diff --git a/examples/documentation/kiko_film_grain.md b/examples/documentation/kiko_film_grain.md
new file mode 100644
index 0000000..35c285a
--- /dev/null
+++ b/examples/documentation/kiko_film_grain.md
@@ -0,0 +1,125 @@
+# Kiko Film Grain
+
+## Overview
+The **Kiko Film Grain** node applies realistic film grain effects to images, simulating the aesthetic of analog film photography. It provides comprehensive controls for grain size, intensity, color saturation, and shadow lifting to achieve various film looks.
+
+## Node Details
+- **Category**: ComfyAssets/image
+- **Node Name**: KikoFilmGrain
+- **Display Name**: Kiko Film Grain
+
+## Inputs
+
+### Required
+- **image** (`IMAGE`)
+  - The input image to apply film grain to
+  - Supports batch processing
+  - Preserves alpha channel if present
+
+### Parameters
+- **scale** (`FLOAT`)
+  - Controls the size of the grain pattern
+  - Range: 0.25 to 2.0
+  - Default: 0.5
+  - Lower values = finer grain, higher values = coarser grain
+
+- **strength** (`FLOAT`)
+  - Intensity of the grain effect
+  - Range: 0.0 to 10.0
+  - Default: 0.5
+  - 0.0 = no grain, higher values = more pronounced grain
+
+- **saturation** (`FLOAT`)
+  - Color saturation of the grain
+  - Range: 0.0 to 2.0
+  - Default: 0.7
+  - 0.0 = monochrome grain, 1.0 = full color, >1.0 = oversaturated
+
+- **toe** (`FLOAT`)
+  - Lifts blacks/shadows for a film-like look
+  - Range: -0.2 to 0.5
+  - Default: 0.0
+  - Positive values lift shadows, negative values crush blacks
+
+- **seed** (`INT`)
+  - Random seed for grain pattern generation
+  - Range: 0 to maximum integer
+  - Default: 0
+  - Use for reproducible grain patterns
+
+## Outputs
+- **image** (`IMAGE`)
+  - The processed image with film grain applied
+  - Same dimensions and batch size as input
+  - Alpha channel preserved if present
+
+## Usage Examples
+
+### Subtle Film Look
+```
+Scale: 0.5
+Strength: 0.3
+Saturation: 0.8
+Toe: 0.05
+```
+Creates a subtle, fine-grained film aesthetic suitable for portraits.
+
+### Vintage Film
+```
+Scale: 1.0
+Strength: 0.8
+Saturation: 0.5
+Toe: 0.15
+```
+Simulates vintage film with moderate grain and lifted shadows.
+
+### High ISO Film
+```
+Scale: 0.75
+Strength: 1.5
+Saturation: 0.6
+Toe: 0.1
+```
+Emulates high ISO film stock with pronounced grain.
+
+### Black & White Film
+```
+Scale: 0.6
+Strength: 0.6
+Saturation: 0.0
+Toe: 0.08
+```
+Creates monochrome grain perfect for black and white photography.
+
+## Technical Details
+
+### Improvements Over Standard Implementations
+1. **Pure PyTorch Operations**: No OpenCV dependencies, better GPU utilization
+2. **ITU-R BT.709 Color Space**: Accurate color conversion for grain application
+3. **Screen Blend Mode**: Preserves highlights better than multiply blending
+4. **Channel-Specific Weighting**: Film grain is stronger in blue channel (3x), moderate in red (2x), matching real film characteristics
+5. **Efficient Memory Management**: Minimizes tensor copies and conversions
+
+### Algorithm Overview
+1. Generate random noise at specified scale
+2. Convert to YCbCr color space for realistic grain distribution
+3. Apply different blur kernels to each channel:
+   - Y (luminance): 3x3 kernel for fine detail
+   - Cb (blue-yellow): 15x15 kernel for color noise
+   - Cr (red-green): 11x11 kernel for color noise
+4. Convert back to RGB and apply strength/saturation
+5. Use screen blend mode to combine with original image
+6. Apply toe adjustment for film-like shadow response
+
+## Tips
+- Start with low strength values (0.2-0.5) and adjust upward
+- For color images, saturation between 0.5-0.8 looks most natural
+- Combine with color grading nodes for complete film emulation
+- Use consistent seed values across batch for uniform grain
+- Scale parameter affects both grain size and render performance (smaller scale = more computation)
+
+## Compatibility
+- Works with any image format supported by ComfyUI
+- Preserves image properties (alpha channel, batch size)
+- Compatible with both RGB and RGBA images
+- Efficient batch processing support
\ No newline at end of file
diff --git a/examples/workflows/kiko_film_grain_example.json b/examples/workflows/kiko_film_grain_example.json
new file mode 100644
index 0000000..74dbcdd
--- /dev/null
+++ b/examples/workflows/kiko_film_grain_example.json
@@ -0,0 +1,165 @@
+{
+  "id": "kiko-film-grain-example",
+  "revision": 0,
+  "last_node_id": 4,
+  "last_link_id": 2,
+  "nodes": [
+    {
+      "id": 1,
+      "type": "LoadImage",
+      "pos": [
+        50,
+        100
+      ],
+      "size": [
+        350,
+        450
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [1],
+          "shape": 3,
+          "label": "IMAGE"
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null,
+          "shape": 3,
+          "label": "MASK"
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "example.png"
+      ]
+    },
+    {
+      "id": 2,
+      "type": "KikoFilmGrain",
+      "pos": [
+        450,
+        100
+      ],
+      "size": [
+        315,
+        202
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 1
+        }
+      ],
+      "outputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "links": [2],
+          "shape": 3,
+          "label": "image",
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "cnr_id": "kikotools",
+        "Node name for S&R": "KikoFilmGrain"
+      },
+      "widgets_values": [
+        0.5,
+        0.5,
+        0.7,
+        0.0,
+        0
+      ],
+      "color": "#223",
+      "bgcolor": "#335"
+    },
+    {
+      "id": 3,
+      "type": "PreviewImage",
+      "pos": [
+        850,
+        100
+      ],
+      "size": [
+        350,
+        450
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 2
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 4,
+      "type": "Note",
+      "pos": [
+        450,
+        350
+      ],
+      "size": [
+        315,
+        150
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "properties": {
+        "text": ""
+      },
+      "widgets_values": [
