HDR Effects (SuperBeasts.AI)

Documentation

• Class name: HDR Effects (SuperBeasts.AI)
• Category: SuperBeastsAI/Image
• Output node: False

The HDR Effects node enhances images by applying High Dynamic Range (HDR) effects, improving the visual quality through adjustments in contrast, color saturation, and brightness. It utilizes advanced image processing techniques to simulate HDR photography, making the images appear more vivid and lifelike.

Input types

Required

• image
  • The input image to be enhanced with HDR effects. It serves as the base for all subsequent HDR-related adjustments, including contrast, color saturation, and brightness enhancements.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• hdr_intensity
  • Controls the overall intensity of the HDR effect applied to the image, affecting the depth and strength of shadows, highlights, and midtones adjustments.
  • Comfy dtype: FLOAT
  • Python dtype: float
• shadow_intensity
  • Determines the intensity of shadows in the image, enhancing the depth and detail in darker areas.
  • Comfy dtype: FLOAT
  • Python dtype: float
• highlight_intensity
  • Adjusts the brightness of highlights, enhancing the detail and visibility in brighter areas of the image.
  • Comfy dtype: FLOAT
  • Python dtype: float
• gamma_intensity
  • Modifies the gamma levels of the image, affecting the midtone luminance and overall visual contrast.
  • Comfy dtype: FLOAT
  • Python dtype: float
• contrast
  • Adjusts the contrast level of the image, making the darks darker and the lights lighter to enhance the overall dynamic range.
  • Comfy dtype: FLOAT
  • Python dtype: float
• enhance_color
  • Increases the color saturation of the image, making the colors more vivid and enhancing the overall visual appeal.
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

• result_img
  • Comfy dtype: IMAGE
  • The enhanced image with applied HDR effects, including improved contrast, color saturation, and brightness.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class HDREffects:
    @classmethod
    def INPUT_TYPES(cls):
        return {'required': {'image': ('IMAGE', {'default': None}),
                             'hdr_intensity': ('FLOAT', {'default': 0.5, 'min': 0.0, 'max': 5.0, 'step': 0.01}),
                             'shadow_intensity': ('FLOAT', {'default': 0.25, 'min': 0.0, 'max': 1.0, 'step': 0.01}),
                             'highlight_intensity': ('FLOAT', {'default': 0.75, 'min': 0.0, 'max': 1.0, 'step': 0.01}),
                             'gamma_intensity': ('FLOAT', {'default': 0.0, 'min': 0.0, 'max': 1.0, 'step': 0.01}),
                             'contrast': ('FLOAT', {'default': 0.1, 'min': 0.0, 'max': 1.0, 'step': 0.01}),
                             'enhance_color': ('FLOAT', {'default': 0.25, 'min': 0.0, 'max': 1.0, 'step': 0.01})
                             }}

    RETURN_TYPES = ('IMAGE',)
    RETURN_NAMES = ('result_img',)
    FUNCTION = 'apply_hdr2'
    CATEGORY = 'SuperBeastsAI/Image'

    @apply_to_batch
    def apply_hdr2(self, image, hdr_intensity=0.5, shadow_intensity=0.25, highlight_intensity=0.75, gamma_intensity=0.25, contrast=0.1, enhance_color=0.25):
        # Load the image
        img = tensor2pil(image)

        # Step 1: Convert RGB to LAB for better color preservation
        img_lab = ImageCms.profileToProfile(img, sRGB_profile, Lab_profile, outputMode='LAB')

        # Extract L, A, and B channels
        luminance, a, b = img_lab.split()

        # Convert luminance to a NumPy array for processing
        lum_array = np.array(luminance, dtype=np.float32)

        # Preparing adjustment layers (shadows, midtones, highlights)
        # This example assumes you have methods to extract or calculate these adjustments
        shadows_adjusted = adjust_shadows_non_linear(luminance, shadow_intensity)
        highlights_adjusted = adjust_highlights_non_linear(luminance, highlight_intensity)


        merged_adjustments = merge_adjustments_with_blend_modes(lum_array, shadows_adjusted, highlights_adjusted, hdr_intensity, shadow_intensity, highlight_intensity)

        # Apply gamma correction with a base_gamma value (define based on desired effect)
        gamma_corrected = apply_gamma_correction(np.array(merged_adjustments), gamma_intensity)
        gamma_corrected = Image.fromarray(gamma_corrected).resize(a.size)


        # Merge L channel back with original A and B channels
        adjusted_lab = Image.merge('LAB', (gamma_corrected, a, b))

        # Step 3: Convert LAB back to RGB
        img_adjusted = ImageCms.profileToProfile(adjusted_lab, Lab_profile, sRGB_profile, outputMode='RGB')


        # Enhance contrast
        enhancer = ImageEnhance.Contrast(img_adjusted)
        contrast_adjusted = enhancer.enhance(1 + contrast)


        # Enhance color saturation
        enhancer = ImageEnhance.Color(contrast_adjusted)
        color_adjusted = enhancer.enhance(1 + enhance_color * 0.2)

        return pil2tensor(color_adjusted)