UnTonemap

Documentation

• Class name: UnTonemap
• Category: image/filters
• Output node: False

The UnTonemap node is designed to reverse the effects of tonemapping on images, converting them back from a tonemapped representation to their original linear or sRGB color space. This process is essential for restoring the original dynamic range and color fidelity of images that have undergone tonemapping.

Input types

Required

• images
  • The images to be untonemapped. This input is crucial for defining the set of images that will undergo the reverse tonemapping process.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• input_mode
  • Specifies the color space of the input images, either 'sRGB' or 'linear', which determines the initial conversion step before untonemapping.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• output_mode
  • Defines the desired color space of the output images, either 'linear' or 'sRGB', affecting the final conversion step after untonemapping.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• tonemap_scale
  • A scaling factor applied during the untonemapping process to adjust the intensity of the effect.
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

• image
  • Comfy dtype: IMAGE
  • The untonemapped images, returned in the specified output color space, either 'linear' or 'sRGB'.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class UnTonemap:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "images": ("IMAGE",),
                "input_mode": (["sRGB", "linear"],),
                "output_mode": (["linear", "sRGB"],),
                "tonemap_scale": ("FLOAT", {"default": 1, "min": 0.1, "max": 10, "step": 0.01}),
            },
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "apply"

    CATEGORY = "image/filters"

    def apply(self, images, input_mode, output_mode, tonemap_scale):
        t = images.detach().clone().cpu().numpy().astype(np.float32)

        if input_mode == "sRGB":
            sRGBtoLinear(t[:,:,:,:3])

        tonemapToLinear(t[:,:,:,:3], tonemap_scale)

        if output_mode == "sRGB":
            linearToSRGB(t[:,:,:,:3])
            t = np.clip(t, 0, 1)

        t = torch.from_numpy(t)
        return (t,)