+        "Kiko Film Grain Example\n\nThis workflow demonstrates the film grain effect.\n\nAdjust parameters:\n- Scale: Grain size (0.25-2.0)\n- Strength: Intensity (0.0-10.0)\n- Saturation: Color amount (0.0-2.0)\n- Toe: Shadow lifting (-0.2-0.5)\n- Seed: Random pattern"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    }
+  ],
+  "links": [
+    [
+      1,
+      1,
+      0,
+      2,
+      0,
+      "IMAGE"
+    ],
+    [
+      2,
+      2,
+      0,
+      3,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 1.0,
+      "offset": [0, 0]
+    }
+  },
+  "version": 0.4
+}
\ No newline at end of file
diff --git a/kikotools/__init__.py b/kikotools/__init__.py
index a58ff0d..490b964 100644
--- a/kikotools/__init__.py
+++ b/kikotools/__init__.py
@@ -14,6 +14,7 @@ from .tools.image_scale_down_by import ImageScaleDownByNode
 from .tools.gemini_prompt import GeminiPromptNode
 from .tools.display_any import DisplayAnyNode
 from .tools.display_text import DisplayTextNode
+from .tools.kiko_film_grain import KikoFilmGrainNode
 from .tools.xyz_helpers import (
     SamplerSelectHelperNode,
     SchedulerSelectHelperNode,
@@ -37,6 +38,7 @@ NODE_CLASS_MAPPINGS = {
     "GeminiPrompt": GeminiPromptNode,
     "DisplayAny": DisplayAnyNode,
     "DisplayText": DisplayTextNode,
+    "KikoFilmGrain": KikoFilmGrainNode,
     "SamplerSelectHelper": SamplerSelectHelperNode,
     "SchedulerSelectHelper": SchedulerSelectHelperNode,
     "TextEncodeSamplerParams": TextEncodeSamplerParamsNode,
@@ -58,6 +60,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "GeminiPrompt": "Gemini Prompt Engineer",
     "DisplayAny": "Display Any",
     "DisplayText": "Display Text",
+    "KikoFilmGrain": "Kiko Film Grain",
     "SamplerSelectHelper": "Sampler Select Helper",
     "SchedulerSelectHelper": "Scheduler Select Helper",
     "TextEncodeSamplerParams": "Text Encode for Sampler Params",
diff --git a/kikotools/tools/kiko_film_grain/__init__.py b/kikotools/tools/kiko_film_grain/__init__.py
new file mode 100644
index 0000000..45692de
--- /dev/null
+++ b/kikotools/tools/kiko_film_grain/__init__.py
@@ -0,0 +1,3 @@
+from .node import KikoFilmGrainNode
+
+__all__ = ["KikoFilmGrainNode"]
diff --git a/kikotools/tools/kiko_film_grain/logic.py b/kikotools/tools/kiko_film_grain/logic.py
new file mode 100644
index 0000000..4ce5376
--- /dev/null
+++ b/kikotools/tools/kiko_film_grain/logic.py
@@ -0,0 +1,221 @@
+import torch
+import torch.nn.functional as F
+
+
+def rgb_to_ycbcr(rgb: torch.Tensor) -> torch.Tensor:
+    """
+    Convert RGB tensor to YCbCr color space.
+
+    Args:
+        rgb: Tensor of shape [B, H, W, C] in range [0, 1]
+
+    Returns:
+        YCbCr tensor of same shape
+    """
+    ycbcr = rgb.detach().clone()
+    r, g, b = rgb[:, :, :, 0], rgb[:, :, :, 1], rgb[:, :, :, 2]
+
+    # ITU-R BT.709 coefficients
+    ycbcr[:, :, :, 0] = 0.2126 * r + 0.7152 * g + 0.0722 * b  # Y
+    ycbcr[:, :, :, 1] = -0.1146 * r - 0.3854 * g + 0.5 * b  # Cb
+    ycbcr[:, :, :, 2] = 0.5 * r - 0.4542 * g - 0.0458 * b  # Cr
+
+    return ycbcr
+
+
+def ycbcr_to_rgb(ycbcr: torch.Tensor) -> torch.Tensor:
+    """
+    Convert YCbCr tensor to RGB color space.
+
+    Args:
+        ycbcr: Tensor of shape [B, H, W, C]
+
+    Returns:
+        RGB tensor of same shape in range [0, 1]
+    """
+    rgb = ycbcr.detach().clone()
+    y, cb, cr = ycbcr[:, :, :, 0], ycbcr[:, :, :, 1], ycbcr[:, :, :, 2]
+
+    rgb[:, :, :, 0] = y + 1.5748 * cr  # R
+    rgb[:, :, :, 1] = y - 0.1873 * cb - 0.4681 * cr  # G
+    rgb[:, :, :, 2] = y + 1.8556 * cb  # B
+
+    return torch.clamp(rgb, 0, 1)
+
+
+def apply_gaussian_blur(tensor: torch.Tensor, kernel_size: int) -> torch.Tensor:
+    """
+    Apply Gaussian blur to a tensor using PyTorch operations.
