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feat: add KikoFilmGrain node for realistic film grain effects

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- Implement film grain effect with customizable parameters (scale, strength, saturation, toe, seed)
- Use pure PyTorch operations for better GPU utilization (no OpenCV dependencies)
- Apply ITU-R BT.709 color space conversion for accurate grain distribution
- Implement screen blend mode for better highlight preservation
- Add channel-specific weighting matching real film characteristics (3x blue, 2x red)
- Preserve alpha channel when present
- Add comprehensive test suite (20 tests covering all functionality)
- Include documentation and example workflow
- Register node under ComfyAssets/image category

Improvements over reference implementation:
- More efficient memory management avoiding numpy/OpenCV conversions
- Better grain mixing algorithm with proper color science
- Improved performance through PyTorch-native operations
This commit is contained in:
Vito Sansevero
2025-08-07 18:42:37 -07:00
parent df60457929
commit 0f79601065
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examples/documentation/kiko_film_grain.md
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# 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
examples/workflows/kiko_film_grain_example.json
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{
"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
}
kikotools/__init__.py
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@@ -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",
kikotools/tools/kiko_film_grain/__init__.py
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from .node import KikoFilmGrainNode
__all__ = ["KikoFilmGrainNode"]
kikotools/tools/kiko_film_grain/logic.py
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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)
kikotools/tools/kiko_film_grain/node.py
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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,)
tests/unit/tools/test_kiko_film_grain.py
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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