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ComfyUI
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use common_upcale in blend

This commit is contained in:
EllangoK 2023-04-04 10:57:34 -04:00
parent fa2febc062
commit 56196ab0f7
1 changed files with 5 additions and 24 deletions
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29
comfy_extras/nodes_post_processing.py
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@ -3,6 +3,8 @@ import torch
import torch.nn.functional as F import torch.nn.functional as F
from PIL import Image from PIL import Image
import comfy.utils
class Blend: class Blend:
def __init__(self): def __init__(self):
@ -31,7 +33,9 @@ class Blend:
def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str): def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str):
if image1.shape != image2.shape: if image1.shape != image2.shape:
image2 = self.crop_and_resize(image2, image1.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 = self.blend_mode(image1, image2, blend_mode)
blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor
@ -55,29 +59,6 @@ class Blend:
def g(self, x): def g(self, x):
return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x)) return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x))
def crop_and_resize(self, img: torch.Tensor, target_shape: tuple):
batch_size, img_h, img_w, img_c = img.shape
_, target_h, target_w, _ = target_shape
img_aspect_ratio = img_w / img_h
target_aspect_ratio = target_w / target_h
# Crop center of the image to the target aspect ratio
if img_aspect_ratio > target_aspect_ratio:
new_width = int(img_h * target_aspect_ratio)
left = (img_w - new_width) // 2
img = img[:, :, left:left + new_width, :]
else:
new_height = int(img_w / target_aspect_ratio)
top = (img_h - new_height) // 2
img = img[:, top:top + new_height, :, :]
# Resize to target size
img = img.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
img = F.interpolate(img, size=(target_h, target_w), mode='bilinear', align_corners=False)
img = img.permute(0, 2, 3, 1)
return img
class Blur: class Blur:
def __init__(self): def __init__(self):
pass pass