sd-webui-color-enhance/mmaker_color_enhance_comfyu...

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import torch
import torchvision.transforms.functional as tf
import torchvision.transforms.v2 as v2
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from .mmaker_color_enhance_core import color_enhance, color_blend
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class ColorEnhanceComfyNode:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "apply_color_enhance"
CATEGORY = "postprocessing/Effects"
def apply_color_enhance(self, image: torch.Tensor, strength: float):
images = []
for img in image:
edited_image = v2.ToDtype(dtype=torch.uint8, scale=True)(img).squeeze()
edited_image = color_enhance(edited_image.detach().cpu().numpy(), strength)
edited_image = tf.to_tensor(edited_image)
images.append(edited_image)
return (torch.stack(images).permute(0, 2, 3, 1),)
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class ColorBlendComfyNode:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"image_blend": ("IMAGE",),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "apply_color_enhance"
CATEGORY = "postprocessing/Effects"
def apply_color_enhance(self, image: torch.Tensor, image_blend: torch.Tensor):
images = []
image_blend = v2.ToDtype(dtype=torch.uint8, scale=True)(image_blend).squeeze().detach().cpu().numpy()
for img in image:
edited_image = v2.ToDtype(dtype=torch.uint8, scale=True)(img).squeeze()
edited_image = color_blend(edited_image.detach().cpu().numpy(), image_blend)
edited_image = tf.to_tensor(edited_image)
images.append(edited_image)
return (torch.stack(images).permute(0, 2, 3, 1),)