diff --git a/__init__.py b/__init__.py
new file mode 100644
index 0000000..1923165
--- /dev/null
+++ b/__init__.py
@@ -0,0 +1,11 @@
+import pathlib
+import importlib.util
+import sys
+import traceback
+from pathlib import Path
+
+
+root = Path(__file__).resolve().parent
+sys.path.insert(0, str(root / "nodes"))
+
+from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
diff --git a/core/__init__.py b/core/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/core/latent_filters.py b/core/latent_filters.py
new file mode 100644
index 0000000..930cddd
--- /dev/null
+++ b/core/latent_filters.py
@@ -0,0 +1,167 @@
+import numpy as np
+from typing import Callable
+from PIL import Image
+import torchvision.transforms as transforms
+
+import torch
+import torch.nn.functional as F
+
+
+def center_tensor(
+    input_tensor: torch.Tensor,
+    per_channel_shift: float = 1,
+    full_tensor_shift: float = 1,
+    channels: list[int] | None = None,
+) -> torch.Tensor:
+    """
+    Recenter a tensor by subtracting the mean of each channel and the full tensor, with optional shift scaling.
+    Expect a tensor of shape (B, C, H, W) and return the same shape.
+    :param input_tensor:
+    :param per_channel_shift:
+    :param full_tensor_shift:
+    :param channels:
+    :return:
+    """
+    if channels is None:
+        channels = torch.range(input_tensor.shape[1])
+    for channel in channels:
+        input_tensor[0, channel] -= input_tensor[0, channel].mean() * per_channel_shift
+    return input_tensor - input_tensor.mean() * full_tensor_shift
+
+
+def huberize_quantile(
+    tensor: torch.Tensor, lowQ: float = 0.01, highQ: float = 0.99, slope: float = 0.1
+) -> torch.Tensor:
+    """
+    Linearly soft clamps extreme values in a tensor based on quantiles,
+    using a piecewise linear function:
+
+      1) Identity (slope=1) for x in [lower_val, upper_val].
+      2) Reduced slope for x < lower_val or x > upper_val.
+
+    The slope in the tails is controlled by 'slope' (0 < slope < 1),
+    ensuring we don't cut off values entirely but do compress them.
+
+    Args:
+        tensor (torch.Tensor): The input data.
+        lowQ (float): Lower quantile, e.g. 0.01 (1%).
+        highQ (float): Upper quantile, e.g. 0.99 (99%).
+        slope (float): Slope to use beyond the low/high quantiles.
+                       0 would be a hard clamp; 1 means no clamp.
+
+    Returns:
+        torch.Tensor: The transformed tensor, same shape as input.
+    """
+    # 1) Determine the lower and upper quantiles
+    lower_val = torch.quantile(tensor, lowQ)
+    upper_val = torch.quantile(tensor, highQ)
+
+    # 2) Define a central "identity" region around the midpoint
+    center = 0.5 * (lower_val + upper_val)
+    half_range = 0.5 * (upper_val - lower_val)
+
+    # 3) Shift the tensor so that 'center' is at 0
+    x_shifted = tensor - center
+
+    # 4) Piecewise function:
+    #    - If x < -half_range:   y = -half_range + slope*(x + half_range)
+    #    - If -half_range <= x <= half_range:  y = x
+    #    - If x >  half_range:   y =  half_range + slope*(x - half_range)
+
+    y = x_shifted.clone()  # same shape as x_shifted
+
+    mask_lower = x_shifted < -half_range
+    mask_upper = x_shifted > half_range
+
+    # Values below the lower bound
+    y[mask_lower] = -half_range + slope * (x_shifted[mask_lower] + half_range)
+
+    # Values above the upper bound
+    y[mask_upper] = half_range + slope * (x_shifted[mask_upper] - half_range)
+
+    # 5) Shift back
+    y += center
+
+    return y
+
+
+def moment_match(
+    from_tensor: torch.Tensor, to_tensor: torch.Tensor, per_channel: bool = True
+) -> torch.Tensor:
+    """
+    Match the mean and variance of a tensor to another tensor.
+    :param from_tensor: The tensor to match.
+    :param to_tensor: The tensor to match to.
