_balance(image=pil_img, temperature=wb_temperature, tint=wb_tint)
return (utility.image_to_tensor(pil_img),)
class PrimereSmartLighting:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_smart_lighting"
CATEGORY = TREE_RASTERIX
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"use_smart_lighting": ("BOOLEAN", {"default": False, "label_off": "Ignore smart lightning", "label_on": "Apply smart lightning"}),
"smart_lighting": ("FLOAT", {"default": 0, "min": 0, "max": 100, "step": 1}),
}
}
def primere_smart_lighting(self, image, use_smart_lighting, smart_lighting):
pil_img = utility.tensor_to_image(image)
if use_smart_lighting and smart_lighting != 0:
pil_img = img_smart_lighting.img_smart_lighting(image=pil_img, intensity=smart_lighting)
return (utility.image_to_tensor(pil_img),)
class PrimereBlur:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_blur"
CATEGORY = TREE_RASTERIX
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"use_blur": ("BOOLEAN", {"default": False, "label_off": "Ignore blur", "label_on": "Apply blur"}),
"blur_type": (["gaussian", "box", "motion", "bilateral", "lens"], {"default": "bilateral"}),
"blur_intensity": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 5.0, "step": 0.1}),
"blur_radius": ("FLOAT", {"default": 2.0, "min": 0.5, "max": 50.0, "step": 0.5}),
"angle": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 360.0, "step": 1.0}),
"bilateral_edge_sensitivity": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"blur_edge_only": ("BOOLEAN", {"default": False, "label_off": "Full image blur", "label_on": "Flat areas only, edges protected"}),
"edge_threshold": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
def primere_blur(self, image, use_blur, blur_type, blur_intensity, blur_radius, angle, bilateral_edge_sensitivity, blur_edge_only, edge_threshold):
pil_img = utility.tensor_to_image(image)
if use_blur and blur_intensity != 0:
pil_img = img_blur.img_blur(image=pil_img, blur_type=blur_type, intensity=blur_intensity, radius=blur_radius, angle=angle, edge_only=blur_edge_only, bilateral_edge_sensitivity=bilateral_edge_sensitivity, edge_threshold=edge_threshold)
return (utility.image_to_tensor(pil_img),)
class PrimereBrightnessContrast:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_brightness_contrast"
CATEGORY = TREE_RASTERIX
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"use_brightness_contrast": ("BOOLEAN", {"default": False, "label_off": "Ignore brightness-contrast", "label_on": "Apply brightness-contrast"}),
"brightness": ("FLOAT", {"default": 0, "min": -150, "max": 150, "step": 1}),
"contrast": ("FLOAT", {"default": 0, "min": -50, "max": 100, "step": 1}),
"use_legacy": ("BOOLEAN", {"default": False, "label_off": "Use non-linear shift", "label_on": "Use adaptive offset"}),
}
}
def primere_brightness_contrast(self, image, use_brightness_contrast, brightness, contrast, use_legacy):
pil_img = utility.tensor_to_image(image)
if use_brightness_contrast and (brightness != 0 or contrast != 0):
pil_img = img_brightness_contrast.img_brightness_contrast(image=pil_img, brightness=brightness, contrast=contrast, use_legacy=use_legacy)
return (utility.image_to_tensor(pil_img),)
class PrimereFilmRendering:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_film_rendering"
CATEGORY = TREE_RASTERIX
FILM_TYPES = ["All", "CF", "BWF", "CCD", "MOB"]
FILM_PRESETS_BY_TYPE = img_film_rendering.list_presets_by_type()
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"use_film_rendering": ("BOOLEAN", {"default": False, "label_off": "Ignore film rendering", "label_on": "Apply film rendering"}),
"film_type": (cls.FILM_TYPES, {"default": "All"}),
"film_rendering": (list(FILM_PRESETS.keys()), {"default": list(FILM_PRESETS.keys())[0]}),
"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"}),
"expiration_years": ("INT", {"default": 0, "min": 0, "max": 30, "step": 1}),
}
}
def primere_film_rendering(self, image, film_type, use_film_rendering, film_rendering, film_rendering_intensity, iso_grain, halation, expiration_years):
pil_img = utility.tensor_to_image(image)
if film_type != "All":
allowed_presets = self.FILM_PRESETS_BY_TYPE.get(film_type, [])
if allowed_presets and film_rendering not in allowed_presets:
film_rendering = allowed_presets[0]
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, add_halation=halation, expiration_years=expiration_years)
return (utility.image_to_tensor(pil_img),)
class PrimereSelectiveTone:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_selective_tone"
CATEGORY = TREE_RASTERIX
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"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}),
"selective_tone_zone": (["highlights", "midtones", "shadows", "blacks"], {"default": "midtones"}),
"selective_tone_separation": ("FLOAT", {"default": 50, "min": 0, "max": 100, "step": 1}),
"selective_tone_strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
def primere_selective_tone(self, image, use_selective_tone, selective_tone_value, selective_tone_zone, selective_tone_separation, selective_tone_strength):
pil_img = utility.tensor_to_image(image)
rasterix_json_path = os.path.join(PRIMERE_ROOT, 'front_end', 'rasterix.json')
rasterix_data = utility.json2tuple(rasterix_json_path) or {}
st_data = rasterix_data.get('selective_tone', {})
if use_selective_tone and st_data:
pil_img = img_selective_tone.img_selective_tone(image=pil_img, channels_data=st_data, separation=selective_tone_separation, strength=selective_tone_strength)
return (utility.image_to_tensor(pil_img),)
class PrimereColorBalance:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_color_balance"
CATEGORY = TREE_RASTERIX
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"use_color_balance": ("BOOLEAN", {"default": False, "label_off": "Ignore color balance", "label_on": "Apply color balance"}),
"color_balance_cyan_red": ("FLOAT", {"default": 0, "min": -100, "max": 100, "step": 1}),
"color_balance_magenta_green": ("FLOAT", {"default": 0, "min": -100, "max": 100, "step": 1}),
"color_balance_yellow_blue": ("FLOAT", {"default": 0, "min": -100, "max": 100, "step": 1}),
"color_balance_tone": (["highlights", "midtones", "shadows"], {"default": "midtones"}),
"color_balance_preserve_luminosity": ("BOOLEAN", {"default": False, "label_off": "Modify luminosity", "label_on": "Restore original luminosity"}),
"color_balance_separation": ("FLOAT", {"default": 50, "min": 0, "max": 100, "step": 1}),
}
}
def primere_color_balance(self, image, 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):
pil_img = utility.tensor_to_image(image)
rasterix_json_path = os.path.join(PRIMERE_ROOT, 'front_end', 'rasterix.json')
rasterix_data = utility.json2tuple(rasterix_json_path) or {}
cb_data = rasterix_data.get('color_balance', {})
if use_color_balance and cb_data:
pil_img = img_color_balance.img_color_balance(image=pil_img, channels_data=cb_data, preserve_luminosity=color_balance_preserve_luminosity, separation=color_balance_separation)
return (utility.image_to_tensor(pil_img),)
class PrimereHSL:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_hsl"
CATEGORY = TREE_RASTERIX
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"use_hsl": ("BOOLEAN", {"default": False, "label_off": "Ignore HSL", "label_on": "Apply HSL"}),
"hsl_hue": ("FLOAT", {"default": 0, "min": -180, "max": 180, "step": 1}),
"hsl_saturation": ("FLOAT", {"default": 0, "min": -100, "max": 100, "step": 1}),
"hsl_lightness": ("FLOAT", {"default": 0, "min": -100, "max": 100, "step": 1}),
"hsl_vibrance": ("FLOAT", {"default": 0, "min": -100, "max": 100, "step": 1}),
"hsl_channel": (["master", "red", "green", "blue"], {"default": "master"}),
"hsl_channel_width": ("FLOAT", {"default": 50, "min": 0, "max": 100, "step": 1}),
"hsl_skin_protection": ("BOOLEAN", {"default": True, "label_off": "Vibrance affects skin tones", "label_on": "Skin tones protected from vibrance"}),
}
}
def primere_hsl(self, image, use_hsl, hsl_hue, hsl_saturation, hsl_lightness, hsl_vibrance, hsl_channel, hsl_channel_width, hsl_skin_protection):
pil_img = utility.tensor_to_image(image)
rasterix_json_path = os.path.join(PRIMERE_ROOT, 'front_end', 'rasterix.json')
rasterix_data = utility.json2tuple(rasterix_json_path) or {}
hs_data = rasterix_data.get('hue_saturation', {})
if use_hsl and hs_data:
pil_img = img_hue_saturation.img_hue_saturation(image=pil_img, channels_data=hs_data, channel_width=hsl_channel_width, skin_protection=hsl_skin_protection)
return (utility.image_to_tensor(pil_img),)
class PrimereShadeDetailer:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_shade_detailer"
CATEGORY = TREE_RASTERIX
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"use_shade_detailer": ("BOOLEAN", {"default": False, "label_off": "Ignore shade detailer", "label_on": "Apply shade detailer"}),
"shade_level": ("FLOAT", {"default": 0, "min": -100, "max": 100, "step": 1}),
"shade_radius": ("FLOAT", {"default": 0, "min": 0, "max": 50, "step": 0.5}),
"detail_mode": (["fine", "medium", "broad"], {"default": "medium"}),
"shade_strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
def primere_shade_detailer(self, image, use_shade_detailer, shade_level, shade_radius, detail_mode, shade_strength):
pil_img = utility.tensor_to_image(image)
rasterix_json_path = os.path.join(PRIMERE_ROOT, 'front_end', 'rasterix.json')
rasterix_data = utility.json2tuple(rasterix_json_path) or {}
shade_data = rasterix_data.get('shade', {})
if use_shade_detailer and shade_data:
for mode, vals in shade_data.items():
lvl = vals.get('shade_level', 0)
if lvl != 0:
rad = vals.get('shade_radius', 0)
pil_img = img_shade_level.img_shade_level(image=pil_img, shade_level=lvl, radius=rad, strength=shade_strength)
return (utility.image_to_tensor(pil_img),)
class PrimereLevelEndpoints:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_level_endpoints"
CATEGORY = TREE_RASTERIX
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"precision": ("BOOLEAN", {"default": False, "label_off": "8 bit", "label_on": "16 bit"}),
"use_level_endpoints": ("BOOLEAN", {"default": False, "label_off": "Ignore endpoint offset", "label_on": "Apply endpoint offset"}),
"black_offset": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 25.0, "step": 0.1}),
"white_offset": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 25.0, "step": 0.1}),
"skip_if_no_clip": ("BOOLEAN", {"default": False, "label_off": "Offset all values", "label_on": "Skip if no clips"}),
}
}
def primere_level_endpoints(self, image, precision, use_level_endpoints, black_offset, white_offset, skip_if_no_clip):
pil_img = utility.tensor_to_image(image)
if use_level_endpoints and (black_offset != 0 or white_offset != 0):
pil_img = img_levels_compress.img_levels_compress(image=pil_img, black_offset=black_offset, white_offset=white_offset, skip_if_no_clip=skip_if_no_clip, high_precision=precision)
return (utility.image_to_tensor(pil_img),)
class PrimerePosterize:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_posterize"
CATEGORY = TREE_RASTERIX
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"use_posterize": ("BOOLEAN", {"default": False, "label_off": "Ignore posterize", "label_on": "Apply posterize"}),
"shades": ("INT", {"default": 255, "min": 1, "max": 255, "step": 1}),
"channels": (["Red", "Green", "Blue"], {"default": "Red"}),
}
}
def primere_posterize(self, image, use_posterize, shades, channels):
pil_img = utility.tensor_to_image(image)
rasterix_json_path = os.path.join(PRIMERE_ROOT, 'front_end', 'rasterix.json')
rasterix_data = utility.json2tuple(rasterix_json_path) or {}
poster_data = rasterix_data.get('posterize', {})
if use_posterize and poster_data:
pil_img = img_posterize.img_posterize(image=pil_img, channels_data=poster_data)
return (utility.image_to_tensor(pil_img),)
class PrimereDithering:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_dithering"
CATEGORY = TREE_RASTERIX
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"precision": ("BOOLEAN", {"default": False, "label_off": "8 bit", "label_on": "16 bit"}),
"normalize_midpeaks": ("BOOLEAN", {"default": False, "label_on": "Anti-spike filter: ON", "label_off": "Anti-spike filter: OFF"}),
"peak_width": ("INT", {"default": 3, "min": 1, "max": 10, "step": 1}),
"dither_quantization": ("BOOLEAN", {"default": False, "label_off": "Dither quantization OFF", "label_on": "Dither quantization ON"}),
"adaptive_dither_strength": ("BOOLEAN", {"default": False, "label_off": "Keep dither strength", "label_on": "Increase dither strength"}),
"error_diffusion": ("BOOLEAN", {"default": False, "label_off": "Error diffusion OFF", "label_on": "Error diffusion ON"}),
}
}
def primere_dithering(self, image, precision, normalize_midpeaks, peak_width, dither_quantization, adaptive_dither_strength, error_diffusion):
pil_img = utility.tensor_to_image(image)
if dither_quantization or error_diffusion or normalize_midpeaks:
pil_img = img_dithering.img_dithering(image=pil_img, dither_quantization=dither_quantization, adaptive_dither_strength=adaptive_dither_strength, error_diffusion=error_diffusion, normalize_midpeaks=normalize_midpeaks, peak_width=peak_width, high_precision=precision)
return (utility.image_to_tensor(pil_img),)
class PrimereAIDetectionBypasser:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_ai_detection_bypasser"
CATEGORY = TREE_RASTERIX
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"use_ai_detection_bypasser": ("BOOLEAN", {"default": False, "label_off": "AI detection bypass off", "label_on": "AI detection bypass on"}),
"adb_freq_strength": ("FLOAT", {"default": 0.019, "min": 0.0, "max": 0.1, "step": 0.001}),
"adb_variance_strength": ("FLOAT", {"default": 0.32, "min": 0.0, "max": 1.0, "step": 0.01}),
"adb_unsharp_percent": ("INT", {"default": 38, "min": 0, "max": 150, "step": 1}),
