From 96187c9a33135a80b6b91c44ce82d613f9ec8037 Mon Sep 17 00:00:00 2001 From: monkeyforever2 Date: Sat, 14 Mar 2026 08:57:43 +0100 Subject: [PATCH] Add LUT Loader + LUT Apply nodes with bundled iPhone 15 Pro LUT --- README.md | 38 +- __init__.py | 11 +- generate_iphone_lut.py | 133 + lut_engine.py | 179 + luts/iPhone_15_Pro_Standard.cube | 35944 +++++++++++++++++++++++++++++ nodes.py | 137 + test_node.py | 85 +- 7 files changed, 36524 insertions(+), 3 deletions(-) create mode 100644 generate_iphone_lut.py create mode 100644 lut_engine.py create mode 100644 luts/iPhone_15_Pro_Standard.cube diff --git a/README.md b/README.md index 1b00aa2..98acc31 100644 --- a/README.md +++ b/README.md @@ -170,6 +170,42 @@ Once installed, find the node in the ComfyUI menu: --- +## 🎨 LUT Color Grading (NEW) + +This pack includes two dedicated LUT nodes for applying `.cube` 3D LUTs — plus a bundled **iPhone 15 Pro Standard** LUT baked from our color science pipeline. + +### LUT Workflow + +``` +[🎨 LUT Loader] → [🎨 LUT Apply] → [SaveImage] + ↑ + [Your Image] +``` + +### 🎨 LUT Loader + +| Parameter | Description | +|-----------|-------------| +| `lut_name` | Dropdown of `.cube` files from the bundled `luts/` folder | + +**Output:** `LUT_DATA` — connect to LUT Apply + +### 🎨 LUT Apply + +| Parameter | Default | Description | +|-----------|---------|-------------| +| `image` | — | Your image input | +| `lut_data` | — | Connect from LUT Loader | +| `strength` | `0.85` | LUT intensity (0.0 = original, 1.0 = full effect) | + +**Output:** `IMAGE` + +### Adding Custom LUTs + +Drop any `.cube` 3D LUT file into the `luts/` folder inside this node pack and restart ComfyUI. It will appear in the LUT Loader dropdown automatically. + +--- + ## 📋 Requirements - **ComfyUI** (latest version recommended) @@ -181,7 +217,7 @@ Once installed, find the node in the ComfyUI menu: ## 🤝 Contributing -Pull requests are welcome! If you'd like to add new camera profiles or improve the processing pipeline, feel free to open an issue or PR. +Pull requests are welcome! If you'd like to add new camera profiles, custom LUTs, or improve the processing pipeline, feel free to open an issue or PR. ## 📄 License diff --git a/__init__.py b/__init__.py index 1d7f4fb..94238fb 100644 --- a/__init__.py +++ b/__init__.py @@ -11,18 +11,27 @@ Stages: 3. Camera-specific JPEG compression artifacts 4. cos⁴ lens vignetting with asymmetry 5. ISP color bias + +Also includes: +- LUT Loader: Load .cube 3D LUT files (bundled iPhone 15 Pro LUT) +- LUT Apply: Apply LUTs with adjustable strength """ -from .nodes import CameraForensicRealismEngine +from .nodes import CameraForensicRealismEngine, LUTLoader, LUTApply NODE_CLASS_MAPPINGS = { "CameraForensicRealismEngine": CameraForensicRealismEngine, + "LUTLoader": LUTLoader, + "LUTApply": LUTApply, } NODE_DISPLAY_NAME_MAPPINGS = { "CameraForensicRealismEngine": "🔬 Camera Forensic Realism Engine", + "LUTLoader": "🎨 LUT Loader", + "LUTApply": "🎨 LUT Apply", } WEB_DIRECTORY = "./js" __all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"] + diff --git a/generate_iphone_lut.py b/generate_iphone_lut.py new file mode 100644 index 0000000..6bed789 --- /dev/null +++ b/generate_iphone_lut.py @@ -0,0 +1,133 @@ +""" +Generate iPhone 15 Pro Standard .cube LUT + +Bakes the CameraForensicRealism color science pipeline into a portable +33x33x33 3D LUT (.cube format). This lets users apply the iPhone look +via a simple LUT instead of running the full processing pipeline. + +Run once: + python generate_iphone_lut.py + +Produces: luts/iPhone_15_Pro_Standard.cube +""" + +import numpy as np +import os +import sys +import time + +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +from forensic_engine import ( + apply_iphone_tone_curve, + apply_p3_color_rendering, + apply_white_balance, + apply_blue_shadow_tint, + apply_highlight_warmth, +) + + +def generate_iphone_lut(size: int = 33) -> tuple: + """Generate a 3D LUT by passing a color grid through the iPhone pipeline. + + We use the core color science stages (tone mapping, P3 color, white balance, + blue shadows, warm highlights) but skip spatial effects (sharpening, noise, + vignette, local tone mapping, skin detection, deep fusion) since those + depend on image structure, not just color values. + + Args: + size: LUT grid size (33 is standard for high-quality LUTs) + + Returns: + lut: np.ndarray (size, size, size, 3) + """ + print(f"Generating {size}x{size}x{size} iPhone 15 Pro LUT...") + t0 = time.time() + + # Create a 1D ramp [0, 1] with `size` steps + ramp = np.linspace(0.0, 1.0, size, dtype=np.float32) + + # Build the full 3D color grid: (size^3, 3) + # Order: R changes fastest, then G, then B (cube format standard) + r, g, b = np.meshgrid(ramp, ramp, ramp, indexing='ij') + # Reshape to (size, size, size, 3) then to a fake image (size^2, size, 3) + # so we can process it through the pipeline functions + grid = np.stack([r, g, b], axis=-1) # (size, size, size, 3) + + # Flatten to a 2D "image" for processing: (size*size, size, 3) + flat_h = size * size + flat_w = size + img = grid.reshape(flat_h, flat_w, 3) + + # --- Apply color science stages --- + # These are the stages that depend only on pixel color, not spatial context. + # Using iPhone 15 Pro Standard defaults at moderate strength. + + # White Balance (iPhone warm daylight bias) + img = apply_white_balance(img, strength=0.35, temperature=0.25, tint=0.0) + + # Tone Mapping (S-curve with highlight rolloff) + img = apply_iphone_tone_curve(img, strength=0.42, highlight_rolloff=0.5, + shadow_lift=0.4, contrast=0.5) + + # Display P3 Color Rendering + img = apply_p3_color_rendering(img, strength=0.35, saturation_boost=0.3, + warmth=0.3) + + # Blue Shadow Tint (iPhone blue-black look) + img = apply_blue_shadow_tint(img, strength=0.28, shadow_threshold=0.35, + blue_intensity=0.5) + + # Warm Highlights (golden highlight tint) + img = apply_highlight_warmth(img, strength=0.21, highlight_threshold=0.7, + warmth=0.5) + + # Reshape back to LUT grid + lut = img.reshape(size, size, size, 3) + lut = np.clip(lut, 0.0, 1.0).astype(np.float32) + + elapsed = time.time() - t0 + print(f"LUT generated in {elapsed:.2f}s") + + return lut + + +def write_cube_file(filepath: str, lut: np.ndarray, title: str = "iPhone 15 Pro Standard"): + """Write a 3D LUT to .cube format. + + Args: + filepath: Output .cube file path + lut: np.ndarray (N, N, N, 3) with values in [0, 1] + title: LUT title string + """ + n = lut.shape[0] + os.makedirs(os.path.dirname(filepath), exist_ok=True) + + with open(filepath, 'w') as f: + f.write(f'# Generated by ComfyUI-CameraForensicRealism\n') + f.write(f'# iPhone 15 Pro ISP Color Science LUT\n') + f.write(f'TITLE "{title}"\n') + f.write(f'LUT_3D_SIZE {n}\n') + f.write(f'DOMAIN_MIN 0.0 0.0 0.0\n') + f.write(f'DOMAIN_MAX 1.0 1.0 1.0\n') + f.write(f'\n') + + # Write data in R-fastest order (standard .cube ordering) + for bi in range(n): + for gi in range(n): + for ri in range(n): + r, g, b = lut[bi, gi, ri] + f.write(f'{r:.6f} {g:.6f} {b:.6f}\n') + + size_kb = os.path.getsize(filepath) / 1024 + print(f"Written: {filepath} ({size_kb:.1f} KB)") + + +if __name__ == "__main__": + lut_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "luts") + out_path = os.path.join(lut_dir, "iPhone_15_Pro_Standard.cube") + + lut = generate_iphone_lut(size=33) + write_cube_file(out_path, lut, title="iPhone 15 Pro Standard") + + print("\nDone! LUT file ready for use.") diff --git a/lut_engine.py b/lut_engine.py new file mode 100644 index 0000000..c3aa38e --- /dev/null +++ b/lut_engine.py @@ -0,0 +1,179 @@ +""" +LUT Engine — .cube 3D LUT Parser & Applicator +Zero-dependency implementation using numpy trilinear interpolation. + +Supports the Adobe/IRIDAS .cube format (1.0 spec): +- LUT_3D_SIZE +- DOMAIN_MIN / DOMAIN_MAX +- RGB data lines + +Usage: + lut, domain_min, domain_max = parse_cube_file("path/to/lut.cube") + result = apply_lut_with_strength(image, lut, domain_min, domain_max, strength=0.85) +""" + +import numpy as np +import os +from typing import Tuple + + +def parse_cube_file(filepath: str) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """Parse a .cube LUT file into a numpy array. + + Returns: + lut: np.ndarray of shape (N, N, N, 3) — the 3D LUT data + domain_min: np.ndarray of shape (3,) — input domain minimum + domain_max: np.ndarray of shape (3,) — input domain maximum + + The .cube format stores data in R-major order: + R increments fastest, then G, then B (outermost). + """ + if not os.path.isfile(filepath): + raise FileNotFoundError(f"LUT file not found: {filepath}") + + lut_size = None + domain_min = np.array([0.0, 0.0, 0.0], dtype=np.float32) + domain_max = np.array([1.0, 1.0, 1.0], dtype=np.float32) + data_lines = [] + + with open(filepath, 'r') as f: + for line in f: + line = line.strip() + + # Skip empty lines and comments + if not line or line.startswith('#'): + continue + + # Parse keywords + if line.startswith('TITLE'): + continue + elif line.startswith('LUT_3D_SIZE'): + lut_size = int(line.split()[-1]) + elif line.startswith('LUT_1D_SIZE'): + raise ValueError("1D LUTs are not supported. Please use a 3D .cube LUT.") + elif line.startswith('DOMAIN_MIN'): + parts = line.split()[1:] + domain_min = np.array([float(x) for x in parts], dtype=np.float32) + elif line.startswith('DOMAIN_MAX'): + parts = line.split()[1:] + domain_max = np.array([float(x) for x in parts], dtype=np.float32) + else: + # Try to parse as data line (3 floats) + try: + parts = line.split() + if len(parts) >= 3: + r, g, b = float(parts[0]), float(parts[1]), float(parts[2]) + data_lines.append([r, g, b]) + except ValueError: + continue # Skip unparseable lines + + if lut_size is None: + raise ValueError("No LUT_3D_SIZE found in .cube file") + + expected_count = lut_size ** 3 + if len(data_lines) != expected_count: + raise ValueError( + f"Expected {expected_count} data lines for size {lut_size}, " + f"got {len(data_lines)}" + ) + + # Reshape into (N, N, N, 3) — R fastest, then G, then B + lut = np.array(data_lines, dtype=np.float32).reshape(lut_size, lut_size, lut_size, 3) + + return lut, domain_min, domain_max + + +def apply_lut_3d(image: np.ndarray, lut: np.ndarray, + domain_min: np.ndarray, domain_max: np.ndarray) -> np.ndarray: + """Apply a 3D LUT to an image using trilinear interpolation. + + Args: + image: float32 array (H, W, 3) in [0, 1] + lut: float32 array (N, N, N, 3) + domain_min: float32 array (3,) + domain_max: float32 array (3,) + + Returns: + float32 array (H, W, 3) in [0, 1] + """ + h, w, _ = image.shape + n = lut.shape[0] # LUT size + + # Normalize input to LUT domain [0, N-1] + domain_range = domain_max - domain_min + domain_range = np.where(domain_range < 1e-10, 1.0, domain_range) + + # Scale image values to LUT index space + coords = (image - domain_min) / domain_range * (n - 1) + coords = np.clip(coords, 0, n - 1) + + # Integer indices for the 8 corners of the interpolation cube + idx0 = np.floor(coords).astype(np.int32) + idx1 = np.minimum(idx0 + 1, n - 1) + + # Fractional parts for interpolation weights + frac = coords - idx0.astype(np.float32) + + # Flatten for indexing + r0, g0, b0 = idx0[:, :, 0], idx0[:, :, 1], idx0[:, :, 2] + r1, g1, b1 = idx1[:, :, 0], idx1[:, :, 1], idx1[:, :, 2] + fr, fg, fb = frac[:, :, 0], frac[:, :, 1], frac[:, :, 2] + + # Trilinear interpolation using the 8 corners + # c000 = lut[b0, g0, r0], c100 = lut[b0, g0, r1], etc. + c000 = lut[b0, g0, r0] + c100 = lut[b0, g0, r1] + c010 = lut[b0, g1, r0] + c110 = lut[b0, g1, r1] + c001 = lut[b1, g0, r0] + c101 = lut[b1, g0, r1] + c011 = lut[b1, g1, r0] + c111 = lut[b1, g1, r1] + + # Expand fractional dims for broadcasting + fr = fr[:, :, np.newaxis] + fg = fg[:, :, np.newaxis] + fb = fb[:, :, np.newaxis] + + # Interpolate along R axis + c00 = c000 * (1 - fr) + c100 * fr + c01 = c001 * (1 - fr) + c101 * fr + c10 = c010 * (1 - fr) + c110 * fr + c11 = c011 * (1 - fr) + c111 * fr + + # Interpolate along G axis + c0 = c00 * (1 - fg) + c10 * fg + c1 = c01 * (1 - fg) + c11 * fg + + # Interpolate along B axis + result = c0 * (1 - fb) + c1 * fb + + return np.clip(result, 0.0, 1.0).astype(np.float32) + + +def apply_lut_with_strength(image: np.ndarray, lut: np.ndarray, + domain_min: np.ndarray, domain_max: np.ndarray, + strength: float = 1.0) -> np.ndarray: + """Apply a 3D LUT with adjustable strength (opacity). + + Args: + image: float32 array (H, W, 3) in [0, 1] + lut: float32 array (N, N, N, 3) + domain_min: float32 array (3,) + domain_max: float32 array (3,) + strength: 0.0 = original image, 1.0 = full LUT + + Returns: + float32 array (H, W, 3) in [0, 1] + """ + if strength <= 0: + return image.copy() + + lut_result = apply_lut_3d(image, lut, domain_min, domain_max) + + if strength >= 1.0: + return lut_result + + # Linear blend + blended = image * (1.0 - strength) + lut_result * strength + return np.clip(blended, 0.0, 1.0).astype(np.float32) diff --git a/luts/iPhone_15_Pro_Standard.cube b/luts/iPhone_15_Pro_Standard.cube new file mode 100644 index 0000000..288ae93 --- /dev/null +++ b/luts/iPhone_15_Pro_Standard.cube @@ -0,0 +1,35944 @@ +# Generated by 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============================================================================ +# LUT Loader Node +# ============================================================================ + +class LUTLoader: + """ + LUT Loader — Load .cube 3D LUT files. + Scans the bundled luts/ folder and presents a dropdown selector. + """ + + LUTS_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "luts") + + def __init__(self): + pass + + @classmethod + def _get_lut_list(cls): + """Discover all .cube files in the luts/ directory.""" + luts_dir = cls.LUTS_DIR + if not os.path.isdir(luts_dir): + return ["No LUTs found"] + files = [f for f in os.listdir(luts_dir) + if f.lower().endswith('.cube') and os.path.isfile(os.path.join(luts_dir, f))] + files.sort() + return files if files else ["No LUTs found"] + + @classmethod + def INPUT_TYPES(cls): + lut_files = cls._get_lut_list() + return { + "required": { + "lut_name": (lut_files, { + "tooltip": "Select a .cube LUT file from the bundled luts/ folder" + }), + }, + } + + RETURN_TYPES = ("LUT_DATA",) + RETURN_NAMES = ("lut_data",) + FUNCTION = "load_lut" + CATEGORY = "image/forensic" + + DESCRIPTION = ( + "LUT Loader\n\n" + "Loads a .cube 3D LUT file from the bundled luts/ folder.