+    :return: The transformed tensor.
+    """
+    if not per_channel:
+        from_mean = from_tensor.mean()
+        from_std = from_tensor.std()
+        to_mean = to_tensor.mean()
+        to_std = to_tensor.std()
+        return (to_tensor + to_mean - from_mean) * to_std / from_std
+    else:
+        ret = from_tensor.clone()
+        ret += from_tensor.mean(dim=(2, 3), keepdim=True) - ret.mean(
+            dim=(2, 3), keepdim=True
+        )
+        ret *= from_tensor.std(dim=(2, 3), keepdim=True) / ret.std(
+            dim=(2, 3), keepdim=True
+        )
+        return ret
+
+
+def add_correlated_gaussian_noise(
+    latent: torch.Tensor,
+    sigma: float = 1.0,
+    amplitude: float = 1.0,
+    max_kernel_size: int = 13,
+) -> torch.Tensor:
+    """
+    Add correlated Gaussian noise to a latent tensor by:
+      1) Generating white noise (normal(0,1)).
+      2) Convolving that noise with a 2D Gaussian kernel.
+      3) Scaling and adding it to the original latent.
+
+    Args:
+        latent (torch.Tensor): Input tensor of shape (B, C, H, W).
+        sigma (float): Standard deviation for the Gaussian kernel filter (controls correlation).
+        amplitude (float): Scale factor for the filtered noise before adding.
+        max_kernel_size:  Maximum size of the Gaussian kernel (odd number).
+
+    Returns:
+        torch.Tensor: Tensor of the same shape, with correlated noise added.
+    """
+    assert max_kernel_size % 2 == 1, "max_kernel_size must be odd"
+
+    # 1) Generate white noise
+    noise = torch.randn_like(latent)  # same shape, ~N(0,1)
+
+    # base size based on sigma
+    kernel_size = min(2 * np.ceil(3 * sigma) + 1, max_kernel_size)
+
+    # 2) Build a 2D Gaussian kernel
+    #    We'll define an inline helper to create a normalized 2D kernel.
+    def gaussian_kernel_2d(k_size, sigma_val):
+        # Create coordinate grid centered at 0
+        coords = torch.arange(k_size) - (k_size - 1) / 2
+        xx, yy = torch.meshgrid(coords, coords, indexing="xy")
+        kernel_2d = torch.exp(-(xx**2 + yy**2) / (2.0 * sigma_val**2))
+        kernel_2d = kernel_2d / kernel_2d.sum()  # normalize to sum=1
+        return kernel_2d
+
+    kernel_2d = gaussian_kernel_2d(kernel_size, sigma)  # shape: (k_size, k_size)
+    kernel_2d = kernel_2d.unsqueeze(0).unsqueeze(0)  # shape: (1, 1, k_size, k_size)
+
+    # 3) Convolve noise with Gaussian kernel for each channel
+    #    - We'll "repeat" the kernel for each channel and use grouped convolution
+    B, C, H, W = latent.shape
+    kernel_2d = kernel_2d.repeat(C, 1, 1, 1)  # shape: (C, 1, k_size, k_size)
+
+    #    - Pad so output is the same spatial size
+    pad = (kernel_size - 1) // 2
+    correlated_noise = F.conv2d(noise, kernel_2d, padding=pad, groups=C)
+
+    # 4) Scale and add to the original latent
+    correlated_noise = amplitude * correlated_noise
+    return latent + correlated_noise
diff --git a/nodes/__init__.py b/nodes/__init__.py
new file mode 100644
index 0000000..01bd5ef
--- /dev/null
+++ b/nodes/__init__.py
@@ -0,0 +1,4 @@
+from . import lnl
+
+NODE_CLASS_MAPPINGS = lnl.NODE_CLASS_MAPPINGS  # | ...
