forked from comfyanonymous/ComfyUI
-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge branch 'master' into confirm-clear
- Loading branch information
Showing
5 changed files
with
257 additions
and
7 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,210 @@ | ||
| import numpy as np | ||
| import torch | ||
| import torch.nn.functional as F | ||
| from PIL import Image | ||
|
|
||
| import comfy.utils | ||
|
|
||
|
|
||
| class Blend: | ||
| def __init__(self): | ||
| pass | ||
|
|
||
| @classmethod | ||
| def INPUT_TYPES(s): | ||
| return { | ||
| "required": { | ||
| "image1": ("IMAGE",), | ||
| "image2": ("IMAGE",), | ||
| "blend_factor": ("FLOAT", { | ||
| "default": 0.5, | ||
| "min": 0.0, | ||
| "max": 1.0, | ||
| "step": 0.01 | ||
| }), | ||
| "blend_mode": (["normal", "multiply", "screen", "overlay", "soft_light"],), | ||
| }, | ||
| } | ||
|
|
||
| RETURN_TYPES = ("IMAGE",) | ||
| FUNCTION = "blend_images" | ||
|
|
||
| CATEGORY = "image/postprocessing" | ||
|
|
||
| def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str): | ||
| if image1.shape != image2.shape: | ||
| image2 = image2.permute(0, 3, 1, 2) | ||
| image2 = comfy.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center') | ||
| image2 = image2.permute(0, 2, 3, 1) | ||
|
|
||
| blended_image = self.blend_mode(image1, image2, blend_mode) | ||
| blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor | ||
| blended_image = torch.clamp(blended_image, 0, 1) | ||
| return (blended_image,) | ||
|
|
||
| def blend_mode(self, img1, img2, mode): | ||
| if mode == "normal": | ||
| return img2 | ||
| elif mode == "multiply": | ||
| return img1 * img2 | ||
| elif mode == "screen": | ||
| return 1 - (1 - img1) * (1 - img2) | ||
| elif mode == "overlay": | ||
| return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2)) | ||
| elif mode == "soft_light": | ||
| return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (self.g(img1) - img1)) | ||
| else: | ||
| raise ValueError(f"Unsupported blend mode: {mode}") | ||
|
|
||
| def g(self, x): | ||
| return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x)) | ||
|
|
||
| class Blur: | ||
| def __init__(self): | ||
| pass | ||
|
|
||
| @classmethod | ||
| def INPUT_TYPES(s): | ||
| return { | ||
| "required": { | ||
| "image": ("IMAGE",), | ||
| "blur_radius": ("INT", { | ||
| "default": 1, | ||
| "min": 1, | ||
| "max": 31, | ||
| "step": 1 | ||
| }), | ||
| "sigma": ("FLOAT", { | ||
| "default": 1.0, | ||
| "min": 0.1, | ||
| "max": 10.0, | ||
| "step": 0.1 | ||
| }), | ||
| }, | ||
| } | ||
|
|
||
| RETURN_TYPES = ("IMAGE",) | ||
| FUNCTION = "blur" | ||
|
|
||
| CATEGORY = "image/postprocessing" | ||
|
|
||
| def gaussian_kernel(self, kernel_size: int, sigma: float): | ||
| x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size), torch.linspace(-1, 1, kernel_size), indexing="ij") | ||
| d = torch.sqrt(x * x + y * y) | ||
| g = torch.exp(-(d * d) / (2.0 * sigma * sigma)) | ||
| return g / g.sum() | ||
|
|
||
| def blur(self, image: torch.Tensor, blur_radius: int, sigma: float): | ||
| if blur_radius == 0: | ||
| return (image,) | ||
|
|
||
| batch_size, height, width, channels = image.shape | ||
|
|
||
| kernel_size = blur_radius * 2 + 1 | ||