\n" + "Connect the output to a LUT Apply node.\n\n" + "Bundled LUT: iPhone 15 Pro Standard — baked from the\n" + "Camera Forensic Realism Engine's color science pipeline." + ) + + def load_lut(self, lut_name: str): + """Load and parse the selected .cube LUT file.""" + from lut_engine import parse_cube_file + + filepath = os.path.join(self.LUTS_DIR, lut_name) + + if not os.path.isfile(filepath): + raise FileNotFoundError(f"LUT file not found: {filepath}") + + lut, domain_min, domain_max = parse_cube_file(filepath) + + lut_data = { + "lut": lut, + "domain_min": domain_min, + "domain_max": domain_max, + "name": lut_name, + "path": filepath, + } + + print(f"🎨 LUT Loader: Loaded '{lut_name}' ({lut.shape[0]}³ grid)") + return (lut_data,) + + +# ============================================================================ +# LUT Apply Node +# ============================================================================ + +class LUTApply: + """ + LUT Apply — Apply a loaded 3D LUT to an image with adjustable strength. + """ + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "lut_data": ("LUT_DATA",), + "strength": ("FLOAT", { + "default": 0.85, "min": 0.0, "max": 1.0, "step": 0.01, + "display": "slider", + "tooltip": "LUT intensity. 0.0 = original, 1.0 = full LUT effect. 0.85 recommended." + }), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + FUNCTION = "apply_lut" + CATEGORY = "image/forensic" + + DESCRIPTION = ( + "LUT Apply\n\n" + "Applies a 3D LUT (from LUT Loader) to your image using\n" + "trilinear interpolation for smooth, accurate color grading.\n\n" + "Strength controls the blend between original and LUT-graded.\n" + "0.85 = recommended for natural iPhone look." + ) + + def apply_lut(self, image: torch.Tensor, lut_data: dict, strength: float): + """Apply the loaded LUT to each image in the batch.""" + from lut_engine import apply_lut_with_strength + + lut = lut_data["lut"] + domain_min = lut_data["domain_min"] + domain_max = lut_data["domain_max"] + lut_name = lut_data.get("name", "Unknown") + + batch_size = image.shape[0] + results = [] + + for i in range(batch_size): + img_np = image[i].cpu().numpy().astype(np.float32) + img_np = np.clip(img_np, 0.0, 1.0) + + processed = apply_lut_with_strength(img_np, lut, domain_min, domain_max, strength) + results.append(torch.from_numpy(processed)) + + output = torch.stack(results, dim=0) + print(f"🎨 LUT Apply: Applied '{lut_name}' at {strength:.0%} strength") + return (output,) + diff --git a/test_node.py b/test_node.py index 5bd8b6e..ed1e961 100644 --- a/test_node.py +++ b/test_node.py @@ -144,6 +144,7 @@ def test_node(): color_saturation=0.3, color_warmth=0.3, enable_local_tone=True, local_tone_strength=0.35, detail_boost=0.4, enable_skin_rendering=True, skin_strength=0.5, skin_warmth=0.4, + enable_deep_fusion=True, fusion_strength=0.6, fusion_texture_freq=0.5, enable_white_balance=True, wb_strength=0.5, wb_temperature=0.25, wb_tint=0.0, enable_color_grading=True, blue_shadows=0.4, warm_highlights=0.3, enable_sharpening=True, sharpen_strength=0.3, @@ -168,12 +169,93 @@ def test_js_exists(): return False +def