+
+    Args:
+        tensor: Tensor of shape [B, H, W, C]
+        kernel_size: Size of the Gaussian kernel (must be odd)
+
+    Returns:
+        Blurred tensor of same shape
+    """
+    if kernel_size <= 1:
+        return tensor
+
+    # Ensure kernel size is odd
+    kernel_size = kernel_size if kernel_size % 2 == 1 else kernel_size + 1
+
+    # Create Gaussian kernel
+    sigma = kernel_size / 3.0
+    x = torch.arange(kernel_size, dtype=torch.float32) - kernel_size // 2
+    gauss = torch.exp(-x.pow(2) / (2 * sigma**2))
+    gauss = gauss / gauss.sum()
+
+    # Create 2D kernel
+    kernel = gauss.unsqueeze(0) * gauss.unsqueeze(1)
+    kernel = kernel.unsqueeze(0).unsqueeze(0)
+
+    # Apply blur per channel
+    batch_size, h, w, channels = tensor.shape
+    tensor_reshaped = tensor.permute(0, 3, 1, 2)  # [B, C, H, W]
+
+    # Expand kernel for all channels
+    kernel = kernel.repeat(channels, 1, 1, 1)
+
+    # Apply convolution with padding
+    padding = kernel_size // 2
+    blurred = F.conv2d(tensor_reshaped, kernel, padding=padding, groups=channels)
+
+    return blurred.permute(0, 2, 3, 1)  # Back to [B, H, W, C]
+
+
+def generate_grain_texture(
+    batch_size: int, height: int, width: int, scale: float, seed: int
+) -> torch.Tensor:
+    """
+    Generate base grain texture at specified scale.
+
+    Args:
+        batch_size: Number of images in batch
+        height: Target height
+        width: Target width
+        scale: Scale factor for grain size (larger = coarser grain)
+        seed: Random seed for reproducibility
+
+    Returns:
+        Grain texture tensor of shape [B, H/scale, W/scale, 3]
+    """
+    torch.manual_seed(seed)
+
+    grain_height = max(1, int(height / scale))
+    grain_width = max(1, int(width / scale))
+
+    # Generate random noise
+    grain = torch.rand(batch_size, grain_height, grain_width, 3)
+
+    return grain
+
+
+def apply_film_grain(
+    image: torch.Tensor,
+    scale: float = 0.5,
+    strength: float = 0.5,
+    saturation: float = 0.7,
+    toe: float = 0.0,
+    seed: int = 0,
+) -> torch.Tensor:
+    """
+    Apply film grain effect to an image with improved algorithms.
+
+    Improvements over original:
+    - Better color space conversion using ITU-R BT.709 coefficients
+    - More efficient Gaussian blur using PyTorch convolutions
+    - Improved grain mixing with better channel weighting
+    - Preserves alpha channel if present
+    - Better memory efficiency
+
+    Args:
+        image: Input tensor of shape [B, H, W, C] in range [0, 1]
+        scale: Grain size (0.25-2.0, higher = coarser grain)
+        strength: Grain intensity (0.0-10.0)
+        saturation: Color saturation of grain (0.0-2.0)
+        toe: Lift blacks/shadows (-0.2-0.5)
+        seed: Random seed for reproducibility
+
+    Returns:
+        Image with film grain applied
+    """
+    if strength == 0.0:
+        return image
+
+    # Handle empty batch
+    if image.shape[0] == 0:
+        return image
+
+    result = image.detach().clone()
+    has_alpha = image.shape[-1] == 4
+
+    # Generate grain texture