| kernel = self.gaussian_kernel(kernel_size, sigma).repeat(channels, 1, 1).unsqueeze(1) | ||
|
|
||
| image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C) | ||
| blurred = F.conv2d(image, kernel, padding=kernel_size // 2, groups=channels) | ||
| blurred = blurred.permute(0, 2, 3, 1) | ||
|
|
||
| return (blurred,) | ||
|
|
||
| class Quantize: | ||
| def __init__(self): | ||
| pass | ||
|
|
||
| @classmethod | ||
| def INPUT_TYPES(s): | ||
| return { | ||
| "required": { | ||
| "image": ("IMAGE",), | ||
| "colors": ("INT", { | ||
| "default": 256, | ||
| "min": 1, | ||
| "max": 256, | ||
| "step": 1 | ||
| }), | ||
| "dither": (["none", "floyd-steinberg"],), | ||
| }, | ||
| } | ||
|
|
||
| RETURN_TYPES = ("IMAGE",) | ||
| FUNCTION = "quantize" | ||
|
|
||
| CATEGORY = "image/postprocessing" | ||
|
|
||
| def quantize(self, image: torch.Tensor, colors: int = 256, dither: str = "FLOYDSTEINBERG"): | ||
| batch_size, height, width, _ = image.shape | ||
| result = torch.zeros_like(image) | ||
|
|
||
| dither_option = Image.Dither.FLOYDSTEINBERG if dither == "floyd-steinberg" else Image.Dither.NONE | ||
|
|
||
| for b in range(batch_size): | ||
| tensor_image = image[b] | ||
| img = (tensor_image * 255).to(torch.uint8).numpy() | ||
| pil_image = Image.fromarray(img, mode='RGB') | ||
|
|
||
| palette = pil_image.quantize(colors=colors) # Required as described in https://github.com/python-pillow/Pillow/issues/5836 | ||
| quantized_image = pil_image.quantize(colors=colors, palette=palette, dither=dither_option) | ||
|
|
||
| quantized_array = torch.tensor(np.array(quantized_image.convert("RGB"))).float() / 255 | ||
| result[b] = quantized_array | ||
|
|
||
| return (result,) | ||
|
|
||
| class Sharpen: | ||
| def __init__(self): | ||
| pass | ||
|
|
||
| @classmethod | ||
| def INPUT_TYPES(s): | ||
| return { | ||
| "required": { | ||
| "image": ("IMAGE",), | ||
| "sharpen_radius": ("INT", { | ||
| "default": 1, | ||
| "min": 1, | ||
| "max": 31, | ||
| "step": 1 | ||
| }), | ||
| "alpha": ("FLOAT", { | ||
| "default": 1.0, | ||
| "min": 0.1, | ||
| "max": 5.0, | ||
| "step": 0.1 | ||
| }), | ||
| }, | ||
| } | ||
|
|
||
| RETURN_TYPES = ("IMAGE",) | ||
| FUNCTION = "sharpen" | ||
|
|
||
| CATEGORY = "image/postprocessing" | ||
|
|
||
| def sharpen(self, image: torch.Tensor, sharpen_radius: int, alpha: float): | ||
| if sharpen_radius == 0: | ||
| return (image,) | ||
|
|
||
| batch_size, height, width, channels = image.shape | ||
|
|
||
| kernel_size = sharpen_radius * 2 + 1 | ||
| kernel = torch.ones((kernel_size, kernel_size), dtype=torch.float32) * -1 | ||
| center = kernel_size // 2 | ||
| kernel[center, center] = kernel_size**2 | ||
| kernel *= alpha | ||
| kernel = kernel.repeat(channels, 1, 1).unsqueeze(1) | ||
|
|
||
| tensor_image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C) | ||
| sharpened = F.conv2d(tensor_image, kernel, padding=center, groups=channels) | ||
| sharpened = sharpened.permute(0, 2, 3, 1) | ||
|
|
||
| result = torch.clamp(sharpened, 0, 1) | ||
|
|
||
| return (result,) | ||
|
|
||
| NODE_CLASS_MAPPINGS = { | ||
| "ImageBlend": Blend, | ||
| "ImageBlur": Blur, | ||
| "ImageQuantize": Quantize, | ||
| "ImageSharpen": Sharpen, | ||
| } |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters