diff --git a/README.MD b/README.MD
index 93ed538..ccae004 100644
--- a/README.MD
+++ b/README.MD
@@ -70,6 +70,7 @@ When this error has occurred, please check the network environment.
## Update
**If the dependency package error after updating, please reinstall the relevant dependency packages. for details, please refer to [here](https://github.com/chflame163/ComfyUI_LayerStyle/issues/5).
+* Commit [HDR Effect](#HDR) node，it enhances the dynamic range and visual appeal of input images. this node is repackage of [HDR Effects (SuperBeasts.AI)](https://github.com/SuperBeastsAI/ComfyUI-SuperBeasts).
* Commit [CropBoxResolve](#CropBoxResolve) node.
* Commit [BiRefNetUltra](#BiRefNetUltra) node, it using the BiRefNet model to remove background has better recognition ability and ultra-high edge details.
* Commit [ImageAutoCropV2](#ImageAutoCropV2) node, it can choose not to remove the background, support mask input, and scale by long or short side size.
@@ -1246,6 +1247,22 @@ Node options:
* angle: Angle of channel separation.
* mode: Channel shift arrangement order.
+
+### HDR Effects
+enhances the dynamic range and visual appeal of input images.
+This node is reorganize and encapsulate of [HDR Effects (SuperBeasts.AI)](https://github.com/SuperBeastsAI/ComfyUI-SuperBeasts), thanks to the original author.
+
+
+Node options:
+
+* hdr_intensity: Range: 0.0 to 5.0, Controls the overall intensity of the HDR effect, Higher values result in a more pronounced HDR effect.
+* shadow_intensity: Range: 0.0 to 1.0，Adjusts the intensity of shadows in the image，Higher values darken the shadows and increase contrast.
+* highlight_intensity: Range: 0.0 to 1.0，Adjusts the intensity of highlights in the image，Higher values brighten the highlights and increase contrast.
+* gamma_intensity: Range: 0.0 to 1.0，Controls the gamma correction applied to the image，Higher values increase the overall brightness and contrast.
+* contrast: Range: 0.0 to 1.0，Enhances the contrast of the image, Higher values result in more pronounced contrast.
+* enhance_color: Range: 0.0 to 1.0，Enhances the color saturation of the image, Higher values result in more vibrant colors.
+
+
### Film
Simulate the grain, dark edge, and blurred edge of the film, support input depth map to simulate defocus.
This node is reorganize and encapsulate of [digitaljohn/comfyui-propost](https://github.com/digitaljohn/comfyui-propost), thanks to the original author.
diff --git a/README_CN.MD b/README_CN.MD
index 85cf2a8..f5254cc 100644
--- a/README_CN.MD
+++ b/README_CN.MD
@@ -70,7 +70,7 @@ git clone https://github.com/chflame163/ComfyUI_LayerStyle.git
## 更新说明
**如果本插件更新后出现依赖包错误，请重新安装相关依赖包。
-