"adb_jpeg_cycles": ("INT", {"default": 4, "min": 0, "max": 6, "step": 1}),
}
}
def primere_ai_detection_bypasser(self, image, use_ai_detection_bypasser, adb_freq_strength, adb_variance_strength, adb_unsharp_percent, adb_jpeg_cycles):
pil_img = utility.tensor_to_image(image)
if use_ai_detection_bypasser:
pil_img = isgen_detect_ext_full.bypass_ai_detector(image=pil_img, freq_strength=adb_freq_strength, variance_strength=adb_variance_strength, unsharp_percent=adb_unsharp_percent, jpeg_cycles=adb_jpeg_cycles)
return (utility.image_to_tensor(pil_img),)
class PrimereRasterixGrain:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_rasterix_grain"
CATEGORY = TREE_RASTERIX
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"use_grain": ("BOOLEAN", {"default": False, "label_off": "Ignore grain", "label_on": "Apply grain"}),
"intensity": ("FLOAT", {"default": 20.0, "min": 0.0, "max": 100.0, "step": 0.5}),
"grain_size": ("FLOAT", {"default": 1.0, "min": 0.5, "max": 8.0, "step": 0.1}),
"grain_type": (["gaussian", "organic", "salt_pepper", "fine"], {"default": "gaussian"}),
"color_mode": (["color", "monochrome"], {"default": "color"}),
"color_tint": (["neutral", "warm", "cool", "green", "custom"], {"default": "neutral"}),
"color_tint_r": ("FLOAT", {"default": 0, "min": -50, "max": 50, "step": 1}),
"color_tint_g": ("FLOAT", {"default": 0, "min": -50, "max": 50, "step": 1}),
"color_tint_b": ("FLOAT", {"default": 0, "min": -50, "max": 50, "step": 1}),
"shadow_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 3.0, "step": 0.05}),
"highlight_strength": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 3.0, "step": 0.05}),
"midtone_peak": ("FLOAT", {"default": 0.4, "min": 0.0, "max": 1.0, "step": 0.05}),
"vignette_boost": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.05}),
},
"optional": {
"seed": ("INT", {"default": 0, "min": 0, "max": utility.MAX_SEED, "forceInput": True}),
}
}
def primere_rasterix_grain(self, image, use_grain, intensity, grain_size, grain_type, color_mode, color_tint, color_tint_r, color_tint_g, color_tint_b, shadow_strength, highlight_strength, midtone_peak, vignette_boost, seed=None):
if intensity == 0 or use_grain == False:
return (image,)
pil_img = utility.tensor_to_image(image)
pil_img = img_film_grain.img_film_grain(
image=pil_img,
intensity=intensity,
grain_size=grain_size,
grain_type=grain_type,
color_mode=color_mode,
color_tint=color_tint,
color_tint_rgb=(color_tint_r, color_tint_g, color_tint_b),
shadow_strength=shadow_strength,
highlight_strength=highlight_strength,
midtone_peak=midtone_peak,
vignette_boost=vignette_boost,
seed=seed,
)
return (utility.image_to_tensor(pil_img),)
class PrimereRasterixLens:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_rasterix_lens"
CATEGORY = TREE_RASTERIX
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"use_vignette": ("BOOLEAN", {"default": False, "label_off": "Ignore vignette", "label_on": "Apply vignette"}),
"vignette_strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"vignette_radius": ("FLOAT", {"default": 0.65, "min": 0.0, "max": 1.0, "step": 0.01}),
"vignette_feather": ("FLOAT", {"default": 0.4, "min": 0.0, "max": 1.0, "step": 0.01}),
"vignette_shape": (["circular", "oval", "corner"], {"default": "circular"}),
"use_chroma": ("BOOLEAN", {"default": False, "label_off": "Ignore chromatic aberration", "label_on": "Apply chromatic aberration"}),
"chroma_intensity": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 10.0, "step": 0.1}),
"chroma_falloff": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"chroma_fringe_color": (["red_blue", "green_magenta", "yellow_purple"], {"default": "red_blue"}),
"use_bokeh": ("BOOLEAN", {"default": False, "label_off": "Ignore bokeh", "label_on": "Apply bokeh"}),
"bokeh_radius": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 40.0, "step": 0.5}),
"bokeh_blades": ("INT", {"default": 0, "min": 0, "max": 12, "step": 1}),
"bokeh_highlight_boost": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
"bokeh_cat_eye": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"use_distortion": ("BOOLEAN", {"default": False, "label_off": "Ignore lens distortion", "label_on": "Apply lens distortion"}),
"distortion_barrel": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
"distortion_pincushion": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"distortion_zoom": ("FLOAT", {"default": 1.0, "min": 0.5, "max": 2.0, "step": 0.01}),
"use_flare": ("BOOLEAN", {"default": False, "label_off": "Ignore lens flare", "label_on": "Apply lens flare"}),