+    grain = generate_grain_texture(
+        image.shape[0], image.shape[1], image.shape[2], scale, seed
+    )
+
+    # Convert to YCbCr for better grain application
+    grain_ycbcr = rgb_to_ycbcr(grain)
+
+    # Apply different blur kernels to each channel for more realistic grain
+    # Y channel - fine detail
+    grain_ycbcr[:, :, :, 0] = apply_gaussian_blur(
+        grain_ycbcr[:, :, :, 0:1], kernel_size=3
+    ).squeeze(-1)
+
+    # Cb channel - medium blur for color noise
+    grain_ycbcr[:, :, :, 1] = apply_gaussian_blur(
+        grain_ycbcr[:, :, :, 1:2], kernel_size=15
+    ).squeeze(-1)
+
+    # Cr channel - slightly less blur
+    grain_ycbcr[:, :, :, 2] = apply_gaussian_blur(
+        grain_ycbcr[:, :, :, 2:3], kernel_size=11
+    ).squeeze(-1)
+
+    # Convert back to RGB
+    grain = ycbcr_to_rgb(grain_ycbcr)
+
+    # Center grain around 0 and apply strength
+    grain = (grain - 0.5) * strength
+
+    # Apply channel-specific weighting for more realistic film grain
+    # Film grain is typically stronger in blue channel, moderate in red
+    grain[:, :, :, 0] *= 2.0  # Red channel
+    grain[:, :, :, 1] *= 1.0  # Green channel (reference)
+    grain[:, :, :, 2] *= 3.0  # Blue channel
+
+    # Add 1 to make it multiplicative
+    grain = grain + 1.0
+
+    # Apply saturation control
+    # Extract luminance for desaturation mixing
+    luminance = grain[:, :, :, 1:2]  # Use green channel as approximation
+    grain = grain * saturation + luminance * (1 - saturation)
+
+    # Interpolate grain to match image size if needed
+    if grain.shape[1] != image.shape[1] or grain.shape[2] != image.shape[2]:
+        grain = F.interpolate(
+            grain.permute(0, 3, 1, 2),
+            size=(image.shape[1], image.shape[2]),
+            mode="bilinear",
+            align_corners=False,
+        ).permute(0, 2, 3, 1)
+
+    # Apply grain using screen blend mode: 1 - (1 - image) * grain
+    # This preserves highlights better than multiply
+    if has_alpha:
+        # Only apply to RGB channels
+        result[:, :, :, :3] = 1 - (1 - result[:, :, :, :3]) * grain
+    else:
+        result = 1 - (1 - result[:, :, :, :3]) * grain
+
+    # Apply toe adjustment (lift blacks)
+    if has_alpha:
+        result[:, :, :, :3] = result[:, :, :, :3] * (1 - toe) + toe
+    else:
+        result = result * (1 - toe) + toe
+
+    # Ensure output is in valid range
+    return torch.clamp(result, 0, 1)
diff --git a/kikotools/tools/kiko_film_grain/node.py b/kikotools/tools/kiko_film_grain/node.py
new file mode 100644
index 0000000..05450c5
--- /dev/null
+++ b/kikotools/tools/kiko_film_grain/node.py
@@ -0,0 +1,123 @@
+import torch
+from typing import Dict, Any, Tuple
+
+from ...base import ComfyAssetsBaseNode
+from .logic import apply_film_grain
+
+
+class KikoFilmGrainNode(ComfyAssetsBaseNode):
+    """
+    Apply realistic film grain effect to images.
+
+    This node simulates the grain patterns found in analog film photography.
+    It provides controls for grain size, intensity, color saturation, and
+    shadow lifting (toe) to achieve various film looks.
+
+    Improvements over reference implementation:
+    - More efficient PyTorch-based blur operations
+    - Better memory management for large batches
+    - Preserves alpha channel when present
+    - Improved grain mixing algorithm
+    - ITU-R BT.709 color space conversion
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "scale": (
+                    "FLOAT",
+                    {
+                        "default": 0.5,
+                        "min": 0.25,
+                        "max": 2.0,
+                        "step": 0.05,
+                        "display": "slider",
+                        "description": "Grain size - smaller values create finer grain",
+                    },
+                ),
+                "strength": (
+                    "FLOAT",
+                    {
+                        "default": 0.5,
+                        "min": 0.0,
+                        "max": 10.0,
+                        "step": 0.01,
+                        "display": "slider",
+                        "description": "Intensity of the grain effect",
+                    },
+                ),
+                "saturation": (
+                    "FLOAT",
+                    {
+                        "default": 0.7,
+                        "min": 0.0,
+                        "max": 2.0,
+                        "step": 0.01,
+                        "display": "slider",
+                        "description": "Color saturation of the grain (0=monochrome)",
+                    },
+                ),
+                "toe": (
+                    "FLOAT",
+                    {
+                        "default": 0.0,
+                        "min": -0.2,
+                        "max": 0.5,
+                        "step": 0.001,
+                        "display": "slider",
+                        "description": "Lift blacks/shadows for a film-like look",
+                    },
+                ),
+                "seed": (
+                    "INT",
+                    {
+                        "default": 0,
+                        "min": 0,
+                        "max": 0xFFFFFFFFFFFFFFFF,
+                        "description": "Random seed for grain pattern generation",
+                    },
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "apply_grain"
+    CATEGORY = "ComfyAssets/image"
+    DESCRIPTION = "Apply realistic film grain effect with customizable parameters"
+
+    def apply_grain(
+        self,
+        image: torch.Tensor,
+        scale: float,
+        strength: float,
+        saturation: float,
+        toe: float,
+        seed: int,
+    ) -> Tuple[torch.Tensor]:
+        """
+        Apply film grain effect to the input image.
+
+        Args:
+            image: Input image tensor [B, H, W, C]
+            scale: Grain size factor (0.25-2.0)
+            strength: Grain intensity (0.0-10.0)
+            saturation: Color saturation of grain (0.0-2.0)
+            toe: Shadow lifting amount (-0.2-0.5)
+            seed: Random seed for reproducibility
+
+        Returns:
+            Tuple containing the processed image tensor
+        """
+        result = apply_film_grain(
+            image=image,
+            scale=scale,
+            strength=strength,
+            saturation=saturation,
+            toe=toe,
+            seed=seed,
+        )
+
+        return (result,)
diff --git a/tests/unit/tools/test_kiko_film_grain.py b/tests/unit/tools/test_kiko_film_grain.py
new file mode 100644
index 0000000..01d271a
--- /dev/null
+++ b/tests/unit/tools/test_kiko_film_grain.py
@@ -0,0 +1,249 @@
+import pytest
+import torch
+import numpy as np
+from unittest.mock import MagicMock
+
+from kikotools.tools.kiko_film_grain.logic import (
+    apply_film_grain,
+    generate_grain_texture,
+    rgb_to_ycbcr,
+    ycbcr_to_rgb,
+    apply_gaussian_blur,
+)
+
+
+class TestColorSpaceConversion:
+    def test_rgb_to_ycbcr_conversion(self):
+        rgb = torch.tensor([[[[1.0, 0.0, 0.0]]]])  # Pure red
+        ycbcr = rgb_to_ycbcr(rgb)
+
+        assert ycbcr.shape == rgb.shape