+* 添加 [HDR Effect](#HDR) 节点，增强图片动态范围。这个节点是[HDR Effects (SuperBeasts.AI)](https://github.com/SuperBeastsAI/ComfyUI-SuperBeasts)的重新封装。感谢原作者。
* 添加 [CropBoxResolve](#CropBoxResolve) 节点。
* 添加 [BiRefNetUltra](#BiRefNetUltra) 节点, 使用BiRefNet模型去除背景，有更好的识别能力，同时具有超高的边缘细节。
* 添加 [ImageAutoCropV2](#ImageAutoCropV2) 节点，可选择不去除背景，支持mask输入，支持按长边或短边尺寸缩放。
@@ -1245,6 +1245,22 @@ mask反转
* angle: 通道分离的角度。
* mode: 通道错位排列顺序。
+*
+### HDR Effects
+增强图像的动态范围。
+这个节点是[HDR Effects (SuperBeasts.AI)](https://github.com/SuperBeastsAI/ComfyUI-SuperBeasts)的重新封装。感谢原作者。
+
+
+节点选项说明:
+
+* hdr_intensity: 范围0-5, 控制HDR效果的整体强度, 数值越高，效果越明显。
+* shadow_intensity: 范围0-1，调整图像阴影部分的强度，较高的值会使阴影变暗并增加对比度。
+* highlight_intensity: 范围0-1，调整图像高光部分的强度，较高的值可使高光变亮并增加对比度。
+* gamma_intensity: 范围0-1，用于图像的伽玛校正，值越高，整体亮度和对比度越高。
+* contrast: 范围0-1，增强图像的对比度, 值越高，对比度越明显。
+* enhance_color: 范围0-1，增强图像的色彩饱和度, 值越高，颜色越鲜艳。
+
+
### Film
模拟胶片的颗粒、暗边和边缘模糊，支持输入深度图模拟虚焦。
这个节点是[digitaljohn/comfyui-propost](https://github.com/digitaljohn/comfyui-propost)的重新封装，感谢原作者。
diff --git a/image/hdr_effects_example.png b/image/hdr_effects_example.png
new file mode 100644
index 0000000..c205f52
Binary files /dev/null and b/image/hdr_effects_example.png differ
diff --git a/image/hdr_effects_node.png b/image/hdr_effects_node.png
new file mode 100644
index 0000000..fa4e7dc
Binary files /dev/null and b/image/hdr_effects_node.png differ
diff --git a/py/hdr_effects.py b/py/hdr_effects.py
new file mode 100644
index 0000000..553d8d1
--- /dev/null
+++ b/py/hdr_effects.py
@@ -0,0 +1,162 @@
+from .imagefunc import *
+from PIL import ImageCms
+from PIL.PngImagePlugin import PngInfo
+
+NODE_NAME = 'HDR Effects'
+
+sRGB_profile = ImageCms.createProfile("sRGB")
+Lab_profile = ImageCms.createProfile("LAB")
+
+def adjust_shadows(luminance_array, shadow_intensity, hdr_intensity):
+ # Darken shadows more as shadow_intensity increases, scaled by hdr_intensity
+ return np.clip(luminance_array - luminance_array * shadow_intensity * hdr_intensity * 0.5, 0, 255)
+
+
+def adjust_highlights(luminance_array, highlight_intensity, hdr_intensity):
+ # Brighten highlights more as highlight_intensity increases, scaled by hdr_intensity
+ return np.clip(luminance_array + (255 - luminance_array) * highlight_intensity * hdr_intensity * 0.5, 0, 255)
+
+
+def apply_adjustment(base, factor, intensity_scale):
+ """Apply positive adjustment scaled by intensity."""
+ # Ensure the adjustment increases values within [0, 1] range, scaling by intensity
+ adjustment = base + (base * factor * intensity_scale)
+ # Ensure adjustment stays within bounds
+ return np.clip(adjustment, 0, 1)
+
+
+def multiply_blend(base, blend):
+ """Multiply blend mode."""
+ return np.clip(base * blend, 0, 255)
+
+
+def overlay_blend(base, blend):
+ """Overlay blend mode."""
