[Added] Arbitrary normalization and a node to input the parameters

2025-10-14 11:40:16 -03:00
parent c5060bafe6
commit 1d1b99ac49
3 changed files with 92 additions and 5 deletions
@@ -35,6 +35,8 @@ Currently we just have a few nodes used by other nodes I maintain.
    - [Normalize Image to ImageNet](docs/nodes_img.md#4-normalize-image-to-imagenet)
    - [Normalize Image to [-0.5, 0.5]](docs/nodes_img.md#5-normalize-image-to-05-05)
    - [Normalize Image to [-1, 1]](docs/nodes_img.md#6-normalize-image-to-1-1)
+    - [Arbitrary Normalize](docs/nodes_img.md#14-arbitrary-normalize)
+    - [Normalize Parameters](docs/nodes_img.md#15-normalize-parameters)
 - &#x0001F4DD; [Usage Notes](#-usage-notes)
 - &#x0001F4DC; [Project History](#-project-history)
 - &#x2696;&#xFE0F; [License](#&#xFE0F;-license)
@@ -37,7 +37,7 @@
     - The animated frame is automatically resized to fit the bounding box dimensions before pasting. The pasting logic safely handles cases where the bbox is partially outside the image boundaries.

 # 3. Face Composite (frame by frame)
-   - **Display Name:** `"Face Composite (frame by frame)`
+   - **Display Name:** `Face Composite (frame by frame)`
   - **Internal Name:** `SET_CompositeFaceFrameByFrame`
   - **Category:** `image/manipulation`
   - **Description:** This node is a simplified variant for **1-to-1** compositing. It is perfect for video processing workflows where you need to paste a sequence of processed frames back into the original video sequence at a static location.
@@ -55,7 +55,7 @@


 # 4. Normalize Image to ImageNet
-   - **Display Name:** `"Normalize Image to ImageNet`
+   - **Display Name:** `Normalize Image to ImageNet`
   - **Internal Name:** `SET_NormalizeToImageNetDataset`
   - **Category:** `image/normalization`
   - **Description:** Normalizes an image tensor using the mean and standard deviation of the ImageNet dataset.
@@ -255,3 +255,34 @@
  - `mask` (`MASK`): The resized mask tensor.
  - `width` (`INT`): The final width of the output mask.
  - `height` (`INT`): The final height of the output mask.
+
+
+# 14. Arbitrary Normalize
+   - **Display Name:** `Arbitrary Normalize`
+   - **Internal Name:** `SET_NormalizeArbitrary`
+   - **Category:** `image/normalization`
+   - **Description:** Normalizes an image tensor using the mean and standard deviation provided in the `parameters` input.
+   - **Purpose:** Essential for pre-processing images before feeding them into models
+   - **Inputs:**
+     - `image` (`IMAGE`): A standard ComfyUI image tensor in the `[0, 1]` range.
+     - `parameters` (`NORM_PARAMS`): The parameters for the normalization. This is a dict with two keys, `mean` and `std`, which are lists with the R, G, B parameters.
+   - **Output:**
+     - `image` (`IMAGE`): The normalized image tensor. The value range will be altered significantly.
+   - **How it Works:**  For each channel, it performs the operation `output = (input - mean) / std`, using the values from NORM_PARAMS
+
+
+# 15. Normalize Parameters
+   - **Display Name:** `Normalize Parameters`
+   - **Internal Name:** `SET_NormalizeParameters`
+   - **Category:** `image/normalization`
+   - **Description:** Provides the `NORM_PARAMS` for the [Arbitrary Normalize](#14-arbitrary-normalize) node
+   - **Purpose:** This is a user interface to the [Arbitrary Normalize](#14-arbitrary-normalize) node
+   - **Inputs:**
+     - `mean_red` (`FLOAT`): Mean value for the red channel
+     - `mean_green` (`FLOAT`): Mean value for the green channel
+     - `mean_blue` (`FLOAT`): Mean value for the blue channel
+     - `std_red` (`FLOAT`): Standard deviation for the red channel
+     - `std_green` (`FLOAT`): Standard deviation for the green channel
+     - `std_blue` (`FLOAT`): Standard deviation for the blue channel
+   - **Output:**
+     - `parameters` (`NORM_PARAMS`): The parameters for the normalization. This is a dict with two keys, `mean` and `std`, which are lists with the R, G, B parameters.
@@ -89,6 +89,12 @@ PAD_TRANS = ("FLOAT", {
                "display": "number",
                "tooltip": ("The transparency for the padded area for all modes except `edge_pixel`."
                            "1.0 is fully transparent, 0.0 is fully opaque.")})
+NORM_PARAM = ("FLOAT", {
+                "default": 1.0,
+                "min": 0.0,
+                "max": 1.0,
+                "step": 0.1,
+                "display": "number"})


