LayerColor: ColorTemperature

Documentation

• Class name: LayerColor: ColorTemperature
• Category: 😺dzNodes/LayerColor
• Output node: False

This node adjusts the color temperature of an image, simulating warmer or cooler lighting conditions. It modifies the image's RGB values based on the specified temperature, enhancing the visual warmth or coolness.

Input types

Required

• image
  • The input image to be processed. Adjusting its color temperature simulates different lighting conditions.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• temperature
  • The temperature value to adjust the image's color temperature. Positive values simulate warmer lighting, while negative values simulate cooler lighting.
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

• image
  • Comfy dtype: IMAGE
  • The image with adjusted color temperature, reflecting the simulated lighting condition.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class ColorTemperature:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": ("IMAGE",),
                "temperature": ("FLOAT", {"default": 0, "min": -100, "max": 100, "step": 1},),
            },
        }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("image",)
    FUNCTION = "color_temperature"
    CATEGORY = '😺dzNodes/LayerColor'

    def color_temperature(self, image, temperature,
                            ):

        batch_size, height, width, _ = image.shape
        result = torch.zeros_like(image)

        temperature /= -100

        for b in range(batch_size):
            tensor_image = image[b].numpy()
            modified_image = Image.fromarray((tensor_image * 255).astype(np.uint8))
            modified_image = np.array(modified_image).astype(np.float32)

            if temperature > 0:
                modified_image[:, :, 0] *= 1 + temperature
                modified_image[:, :, 1] *= 1 + temperature * 0.4
            elif temperature < 0:
                modified_image[:, :, 0] *= 1 + temperature * 0.2
                modified_image[:, :, 2] *= 1 - temperature

            modified_image = np.clip(modified_image, 0, 255)
            modified_image = modified_image.astype(np.uint8)
            modified_image = modified_image / 255
            modified_image = torch.from_numpy(modified_image).unsqueeze(0)
            result[b] = modified_image

        log(f"{NODE_NAME} Processed {len(result)} image(s).", message_type='finish')
        return (result,)