+ # Normalize base and blend to [0, 1] for blending calculation
+ base = base / 255.0
+ blend = blend / 255.0
+ return np.where(base < 0.5, 2 * base * blend, 1 - 2 * (1 - base) * (1 - blend)) * 255
+
+
+def adjust_shadows_non_linear(luminance, shadow_intensity, max_shadow_adjustment=1.5):
+ lum_array = np.array(luminance, dtype=np.float32) / 255.0 # Normalize
+ # Apply a non-linear darkening effect based on shadow_intensity
+ shadows = lum_array ** (1 / (1 + shadow_intensity * max_shadow_adjustment))
+ return np.clip(shadows * 255, 0, 255).astype(np.uint8) # Re-scale to [0, 255]
+
+
+def adjust_highlights_non_linear(luminance, highlight_intensity, max_highlight_adjustment=1.5):
+ lum_array = np.array(luminance, dtype=np.float32) / 255.0 # Normalize
+ # Brighten highlights more aggressively based on highlight_intensity
+ highlights = 1 - (1 - lum_array) ** (1 + highlight_intensity * max_highlight_adjustment)
+ return np.clip(highlights * 255, 0, 255).astype(np.uint8) # Re-scale to [0, 255]
+
+
+def merge_adjustments_with_blend_modes(luminance, shadows, highlights, hdr_intensity, shadow_intensity,
+ highlight_intensity):
+ # Ensure the data is in the correct format for processing
+ base = np.array(luminance, dtype=np.float32)
+
+ # Scale the adjustments based on hdr_intensity
+ scaled_shadow_intensity = shadow_intensity ** 2 * hdr_intensity
+ scaled_highlight_intensity = highlight_intensity ** 2 * hdr_intensity
+
+ # Create luminance-based masks for shadows and highlights
+ shadow_mask = np.clip((1 - (base / 255)) ** 2, 0, 1)
+ highlight_mask = np.clip((base / 255) ** 2, 0, 1)
+
+ # Apply the adjustments using the masks
+ adjusted_shadows = np.clip(base * (1 - shadow_mask * scaled_shadow_intensity), 0, 255)
+ adjusted_highlights = np.clip(base + (255 - base) * highlight_mask * scaled_highlight_intensity, 0, 255)
+
+ # Combine the adjusted shadows and highlights
+ adjusted_luminance = np.clip(adjusted_shadows + adjusted_highlights - base, 0, 255)
+
+ # Blend the adjusted luminance with the original luminance based on hdr_intensity
+ final_luminance = np.clip(base * (1 - hdr_intensity) + adjusted_luminance * hdr_intensity, 0, 255).astype(np.uint8)
+
+ return Image.fromarray(final_luminance)
+
+
+def apply_gamma_correction(lum_array, intensity, base_gamma):
+ """
+ Apply gamma correction to the luminance array.
+ :param lum_array: Luminance channel as a NumPy array.
+ :param intensity: HDR intensity factor.
+ :param base_gamma: Base gamma value for correction.
+ """
+ if intensity == 0: # If intensity is 0, return the array as is.
+ return lum_array
+
+ gamma = 1 + (base_gamma - 1) * intensity # Scale gamma based on intensity.
+ adjusted = 255 * (lum_array / 255) ** gamma
+ return np.clip(adjusted, 0, 255).astype(np.uint8)
+
+
+class LS_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': 0.25, '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 = ('image',)
+ FUNCTION = 'hdr_effects'
+ CATEGORY = '😺dzNodes/LayerFilter'
+
+ @apply_to_batch
+ def hdr_effects(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), hdr_intensity, gamma_intensity)
+
+ # Merge L channel back with original A and B channels
+ adjusted_lab = Image.merge('LAB', (merged_adjustments, 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)
+
+
+NODE_CLASS_MAPPINGS = {
+ "LayerFilter: HDREffects": LS_HDREffects
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "LayerFilter: HDREffects": "LayerFilter: HDR Effects"
+}
\ No newline at end of file
diff --git a/py/imagefunc.py b/py/imagefunc.py
index e970e04..0f10aef 100644
--- a/py/imagefunc.py
+++ b/py/imagefunc.py
@@ -53,6 +53,20 @@ except ImportError as e:
f"\nFor detail refer to \033[4mhttps://github.com/chflame163/ComfyUI_LayerStyle/issues/5\033[0m")
+
+'''warpper'''
+
+# create a wrapper function that can apply a function to multiple images in a batch while passing all other arguments to the function
+def apply_to_batch(func):
+ def wrapper(self, image, *args, **kwargs):
+ images = []
+ for img in image:
+ images.append(func(self, img, *args, **kwargs))
+ batch_tensor = torch.cat(images, dim=0)
+ return (batch_tensor,)
+ return wrapper
+
+
'''pickle'''