"flare_intensity": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"flare_pos_x": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.01}),
"flare_pos_y": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.01}),
"flare_streak_count": ("INT", {"default": 6, "min": 2, "max": 12, "step": 1}),
"flare_streak_length":("FLOAT", {"default": 0.4, "min": 0.1, "max": 1.0, "step": 0.01}),
"flare_ghost_count": ("INT", {"default": 4, "min": 0, "max": 8, "step": 1}),
"flare_color": (["warm", "cool", "neutral", "rainbow"], {"default": "warm"}),
"use_halation": ("BOOLEAN", {"default": False, "label_off": "Ignore halation", "label_on": "Apply halation"}),
"halation_intensity": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"halation_radius": ("FLOAT", {"default": 15.0, "min": 2.0, "max": 50.0, "step": 0.5}),
"halation_threshold": ("FLOAT", {"default": 0.75, "min": 0.0, "max": 1.0, "step": 0.01}),
"halation_warmth": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
"use_focus": ("BOOLEAN", {"default": False, "label_off": "Ignore focus falloff", "label_on": "Apply focus falloff"}),
"focus_blur_radius": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 30.0, "step": 0.5}),
"focus_mode": (["horizontal", "vertical", "radial", "oval"], {"default": "horizontal"}),
"focus_pos": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"focus_width": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.01}),
"focus_feather": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
"use_spherical": ("BOOLEAN", {"default": False, "label_off": "Ignore spherical aberration", "label_on": "Apply spherical aberration"}),
"spherical_intensity": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"spherical_radius": ("FLOAT", {"default": 3.0, "min": 0.5, "max": 15.0, "step": 0.5}),
"spherical_zone": (["centre", "edge", "global"], {"default": "centre"}),
"use_anamorphic": ("BOOLEAN", {"default": False, "label_off": "Ignore anamorphic", "label_on": "Apply anamorphic"}),
"anamorphic_intensity": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"anamorphic_streak_color": (["blue", "warm", "white"], {"default": "blue"}),
"anamorphic_streak_length": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.01}),
"anamorphic_oval_bokeh": ("FLOAT", {"default": 0.4, "min": 0.0, "max": 1.0, "step": 0.01}),
"anamorphic_blue_bias": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
def primere_rasterix_lens(self, image, use_vignette, vignette_strength, vignette_radius, vignette_feather, vignette_shape, use_chroma, chroma_intensity, chroma_falloff, chroma_fringe_color, use_bokeh, bokeh_radius, bokeh_blades, bokeh_highlight_boost, bokeh_cat_eye, use_distortion, distortion_barrel, distortion_pincushion, distortion_zoom, use_flare, flare_intensity, flare_pos_x, flare_pos_y, flare_streak_count, flare_streak_length, flare_ghost_count, flare_color, use_halation, halation_intensity, halation_radius, halation_threshold, halation_warmth, use_focus, focus_blur_radius, focus_mode, focus_pos, focus_width, focus_feather, use_spherical, spherical_intensity, spherical_radius, spherical_zone, use_anamorphic, anamorphic_intensity, anamorphic_streak_color, anamorphic_streak_length, anamorphic_oval_bokeh, anamorphic_blue_bias):
pil_img = utility.tensor_to_image(image)
pil_img = img_lens_effects.img_lens_effect(
image=pil_img,
vignette_strength=vignette_strength if use_vignette else 0,
vignette_radius=vignette_radius,
vignette_feather=vignette_feather,
vignette_shape=vignette_shape,
chroma_intensity=chroma_intensity if use_chroma else 0,
chroma_falloff=chroma_falloff,
chroma_fringe_color=chroma_fringe_color,
bokeh_radius=bokeh_radius if use_bokeh else 0,
bokeh_blades=bokeh_blades,
bokeh_highlight_boost=bokeh_highlight_boost,
bokeh_cat_eye=bokeh_cat_eye,
distortion_barrel=distortion_barrel if use_distortion else 0,
distortion_pincushion=distortion_pincushion if use_distortion else 0,
distortion_zoom=distortion_zoom,
flare_intensity=flare_intensity if use_flare else 0,
flare_pos_x=flare_pos_x,
flare_pos_y=flare_pos_y,
flare_streak_count=flare_streak_count,
flare_streak_length=flare_streak_length,
flare_ghost_count=flare_ghost_count,
flare_color=flare_color,
halation_intensity=halation_intensity if use_halation else 0,
halation_radius=halation_radius,
halation_threshold=halation_threshold,
halation_warmth=halation_warmth,
focus_blur_radius=focus_blur_radius if use_focus else 0,
focus_mode=focus_mode,
focus_pos=focus_pos,
focus_width=focus_width,
focus_feather=focus_feather,
spherical_intensity=spherical_intensity if use_spherical else 0,
spherical_radius=spherical_radius,
spherical_zone=spherical_zone,