+        assert 0.0 <= ycbcr[0, 0, 0, 0] <= 1.0  # Y channel
+
+    def test_ycbcr_to_rgb_conversion(self):
+        ycbcr = torch.tensor([[[[0.5, 0.0, 0.0]]]])
+        rgb = ycbcr_to_rgb(ycbcr)
+
+        assert rgb.shape == ycbcr.shape
+        assert rgb.min() >= 0.0
+        assert rgb.max() <= 1.0
+
+    def test_rgb_ycbcr_round_trip(self):
+        original = torch.rand(1, 4, 4, 3)
+        converted = ycbcr_to_rgb(rgb_to_ycbcr(original))
+
+        # Should be approximately equal after round trip
+        assert torch.allclose(original, converted, atol=0.01)
+
+
+class TestGaussianBlur:
+    def test_apply_gaussian_blur_no_blur(self):
+        image = torch.rand(1, 10, 10, 3)
+        blurred = apply_gaussian_blur(image, kernel_size=1)
+
+        # Kernel size 1 should not blur
+        assert torch.allclose(image, blurred, atol=0.001)
+
+    def test_apply_gaussian_blur_with_blur(self):
+        # Create sharp edge image
+        image = torch.zeros(1, 10, 10, 1)
+        image[:, :5, :, :] = 1.0
+
+        blurred = apply_gaussian_blur(image, kernel_size=3)
+
+        # Edge should be smoothed
+        edge_original = image[0, 4:6, 5, 0]
+        edge_blurred = blurred[0, 4:6, 5, 0]
+        # White side near edge should be darker due to blur
+        assert edge_blurred[0] < edge_original[0]
+        # Black side near edge should be lighter due to blur
+        assert edge_blurred[1] > edge_original[1]
+
+    def test_apply_gaussian_blur_preserves_shape(self):
+        for shape in [(1, 32, 32, 3), (2, 64, 128, 1), (4, 16, 16, 3)]:
+            image = torch.rand(*shape)
+            blurred = apply_gaussian_blur(image, kernel_size=5)
+            assert blurred.shape == image.shape
+
+
+class TestGrainGeneration:
+    def test_generate_grain_texture_shape(self):
+        batch_size = 2
+        height = 64
+        width = 128
+        scale = 2.0
+
+        grain = generate_grain_texture(batch_size, height, width, scale, seed=42)
+
+        expected_height = int(height / scale)
+        expected_width = int(width / scale)
+        assert grain.shape == (batch_size, expected_height, expected_width, 3)
+
+    def test_generate_grain_texture_deterministic(self):
+        grain1 = generate_grain_texture(1, 32, 32, 1.0, seed=123)
+        grain2 = generate_grain_texture(1, 32, 32, 1.0, seed=123)
+
+        assert torch.allclose(grain1, grain2)
+
+    def test_generate_grain_texture_different_seeds(self):
+        grain1 = generate_grain_texture(1, 32, 32, 1.0, seed=123)
+        grain2 = generate_grain_texture(1, 32, 32, 1.0, seed=456)
+
+        assert not torch.allclose(grain1, grain2)
+
+    def test_generate_grain_texture_scale_factor(self):
+        height, width = 64, 64
+        grain_1x = generate_grain_texture(1, height, width, 1.0, seed=42)
+        grain_2x = generate_grain_texture(1, height, width, 2.0, seed=42)
+
+        assert grain_1x.shape[1] == height
+        assert grain_2x.shape[1] == height // 2
+
+
+class TestFilmGrainApplication:
+    def test_apply_film_grain_no_effect(self):
+        image = torch.rand(1, 32, 32, 3)
+
+        # Zero strength should have no effect
+        result = apply_film_grain(
+            image, scale=1.0, strength=0.0, saturation=1.0, toe=0.0, seed=42
+        )
+
+        assert torch.allclose(image, result, atol=0.001)
+
+    def test_apply_film_grain_with_strength(self):
+        image = torch.ones(1, 32, 32, 3) * 0.5
+
+        result = apply_film_grain(
+            image, scale=1.0, strength=1.0, saturation=1.0, toe=0.0, seed=42
+        )
+
+        # Should add variation
+        assert not torch.allclose(image, result)
+        # Should remain in valid range
+        assert result.min() >= 0.0
+        assert result.max() <= 1.0
+
+    def test_apply_film_grain_saturation_effect(self):
+        image = torch.ones(1, 32, 32, 3) * 0.5
+
+        # Full saturation
+        result_saturated = apply_film_grain(
+            image, scale=1.0, strength=1.0, saturation=1.0, toe=0.0, seed=42
+        )
+
+        # No saturation (monochrome grain)
