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https://github.com/comfyanonymous/ComfyUI.git
synced 2026-05-10 17:22:36 +08:00
post processing now loops over batch dim
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EllangoK
parent
406f2872db
commit
a305691d48
@ -27,32 +27,38 @@ class Dither:
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CATEGORY = "postprocessing"
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def dither(self, image: torch.Tensor, bits: int):
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tensor_image = image.numpy()[0]
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img = (tensor_image * 255)
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height, width, _ = img.shape
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batch_size, height, width, _ = image.shape
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result = torch.zeros_like(image)
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scale = 255 / (2**bits - 1)
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for b in range(batch_size):
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tensor_image = image[b].numpy()
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img = (tensor_image * 255)
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height, width, _ = img.shape
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for y in range(height):
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for x in range(width):
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old_pixel = img[y, x].copy()
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new_pixel = np.round(old_pixel / scale) * scale
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img[y, x] = new_pixel
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scale = 255 / (2**bits - 1)
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quant_error = old_pixel - new_pixel
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for y in range(height):
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for x in range(width):
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old_pixel = img[y, x].copy()
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new_pixel = np.round(old_pixel / scale) * scale
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img[y, x] = new_pixel
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quant_error = old_pixel - new_pixel
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if x + 1 < width:
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img[y, x + 1] += quant_error * 7 / 16
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if y + 1 < height:
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if x - 1 >= 0:
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img[y + 1, x - 1] += quant_error * 3 / 16
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img[y + 1, x] += quant_error * 5 / 16
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if x + 1 < width:
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img[y + 1, x + 1] += quant_error * 1 / 16
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img[y, x + 1] += quant_error * 7 / 16
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if y + 1 < height:
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if x - 1 >= 0:
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img[y + 1, x - 1] += quant_error * 3 / 16
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img[y + 1, x] += quant_error * 5 / 16
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if x + 1 < width:
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img[y + 1, x + 1] += quant_error * 1 / 16
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dithered = img / 255
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tensor = torch.from_numpy(dithered).unsqueeze(0)
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return (tensor,)
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dithered = img / 255
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tensor = torch.from_numpy(dithered).unsqueeze(0)
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result[b] = tensor
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return (result,)
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class KMeansQuantize:
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def __init__(self):
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@ -84,25 +90,31 @@ class KMeansQuantize:
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CATEGORY = "postprocessing"
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def kmeans_quantize(self, image: torch.Tensor, colors: int, precision: int):
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tensor_image = image.numpy()[0].astype(np.float32)
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img = tensor_image
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batch_size, height, width, _ = image.shape
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result = torch.zeros_like(image)
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height, width, c = img.shape
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for b in range(batch_size):
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tensor_image = image[b].numpy().astype(np.float32)
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img = tensor_image
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criteria = (
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cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER,
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precision * 5, 0.01
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)
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height, width, c = img.shape
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img_copy = img.reshape(-1, c)
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_, label, center = cv2.kmeans(
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img_copy, colors, None,
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criteria, 1, cv2.KMEANS_PP_CENTERS
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)
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criteria = (
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cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER,
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precision * 5, 0.01
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)
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result = center[label.flatten()].reshape(*img.shape)
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tensor = torch.from_numpy(result).unsqueeze(0)
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return (tensor,)
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img_copy = img.reshape(-1, c)
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_, label, center = cv2.kmeans(
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img_copy, colors, None,
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criteria, 1, cv2.KMEANS_PP_CENTERS
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)
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img = center[label.flatten()].reshape(*img.shape)
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tensor = torch.from_numpy(img).unsqueeze(0)
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result[b] = tensor
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return (result,)
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class GaussianBlur:
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def __init__(self):
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@ -134,10 +146,16 @@ class GaussianBlur:
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CATEGORY = "postprocessing"
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def blur(self, image: torch.Tensor, kernel_size: int, sigma: float):
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tensor_image = image.numpy()[0]
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blurred = cv2.GaussianBlur(tensor_image, (kernel_size, kernel_size), sigma)
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tensor = torch.from_numpy(blurred).unsqueeze(0)
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return (tensor,)
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batch_size, height, width, _ = image.shape
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result = torch.zeros_like(image)
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for b in range(batch_size):
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tensor_image = image[b].numpy()
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blurred = cv2.GaussianBlur(tensor_image, (kernel_size, kernel_size), sigma)
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tensor = torch.from_numpy(blurred).unsqueeze(0)
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result[b] = tensor
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return (result,)
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class Sharpen:
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def __init__(self):
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@ -169,18 +187,24 @@ class Sharpen:
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CATEGORY = "postprocessing"
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def sharpen(self, image: torch.Tensor, kernel_size: int, alpha: float):
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tensor_image = image.numpy()[0]
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batch_size, height, width, _ = image.shape
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result = torch.zeros_like(image)
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kernel = np.ones((kernel_size, kernel_size), dtype=np.float32) * -1
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center = kernel_size // 2
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kernel[center, center] = kernel_size**2
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kernel *= alpha
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for b in range(batch_size):
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tensor_image = image[b].numpy()
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sharpened = cv2.filter2D(tensor_image, -1, kernel)
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kernel = np.ones((kernel_size, kernel_size), dtype=np.float32) * -1
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center = kernel_size // 2
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kernel[center, center] = kernel_size**2
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kernel *= alpha
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tensor = torch.from_numpy(sharpened).unsqueeze(0)
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tensor = torch.clamp(tensor, 0, 1)
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return (tensor,)
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sharpened = cv2.filter2D(tensor_image, -1, kernel)
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tensor = torch.from_numpy(sharpened).unsqueeze(0)
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tensor = torch.clamp(tensor, 0, 1)
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result[b] = tensor
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return (result,)
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class CannyEdgeDetection:
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def __init__(self):
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@ -212,11 +236,17 @@ class CannyEdgeDetection:
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CATEGORY = "postprocessing"
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def canny(self, image: torch.Tensor, lower_threshold: int, upper_threshold: int):
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tensor_image = image.numpy()[0]
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gray_image = (cv2.cvtColor(tensor_image, cv2.COLOR_BGR2GRAY) * 255).astype(np.uint8)
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canny = cv2.Canny(gray_image, lower_threshold, upper_threshold)
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tensor = torch.from_numpy(canny).unsqueeze(0)
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return (tensor,)
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batch_size, height, width, _ = image.shape
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result = torch.zeros(batch_size, height, width)
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for b in range(batch_size):
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tensor_image = image[b].numpy().copy()
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gray_image = (cv2.cvtColor(tensor_image, cv2.COLOR_RGB2GRAY) * 255).astype(np.uint8)
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canny = cv2.Canny(gray_image, lower_threshold, upper_threshold)
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tensor = torch.from_numpy(canny)
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result[b] = tensor
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return (result,)
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class ColorCorrect:
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def __init__(self):
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@ -272,52 +302,57 @@ class ColorCorrect:
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CATEGORY = "postprocessing"
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def color_correct(self, image: torch.Tensor, temperature: float, hue: float, brightness: float, contrast: float, saturation: float, gamma: float):
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tensor_image = image.numpy()[0]
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batch_size, height, width, _ = image.shape
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result = torch.zeros_like(image)
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brightness /= 100
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contrast /= 100
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saturation /= 100
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temperature /= 100
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for b in range(batch_size):
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tensor_image = image[b].numpy()
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brightness = 1 + brightness
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contrast = 1 + contrast
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saturation = 1 + saturation
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brightness /= 100
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contrast /= 100
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saturation /= 100
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temperature /= 100
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modified_image = Image.fromarray((tensor_image * 255).astype(np.uint8))
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brightness = 1 + brightness
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contrast = 1 + contrast
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saturation = 1 + saturation
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# brightness
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modified_image = ImageEnhance.Brightness(modified_image).enhance(brightness)
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modified_image = Image.fromarray((tensor_image * 255).astype(np.uint8))
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# contrast
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modified_image = ImageEnhance.Contrast(modified_image).enhance(contrast)
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modified_image = np.array(modified_image).astype(np.float32)
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# brightness
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modified_image = ImageEnhance.Brightness(modified_image).enhance(brightness)
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# temperature
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if temperature > 0:
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modified_image[:, :, 0] *= 1 + temperature
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modified_image[:, :, 1] *= 1 + temperature * 0.4
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elif temperature < 0:
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modified_image[:, :, 2] *= 1 - temperature
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modified_image = np.clip(modified_image, 0, 255)/255
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# contrast
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modified_image = ImageEnhance.Contrast(modified_image).enhance(contrast)
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modified_image = np.array(modified_image).astype(np.float32)
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# gamma
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modified_image = np.clip(np.power(modified_image, gamma), 0, 1)
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# temperature
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if temperature > 0:
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modified_image[:, :, 0] *= 1 + temperature
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modified_image[:, :, 1] *= 1 + temperature * 0.4
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elif temperature < 0:
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modified_image[:, :, 2] *= 1 - temperature
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modified_image = np.clip(modified_image, 0, 255)/255
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# saturation
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hls_img = cv2.cvtColor(modified_image, cv2.COLOR_RGB2HLS)
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hls_img[:, :, 2] = np.clip(saturation*hls_img[:, :, 2], 0, 1)
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modified_image = cv2.cvtColor(hls_img, cv2.COLOR_HLS2RGB) * 255
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# gamma
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modified_image = np.clip(np.power(modified_image, gamma), 0, 1)
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# hue
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hsv_img = cv2.cvtColor(modified_image, cv2.COLOR_RGB2HSV)
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hsv_img[:, :, 0] = (hsv_img[:, :, 0] + hue) % 360
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modified_image = cv2.cvtColor(hsv_img, cv2.COLOR_HSV2RGB)
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# saturation
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hls_img = cv2.cvtColor(modified_image, cv2.COLOR_RGB2HLS)
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hls_img[:, :, 2] = np.clip(saturation*hls_img[:, :, 2], 0, 1)
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modified_image = cv2.cvtColor(hls_img, cv2.COLOR_HLS2RGB) * 255
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modified_image = modified_image.astype(np.uint8)
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modified_image = modified_image / 255
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modified_image = torch.from_numpy(modified_image).unsqueeze(0)
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# hue
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hsv_img = cv2.cvtColor(modified_image, cv2.COLOR_RGB2HSV)
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hsv_img[:, :, 0] = (hsv_img[:, :, 0] + hue) % 360
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modified_image = cv2.cvtColor(hsv_img, cv2.COLOR_HSV2RGB)
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return (modified_image, )
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modified_image = modified_image.astype(np.uint8)
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modified_image = modified_image / 255
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modified_image = torch.from_numpy(modified_image).unsqueeze(0)
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result[b] = modified_image
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return (result, )
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NODE_CLASS_MAPPINGS = {
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