anamorphic_intensity=anamorphic_intensity if use_anamorphic else 0,
anamorphic_streak_color=anamorphic_streak_color,
anamorphic_streak_length=anamorphic_streak_length,
anamorphic_oval_bokeh=anamorphic_oval_bokeh,
anamorphic_blue_bias=anamorphic_blue_bias,
)
return (utility.image_to_tensor(pil_img),)
class PrimereHistogram:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_histogram"
CATEGORY = TREE_RASTERIX
OUTPUT_NODE = True
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"precision": ("BOOLEAN", {"default": False, "label_off": "8 bit", "label_on": "16 bit"}),
"show_histogram": ("BOOLEAN", {"default": False, "label_off": "Ignore histogram", "label_on": "Create histogram"}),
"histogram_channel": (["RGB", "RED", "GREEN", "BLUE"], {"default": "RGB"}),
"histogram_style": (["bars", "lines", "waveform", "heatmap", "stacked", "luma", "parade", "gradient", "glow", "dots", "step", "log", "percentile", "inverse"], {"default": "bars"}),
},
"hidden": {
"id": "UNIQUE_ID",
}
}
def primere_histogram(self, image, precision, show_histogram=False, histogram_channel="RGB", histogram_style="bars", id=None):
pil_img = utility.tensor_to_image(image)
pil_img_input = pil_img.copy()
histogram.rasterix_hist_cache_store(pil_img_input, pil_img, precision, node_id=id)
if show_histogram:
histogram.rasterix_hist_cache_store(pil_img_input, pil_img, precision, node_id=id)
active_hist = histogram.rasterix_hist_render_selected(pil_img_input, pil_img, precision, True, histogram_channel, histogram_style, node_id=id)
suffix = ''.join(random.choice("abcdefghijklmnopqrstuvwxyz0123456789") for _ in range(8))
temp_file = f"rasterix_hist_{suffix}.png"
active_hist.save(os.path.join(folder_paths.temp_directory, temp_file), compress_level=1)
return {"ui": {"images": [{"filename": temp_file, "subfolder": "", "type": "temp"}]}, "result": (utility.image_to_tensor(pil_img),), }
else:
INVALID_IMAGE_PATH = os.path.join(PRIMERE_ROOT, 'front_end', 'images')
INVALID_IMAGE = os.path.join(INVALID_IMAGE_PATH, "No_histogram_08.jpg")
images = utility.ImageLoaderFromPath(INVALID_IMAGE)
r1 = random.randint(1000, 9999)
temp_filename = f"Primere_ComfyUI_{r1}.png"
os.makedirs(folder_paths.get_temp_directory(), exist_ok=True)
TEMP_FILE = os.path.join(folder_paths.get_temp_directory(), temp_filename)
utility.tensor_to_image(images[0]).save(TEMP_FILE)
return {"ui": {"images": [{"filename": temp_filename, "subfolder": "", "type": "temp"}]}, "result": (utility.image_to_tensor(pil_img),),}
class PrimereSolarizationBW:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_solarization_bw"
CATEGORY = TREE_RASTERIX
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"use_solarization": ("BOOLEAN", {"default": False, "label_off": "Ignore solarization", "label_on": "Apply solarization"}),
"precision": ("BOOLEAN", {"default": False, "label_off": "8 bit", "label_on": "16 bit"}),
"color_mode": ("BOOLEAN", {"default": False, "label_off": "Keep unchanged", "label_on": "Force B&W"}),
"strength": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 2.0, "step": 0.01}),
"pivot": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"sigma": ("FLOAT", {"default": 0.18, "min": 0.01, "max": 0.5, "step": 0.01}),
"edge_boost": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 3.0, "step": 0.05}),
"edge_radius": ("FLOAT", {"default": 1.0, "min": 0.5, "max": 3.0, "step": 0.1}),
"contrast": ("FLOAT", {"default": 1.1, "min": 0.5, "max": 2.0, "step": 0.01}),
"hard_paper": ("BOOLEAN", {"default": False, "label_off": "Soft paper", "label_on": "Hard paper"}),
"grain_modulation": ("BOOLEAN", {"default": False, "label_off": "No grain modulation", "label_on": "Grain-modulated inversion"}),
"grain_strength": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.01}),
"grain_scale": ("FLOAT", {"default": 1.0, "min": 0.3, "max": 3.0, "step": 0.1}),
},
"optional": {
"seed": ("INT", {"default": 0, "min": 0, "max": utility.MAX_SEED, "forceInput": True}),
}
}
def primere_solarization_bw(self, image, color_mode, use_solarization, precision, strength, pivot, sigma, edge_boost, edge_radius, contrast, hard_paper, grain_modulation, grain_strength, grain_scale, seed = None):
pil_img = utility.tensor_to_image(image)
if use_solarization:
pil_img = img_solarization_bw.img_solarization_bw(image=pil_img, color_mode=color_mode, strength=strength, pivot=pivot, sigma=sigma, edge_boost=edge_boost, edge_radius=edge_radius, contrast=contrast, precision=precision, hard_paper=hard_paper, grain_modulation=grain_modulation, grain_strength=grain_strength, grain_scale=grain_scale, seed=seed)
return (utility.image_to_tensor(pil_img),)
