xinyun/ComfyUI-KJNodes
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bugfixes

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kijai 2024-10-20 14:09:17 +03:00
parent 7c1228a5a3
commit 6842699f9e
2 changed files with 15 additions and 10 deletions
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4
nodes/curve_nodes.py
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@ -353,7 +353,9 @@ Locations are center locations.
images_list = []
masks_list = []
if len(size_multiplier) != batch_size:
if not size_multiplier or len(size_multiplier) != batch_size:
size_multiplier = [0] * batch_size
else:
size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)]
for i, coord in enumerate(coordinates):
image = Image.new("RGB", (frame_width, frame_height), bg_color)

21
nodes/image_nodes.py
View File

@ -556,10 +556,10 @@ Can be used for realtime diffusion with autoqueue.
def INPUT_TYPES(s):
return {
"required": {
"x": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
"y": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
"width": ("INT", {"default": 512,"min": 0, "max": 4096, "step": 1}),
"height": ("INT", {"default": 512,"min": 0, "max": 4096, "step": 1}),
"x": ("INT", {"default": 0,"min": 0, "max": 10000, "step": 1}),
"y": ("INT", {"default": 0,"min": 0, "max": 10000, "step": 1}),
"width": ("INT", {"default": 512,"min": 0, "max": 10000, "step": 1}),
"height": ("INT", {"default": 512,"min": 0, "max": 10000, "step": 1}),
"num_frames": ("INT", {"default": 1,"min": 1, "max": 255, "step": 1}),
"delay": ("FLOAT", {"default": 0.1,"min": 0.0, "max": 10.0, "step": 0.01}),
},
@ -1307,7 +1307,7 @@ class CrossFadeImagesMulti:
return image_1,
def wipe(images_1, images_2, alpha, transition_type, blur_radius, reverse):
def transition_images(images_1, images_2, alpha, transition_type, blur_radius, reverse):
width = images_1.shape[1]
height = images_1.shape[0]
@ -1461,7 +1461,7 @@ Creates transitions between images.
t = frame / (transitioning_frames - 1)
alpha = easing_function(t)
alpha_tensor = torch.tensor(alpha, dtype=last_frame_image_1.dtype, device=last_frame_image_1.device)
frame_image = wipe(last_frame_image_1, first_frame_image_2, alpha_tensor, transition_type, blur_radius, reverse)
frame_image = transition_images(last_frame_image_1, first_frame_image_2, alpha_tensor, transition_type, blur_radius, reverse)
frames.append(frame_image)
frames = torch.stack(frames).cpu()
@ -1493,6 +1493,8 @@ Creates transitions between images in a batch.
#transitions from matteo's essential nodes
def transition(self, images, transitioning_frames, transition_type, interpolation, device, blur_radius, reverse):
if images.shape[0] == 1:
return images,
gpu = model_management.get_torch_device()
@ -1515,7 +1517,7 @@ Creates transitions between images in a batch.
t = frame / (transitioning_frames - 1)
alpha = easing_function(t)
alpha_tensor = torch.tensor(alpha, dtype=image_1.dtype, device=image_1.device)
frame_image = wipe(image_1, image_2, alpha_tensor, transition_type, blur_radius, reverse)
frame_image = transition_images(image_1, image_2, alpha_tensor, transition_type, blur_radius, reverse)
frames.append(frame_image)
frames = torch.stack(frames).cpu()
@ -2525,11 +2527,12 @@ class ImageUncropByMask:
bbox_width = x1 - x0
# Resize source image to match the bounding box dimensions
resized_source = F.interpolate(source[i].unsqueeze(0).movedim(-1, 1), size=(bbox_height, bbox_width), mode='bilinear', align_corners=False)
#resized_source = F.interpolate(source[i].unsqueeze(0).movedim(-1, 1), size=(bbox_height, bbox_width), mode='bilinear', align_corners=False)
resized_source = common_upscale(source[i].unsqueeze(0).movedim(-1, 1), bbox_width, bbox_height, "lanczos", "disabled")
resized_source = resized_source.movedim(1, -1).squeeze(0)
# Resize mask to match the bounding box dimensions
resized_mask = F.interpolate(mask[i].unsqueeze(0).unsqueeze(0), size=(bbox_height, bbox_width), mode='nearest')
resized_mask = F.interpolate(mask[i].unsqueeze(0).unsqueeze(0), size=(bbox_height, bbox_width), mode='bilinear')
resized_mask = resized_mask.squeeze(0).squeeze(0)
# Calculate padding values