 def tensor_to_pil(tensor: torch.Tensor) -> Image.Image:
@@ -418,7 +424,6 @@ class NormalizeToImageNetDataset():
    DESCRIPTION = ("Normalize the image to the ImageNet dataset")
    UNIQUE_NAME = "SET_NormalizeToImageNetDataset"
    DISPLAY_NAME = "Normalize Image to ImageNet"
-    imagenet_normalize = None

    def normalize(self, image: torch.Tensor):
        return (TF.normalize(image.permute(0, 3, 1, 2),  # BHWC -> BCHW
@@ -440,7 +445,6 @@ class NormalizeToRangeMinus05to05():
    DESCRIPTION = ("Normalize the image to [-0.5, 0.5]")
    UNIQUE_NAME = "SET_NormalizeToRangeMinus05to05"
    DISPLAY_NAME = "Normalize Image to [-0.5, 0.5]"
-    imagenet_normalize = None

    def normalize(self, image: torch.Tensor):
        return (TF.normalize(image.permute(0, 3, 1, 2),  # BHWC -> BCHW
@@ -462,7 +466,6 @@ class NormalizeToRangeMinus1to1():
    DESCRIPTION = ("Normalize the image to [-1, 1]")
    UNIQUE_NAME = "SET_NormalizeToRangeMinus1to1"
    DISPLAY_NAME = "Normalize Image to [-1, 1] (i.e. GAN)"
-    imagenet_normalize = None

    def normalize(self, image: torch.Tensor):
        return (TF.normalize(image.permute(0, 3, 1, 2),  # BHWC -> BCHW
@@ -470,6 +473,57 @@ class NormalizeToRangeMinus1to1():
                             std=[0.5, 0.5, 0.5]).permute(0, 2, 3, 1),)  # BCHW -> BHWC


+class NormalizeArbitrary():
+    """
+    A ComfyUI node to normalize the values to arbitrary mean/std
+    """
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "parameters": ("NORM_PARAMS",),
+            },
+        }
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "normalize"
+    CATEGORY = BASE_CATEGORY + "/" + NORMALIZATION
+    DESCRIPTION = ("Normalize the image to the provided parameters")
+    UNIQUE_NAME = "SET_NormalizeArbitrary"
+    DISPLAY_NAME = "Arbitrary Normalize"
+
+    def normalize(self, image: torch.Tensor, parameters):
+        return (TF.normalize(image.movedim(-1, 1),  # BHWC -> BCHW
+                             mean=parameters["mean"],
+                             std=parameters["std"]).movedim(1, -1),)  # BCHW -> BHWC
+
+
+class NormalizeParameters():
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "mean_red": NORM_PARAM,
+                "mean_green": NORM_PARAM,
+                "mean_blue": NORM_PARAM,
+                "std_red": NORM_PARAM,
+                "std_green": NORM_PARAM,
+                "std_blue": NORM_PARAM,
+            },
+        }
+    RETURN_TYPES = ("NORM_PARAMS",)
+    RETURN_NAMES = ("parameters",)
+    FUNCTION = "normalize"
+    CATEGORY = BASE_CATEGORY + "/" + NORMALIZATION
+    DESCRIPTION = ("Parameters for the arbitrary normalization")
+    UNIQUE_NAME = "SET_NormalizeParameters"
+    DISPLAY_NAME = "Normalize Parameters"
+
+    def normalize(self, mean_red, mean_green, mean_blue, std_red, std_green, std_blue):
+        return ({"mean": [mean_red, mean_green, mean_blue], "std": [std_red, std_green, std_blue]},)
+
+
 class ApplyMaskAFFCE:
    @classmethod
    def INPUT_TYPES(cls):