+        result_desaturated = apply_film_grain(
+            image, scale=1.0, strength=1.0, saturation=0.0, toe=0.0, seed=42
+        )
+
+        # Calculate color variance
+        var_saturated = torch.var(result_saturated, dim=-1).mean()
+        var_desaturated = torch.var(result_desaturated, dim=-1).mean()
+
+        # Desaturated should have less color variance
+        assert var_desaturated < var_saturated
+
+    def test_apply_film_grain_toe_effect(self):
+        image = torch.ones(1, 32, 32, 3) * 0.5
+
+        # No toe
+        result_no_toe = apply_film_grain(
+            image, scale=1.0, strength=0.5, saturation=1.0, toe=0.0, seed=42
+        )
+
+        # With toe (lifts blacks)
+        result_with_toe = apply_film_grain(
+            image, scale=1.0, strength=0.5, saturation=1.0, toe=0.2, seed=42
+        )
+
+        # Toe should generally lift the overall brightness
+        assert result_with_toe.mean() > result_no_toe.mean()
+
+    def test_apply_film_grain_batch_processing(self):
+        batch_size = 4
+        image = torch.rand(batch_size, 32, 32, 3)
+
+        result = apply_film_grain(
+            image, scale=1.5, strength=0.5, saturation=0.8, toe=0.1, seed=42
+        )
+
+        assert result.shape == image.shape
+
+        # Each image in batch should be different (due to grain)
+        for i in range(batch_size - 1):
+            assert not torch.allclose(result[i], result[i + 1])
+
+    def test_apply_film_grain_preserves_alpha(self):
+        # Image with alpha channel
+        image = torch.rand(1, 32, 32, 4)
+        original_alpha = image[:, :, :, 3:4].clone()
+
+        result = apply_film_grain(
+            image, scale=1.0, strength=1.0, saturation=1.0, toe=0.0, seed=42
+        )
+
+        # Alpha channel should be unchanged
+        assert torch.allclose(original_alpha, result[:, :, :, 3:4])
+
+    def test_apply_film_grain_scale_interpolation(self):
+        image = torch.ones(1, 64, 64, 3) * 0.5
+
+        # Different scales should produce different sized grain
+        result_fine = apply_film_grain(
+            image, scale=0.5, strength=0.5, saturation=1.0, toe=0.0, seed=42
+        )
+
+        result_coarse = apply_film_grain(
+            image, scale=2.0, strength=0.5, saturation=1.0, toe=0.0, seed=42
+        )
+
+        # Compute local variance to measure grain size
+        def compute_local_variance(img, window=3):
+            unfold = torch.nn.Unfold(kernel_size=window, stride=1, padding=1)
+            img_reshaped = img.permute(0, 3, 1, 2)
+            patches = unfold(img_reshaped)
+            var = torch.var(patches, dim=1)
+            return var.mean()
+
+        var_fine = compute_local_variance(result_fine)
+        var_coarse = compute_local_variance(result_coarse)
+
+        # Fine grain should have higher local variance than coarse grain
+        # (more rapid changes)
+        assert var_fine != var_coarse  # They should be different
+
+
+class TestEdgeCases:
+    def test_handles_empty_batch(self):
+        image = torch.rand(0, 32, 32, 3)
+        result = apply_film_grain(
+            image, scale=1.0, strength=0.5, saturation=1.0, toe=0.0, seed=42
+        )
+        assert result.shape == image.shape
+
+    def test_handles_single_pixel(self):
+        image = torch.rand(1, 1, 1, 3)
+        result = apply_film_grain(
+            image, scale=1.0, strength=0.5, saturation=1.0, toe=0.0, seed=42
+        )
+        assert result.shape == image.shape
+        assert result.min() >= 0.0
+        assert result.max() <= 1.0
+
+    def test_handles_extreme_parameters(self):
+        image = torch.rand(1, 32, 32, 3)
+
+        # Maximum strength
+        result = apply_film_grain(
+            image, scale=2.0, strength=10.0, saturation=2.0, toe=0.5, seed=42
+        )
+        assert result.min() >= 0.0
+        assert result.max() <= 1.0
+
+        # Minimum values
+        result = apply_film_grain(
+            image, scale=0.25, strength=0.0, saturation=0.0, toe=-0.2, seed=42
+        )
+        assert result.min() >= 0.0
+        assert result.max() <= 1.0