class PrimereClarity:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_clarity"
CATEGORY = TREE_RASTERIX
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"use_clarity": ("BOOLEAN", {"default": False, "label_off": "Ignore clarity", "label_on": "Apply clarity"}),
"precision": ("BOOLEAN", {"default": False, "label_off": "8 bit", "label_on": "16 bit"}),
"strength": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 3.0, "step": 0.01}),
"radius": ("FLOAT", {"default": 2.0, "min": 0.5, "max": 10.0, "step": 0.1}),
"midtone_range": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 1.0, "step": 0.01}),
"edge_preservation": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
def primere_clarity(self, image, use_clarity, precision, strength, radius, midtone_range, edge_preservation):
pil_img = utility.tensor_to_image(image)
if use_clarity and strength != 0:
pil_img = img_clarity.img_clarity(image=pil_img, strength=strength, radius=radius, midtone_range=midtone_range, edge_preservation=edge_preservation, precision=precision)
return (utility.image_to_tensor(pil_img),)
class PrimereDehaze:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_dehaze"
CATEGORY = TREE_RASTERIX
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"use_dehaze": ("BOOLEAN", {"default": False, "label_off": "Ignore dehaze", "label_on": "Apply dehaze"}),
"precision": ("BOOLEAN", {"default": False, "label_off": "8 bit", "label_on": "16 bit"}),
"strength": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 2.0, "step": 0.01}),
"radius": ("INT", {"default": 15, "min": 3, "max": 100, "step": 1}),
"omega": ("FLOAT", {"default": 0.95, "min": 0.5, "max": 1.0, "step": 0.01}),
"t0": ("FLOAT", {"default": 0.1, "min": 0.01, "max": 0.5, "step": 0.01}),
"contrast": ("FLOAT", {"default": 1.05, "min": 0.5, "max": 2.0, "step": 0.01}),
}
}
def primere_dehaze(self, image, use_dehaze, precision, strength, radius, omega, t0, contrast):
pil_img = utility.tensor_to_image(image)
if use_dehaze and strength > 0:
pil_img = img_dehaze.img_dehaze(image=pil_img, strength=strength, radius=radius, omega=omega, t0=t0, contrast=contrast, precision=precision)
return (utility.image_to_tensor(pil_img),)
class PrimereLocalLaplacian:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_local_laplacian"
CATEGORY = TREE_RASTERIX
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"use_local_laplacian": ("BOOLEAN", {"default": False, "label_off": "Ignore local laplacian", "label_on": "Apply local laplacian"}),
"sigma": ("FLOAT", {"default": 1.0, "min": 0.5, "max": 5.0, "step": 0.1}),
"contrast": ("FLOAT", {"default": 1.2, "min": 0.5, "max": 3.0, "step": 0.01}),
"detail": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 3.0, "step": 0.01}),
"levels": ("INT", {"default": 8, "min": 4, "max": 32, "step": 1}),
}
}
def primere_local_laplacian(self, image, use_local_laplacian, sigma, contrast, detail, levels):
pil_img = utility.tensor_to_image(image)
if use_local_laplacian:
pil_img = img_local_laplacian.img_local_laplacian(image=pil_img, sigma=sigma, contrast=contrast, detail=detail, levels=levels)
return (utility.image_to_tensor(pil_img),)
class PrimereFrequencySeparation:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_frequency_separation"
CATEGORY = TREE_RASTERIX
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"use_frequency_separation": ("BOOLEAN", {"default": False, "label_off": "Ignore frequency separation", "label_on": "Apply frequency separation"}),
"radius": ("FLOAT", {"default": 3.0, "min": 0.5, "max": 20.0, "step": 0.1}),
"low_freq_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 3.0, "step": 0.01}),
"high_freq_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 3.0, "step": 0.01}),
"blend_mode": (["add", "multiply", "overlay"], {"default": "add"}),
}
}
def primere_frequency_separation(self, image, use_frequency_separation, radius, low_freq_strength, high_freq_strength, blend_mode):
pil_img = utility.tensor_to_image(image)
if use_frequency_separation:
pil_img = img_frequency_separation.img_frequency_separation(image=pil_img, radius=radius, low_freq_strength=low_freq_strength, high_freq_strength=high_freq_strength, blend_mode=blend_mode)
return (utility.image_to_tensor(pil_img),)
class PrimereFilmicCurve:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_filmic_curve"
CATEGORY = TREE_RASTERIX
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"use_filmic": ("BOOLEAN", {"default": False, "label_off": "Ignore filmic", "label_on": "Apply filmic"}),
"curve_type": (["filmic", "log"], {"default": "filmic"}),
"contrast": ("FLOAT", {"default": 1.0, "min": 0.5, "max": 2.0, "step": 0.01}),