test_lut_parse(): + print("\n--- Test 11: LUT Parse ---") + from lut_engine import parse_cube_file + lut_path = os.path.join(os.path.dirname(__file__), "luts", "iPhone_15_Pro_Standard.cube") + if not os.path.exists(lut_path): + print(f" [FAIL] LUT file missing: {lut_path}"); return False + lut, dmin, dmax = parse_cube_file(lut_path) + ok = True + if lut.shape != (33, 33, 33, 3): + print(f" [FAIL] Shape: {lut.shape}"); ok = False + if lut.min() < 0 or lut.max() > 1: + print(f" [FAIL] Range: [{lut.min():.4f}, {lut.max():.4f}]"); ok = False + if np.any(np.isnan(lut)): + print(f" [FAIL] NaN in LUT"); ok = False + if ok: print(f" [PASS] LUT parsed: {lut.shape}, range [{lut.min():.4f}, {lut.max():.4f}]") + return ok + + +def test_lut_apply(): + print("\n--- Test 12: LUT Apply ---") + from lut_engine import parse_cube_file, apply_lut_3d + lut_path = os.path.join(os.path.dirname(__file__), "luts", "iPhone_15_Pro_Standard.cube") + lut, dmin, dmax = parse_cube_file(lut_path) + img = make_test_image() + result = apply_lut_3d(img, lut, dmin, dmax) + ok = check(result, img.shape, "LUT apply") + diff = np.mean(np.abs(result - img)) + if diff < 1e-6: + print(f" [FAIL] LUT had no effect (diff={diff:.6f})"); ok = False + else: + print(f" [PASS] LUT modified image (mean diff={diff:.4f})") + return ok + + +def test_lut_strength(): + print("\n--- Test 13: LUT Strength ---") + from lut_engine import parse_cube_file, apply_lut_with_strength + lut_path = os.path.join(os.path.dirname(__file__), "luts", "iPhone_15_Pro_Standard.cube") + lut, dmin, dmax = parse_cube_file(lut_path) + img = make_test_image() + ok = True + # Strength 0 = original + r0 = apply_lut_with_strength(img, lut, dmin, dmax, 0.0) + diff0 = np.max(np.abs(r0 - img)) + if diff0 > 1e-6: + print(f" [FAIL] Strength 0 changed image (max diff={diff0:.6f})"); ok = False + else: + print(f" [PASS] Strength 0.0 = original") + # Strength 0.5 = blend + r05 = apply_lut_with_strength(img, lut, dmin, dmax, 0.5) + r10 = apply_lut_with_strength(img, lut, dmin, dmax, 1.0) + diff05 = np.mean(np.abs(r05 - img)) + diff10 = np.mean(np.abs(r10 - img)) + if diff05 < diff10: + print(f" [PASS] Strength 0.5 ({diff05:.4f}) < 1.0 ({diff10:.4f})") + else: + print(f" [FAIL] Strength scaling broken"); ok = False + return ok + + +def test_lut_nodes(): + print("\n--- Test 14: LUT Nodes ---") + from nodes import LUTLoader, LUTApply + ok = True + # LUTLoader + loader_inputs = LUTLoader.INPUT_TYPES()["required"] + if "lut_name" in loader_inputs: + print(f" [PASS] LUTLoader has 'lut_name' input") + else: + print(f" [FAIL] LUTLoader missing 'lut_name'"); ok = False + # LUTApply + apply_inputs = LUTApply.INPUT_TYPES()["required"] + for name in ["image", "lut_data", "strength"]: + if name in apply_inputs: + print(f" [PASS] LUTApply has '{name}' input") + else: + print(f" [FAIL] LUTApply missing '{name}'"); ok = False + return ok + + if __name__ == "__main__": print("=" * 60) print("Camera Forensic Realism Engine v3 - Test Suite") print("=" * 60) tests = [test_tone, test_p3, test_local, test_skin, test_blue_shadows, - test_warm_highlights, test_wb, test_pipeline, test_node, test_js_exists] + test_warm_highlights, test_wb, test_pipeline, test_node, test_js_exists, + test_lut_parse, test_lut_apply, test_lut_strength, test_lut_nodes] results = [] for t in tests: try: results.append(t()) @@ -186,3 +268,4 @@ if __name__ == "__main__": print(f"Results: {p}/{len(results)} tests passed") print("ALL PASSED!" if p == len(results) else "Some failed") print("=" * 60) +