+NODE_DISPLAY_NAME_MAPPINGS = lnl.NODE_DISPLAY_NAME_MAPPINGS  # | ...
diff --git a/nodes/lnl.py b/nodes/lnl.py
new file mode 100644
index 0000000..af64ec8
--- /dev/null
+++ b/nodes/lnl.py
@@ -0,0 +1,114 @@
+import comfy
+from ..core import latent_filters as lf
+
+# import torch
+
+
+class LatentNormalizedLanczosResize:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "latent": ["LATENT"],
+                "vae": ["VAE"],
+                "size_multiplier": (
+                    "FLOAT",
+                    {"default": 2.0, "min": 0.1, "max": 4.0, "step": 0.5},
+                ),
+                "soft_clamp_outliers": (["enable", "disable"],),
+                "outlier_quantile": (
+                    "FLOAT",
+                    {"default": 0.01, "min": 0.0, "max": 1.0, "step": 0.01},
+                ),
+                "outlier_clamp_slope": (
+                    "FLOAT",
+                    {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.1},
+                ),
+                "add_latent_noise": (["enable", "disable"],),
+                "latent_noise_std": (
+                    "FLOAT",
+                    {"default": 0.1, "min": 0.0, "max": 10.0, "step": 0.01},
+                ),
+                "latent_noise_scale": (
+                    "FLOAT",
+                    {"default": 1.0, "min": 0.0, "max": 10.0},
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "transform"
+    CATEGORY = "image/upscaling"
+    OUTPUT_NODE = False
+
+    @classmethod
+    def help(cls):
+        return (
+            "Upscale an image or latent using a normalized Lanczos resizer that tries to match the distribution "
+            "of the input latent, optionally adding noise."
+        )
+
+    def upscale(
+        self, image: "torch.Tensor", upscale_method: str, scale_by
+    ) -> "torch.Tensor":
+        samples = image.movedim(-1, 1)
+        width = round(samples.shape[3] * scale_by)
+        height = round(samples.shape[2] * scale_by)
+        s = comfy.utils.common_upscale(
+            samples, width, height, upscale_method, "disabled"
+        )
+        s = s.movedim(1, -1)
+        return (s,)
+
+    def transform(
+        self,
+        latent,
+        vae,
+        size_multiplier: float = 2.0,
+        soft_clamp_outliers: str = "disable",
+        outlier_quantile: float = 0.01,
+        outlier_clamp_slope: float = 0.1,
+        add_latent_noise: str = "disable",
+        latent_noise_std: float = 0.1,
+        latent_noise_scale: float = 1.0,
+    ):
+        samples = latent["samples"]
+
+        soft_clamp_outliers = soft_clamp_outliers == "enable"
+        add_latent_noise = add_latent_noise == "enable"
+
+        if soft_clamp_outliers:
+            samples = lf.huberize_quantile(
+                samples, outlier_quantile, 1 - outlier_quantile, outlier_clamp_slope
+            )
+
+        image = vae.decode(samples)
+
+        width = round(samples.shape[3] * size_multiplier)
+        height = round(samples.shape[2] * size_multiplier)
+        image_upscaled = comfy.utils.common_upscale(
+            image.movedim(-1, 1),
+            width,
+            height,
+            "lanczos",
+            "disabled",
+        ).movedim(1, -1)
+
+        latent_of_upscaled = vae.encode(image_upscaled)
+        matched_latent = lf.moment_match(samples, latent_of_upscaled)
+
+        if add_latent_noise:
+            matched_latent = lf.add_correlated_gaussian_noise(
+                matched_latent, latent_noise_scale, latent_noise_std
+            )
+
+        return (matched_latent,)
+
+
+NODE_CLASS_MAPPINGS = {
+    "LatentNormalizedLanczosResize": LatentNormalizedLanczosResize,
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LatentNormalizedLanczosResize": "Latent Normalized Lanczos Resize(LNLR)"
+}
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..b0a4058
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,14 @@
+[project]
+name = "ComfyLatentTools"
+description = "A set of tools for manipulating latent tensors in ComfyUI"
+version = "0.1.0"
+license = { text = "Apache-2.0" }
+dependencies = ["torch", "torchvision", "pillow", "numpy"]
+
+[project.urls]
+Repository = "https://github.com/JTriggerFish/ComfyLatentTools"
+
+[tool.comfy]
+PublisherId = "JTriggerFish"
+DisplayName = "Comfy Latent Tools"
+Icon = ""
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..92fb4ee
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,4 @@
+torch
+torchvision
+pillow
+numpy