"highlight_rolloff": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"shadow_lift": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 0.5, "step": 0.01}),
"pivot": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
def primere_filmic_curve(self, image, use_filmic, curve_type, contrast, highlight_rolloff, shadow_lift, pivot):
pil_img = utility.tensor_to_image(image)
if use_filmic:
pil_img = img_filmic_curve.img_filmic_curve(image=pil_img, curve_type=curve_type, contrast=contrast, highlight_rolloff=highlight_rolloff, shadow_lift=shadow_lift, pivot=pivot)
return (utility.image_to_tensor(pil_img),)
class PrimereLUT3D:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_lut3d"
CATEGORY = TREE_RASTERIX
LUT_DIR = os.path.join(PRIMERE_ROOT, 'components', 'images', 'luts')
@classmethod
def _list_luts(cls):
lut_entries = ["None"]
if not os.path.exists(cls.LUT_DIR):
return lut_entries
for f in sorted(os.listdir(cls.LUT_DIR)):
full_path = os.path.join(cls.LUT_DIR, f)
if os.path.isfile(full_path) and f.lower().endswith(".cube"):
lut_entries.append(f)
for d in sorted(os.listdir(cls.LUT_DIR)):
subdir = os.path.join(cls.LUT_DIR, d)
if os.path.isdir(subdir):
for f in sorted(os.listdir(subdir)):
if f.lower().endswith(".cube"):
lut_entries.append(f"{d}/{f}")
return lut_entries
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"use_lut": ("BOOLEAN", {"default": False, "label_off": "Ignore LUT", "label_on": "Apply LUT"}),
"lut_file": (cls._list_luts(),),
"intensity": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.01}),
"color_space": (["sRGB", "linear"], {"default": "sRGB"}),
}
}
def primere_lut3d(self, image, use_lut, lut_file, intensity, color_space):
pil_img = utility.tensor_to_image(image)
if use_lut and lut_file != "None":
lut_path = os.path.join(self.LUT_DIR, lut_file)
pil_img = img_lut3d.img_lut3d(image=pil_img, lut_path=lut_path, intensity=intensity, input_space=color_space, output_space=color_space)
return (utility.image_to_tensor(pil_img),)
class PrimereEdgeJitter:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_edge_jitter"
CATEGORY = TREE_RASTERIX
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"use_edge_jitter": ("BOOLEAN", {"default": False, "label_off": "Ignore edge jitter", "label_on": "Apply edge jitter"}),
"precision": ("BOOLEAN", {"default": False, "label_off": "8 bit", "label_on": "16 bit"}),
"strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 3.0, "step": 0.01}),
"radius": ("FLOAT", {"default": 1.5, "min": 0.5, "max": 5.0, "step": 0.1}),
"edge_threshold": ("FLOAT", {"default": 0.1, "min": 0.01, "max": 0.5, "step": 0.01}),
"randomness": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
},
"optional": {
"seed": ("INT", {"default": 0, "min": 0, "max": utility.MAX_SEED, "forceInput": True}),
}
}
def primere_edge_jitter(self, image, use_edge_jitter, precision, strength, radius, edge_threshold, randomness, seed=None):
pil_img = utility.tensor_to_image(image)
if use_edge_jitter and strength > 0:
pil_img = img_edge_jitter.img_edge_jitter(image=pil_img, strength=strength, radius=radius, edge_threshold=edge_threshold, randomness=randomness, seed=seed, precision=precision)
return (utility.image_to_tensor(pil_img),)
class PrimereDepthBlur:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "primere_depth_blur"
CATEGORY = TREE_RASTERIX
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"forceInput": True}),
"use_depth_blur": ("BOOLEAN", {"default": False, "label_off": "Ignore depth blur", "label_on": "Apply depth blur"}),
"auto_optimize": ("BOOLEAN", {"default": False, "label_off": "Use custom inputs", "label_on": "Optimize settings by focus"}),
"use_DA_v3": ("BOOLEAN", {"default": False, "label_off": "Depth-anything V2", "label_on": "Depth-anything V3"}),
"focus_depth": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"depth_range": ("FLOAT", {"default": 0.200, "min": 0.001, "max": 1.000, "step": 0.001}),
"max_blur": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 50.0, "step": 0.5}),
"depth_gamma": ("FLOAT", {"default": 1.00, "min": 0.10, "max": 5.00, "step": 0.01}),
}
}
def primere_depth_blur(self, image, use_depth_blur, auto_optimize, use_DA_v3, focus_depth, depth_range, max_blur, depth_gamma):
pil_img = utility.tensor_to_image(image)
if use_depth_blur:
pil_img = img_depth_blur.img_depth_blur(image=pil_img, focus_depth=focus_depth, depth_range=depth_range, max_blur=max_blur, depth_gamma=depth_gamma, auto_optimize=auto_optimize, use_v3=use_DA_v3)
return (utility.image_to_tensor(pil_img),)
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