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Update image_nodes.py

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kijai 2024-10-15 12:26:01 +03:00
parent 579f0b4050
commit f123b36d28
1 changed files with 31 additions and 8 deletions
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39
nodes/image_nodes.py
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@ -1322,19 +1322,22 @@ class TransitionImagesMulti:
"interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],),
"transition_type": (["horizontal slide", "vertical slide", "box", "circle", "horizontal bar", "vertical bar", "horizontal door", "vertical door", "fade"],),
"transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
"blur_radius": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 100.0, "step": 0.1}),
"device": (["CPU", "GPU"], {"default": "CPU"}),
},
}
#transitions from matteo's essential nodes
def transition(self, inputcount, transitioning_frames, transition_type, interpolation, device, **kwargs):
def transition(self, inputcount, transitioning_frames, transition_type, interpolation, device, blur_radius, **kwargs):
device = model_management.get_torch_device()
gpu = model_management.get_torch_device()
def wipe(images_1, images_2, alpha, transition_type):
def wipe(images_1, images_2, alpha, transition_type, blur_radius):
width = images_1.shape[1]
height = images_1.shape[0]
mask = torch.zeros_like(images_1)
mask = torch.zeros_like(images_1, device=images_1.device)
alpha = alpha.item()
if "horizontal slide" in transition_type:
@ -1383,6 +1386,8 @@ class TransitionImagesMulti:
elif "fade" in transition_type:
mask[:, :, :] = alpha
mask = gaussian_blur(mask, blur_radius)
return images_1 * (1 - mask) + images_2 * mask
def ease_in(t):
@ -1403,6 +1408,24 @@ class TransitionImagesMulti:
def exponential_ease_out(t):
return 1 - (1 - t) ** 4
def gaussian_blur(mask, blur_radius):
print(mask.device)
if blur_radius > 0:
kernel_size = int(blur_radius * 2) + 1
if kernel_size % 2 == 0:
kernel_size += 1 # Ensure kernel size is odd
sigma = blur_radius / 3
x = torch.arange(-kernel_size // 2 + 1, kernel_size // 2 + 1, dtype=torch.float32)
x = torch.exp(-0.5 * (x / sigma) ** 2)
kernel1d = x / x.sum()
kernel2d = kernel1d[:, None] * kernel1d[None, :]
kernel2d = kernel2d.to(mask.device)
kernel2d = kernel2d.expand(mask.shape[2], 1, kernel2d.shape[0], kernel2d.shape[1])
mask = mask.permute(2, 0, 1).unsqueeze(0) # Change to [C, H, W] and add batch dimension
mask = F.conv2d(mask, kernel2d, padding=kernel_size // 2, groups=mask.shape[1])
mask = mask.squeeze(0).permute(1, 2, 0) # Change back to [H, W, C]
return mask
easing_functions = {
"linear": lambda t: t,
"ease_in": ease_in,
@ -1433,17 +1456,17 @@ class TransitionImagesMulti:
last_frame_image_1 = image_1[-1]
first_frame_image_2 = new_image[0]
if device == "GPU":
last_frame_image_1 = last_frame_image_1.to(device)
first_frame_image_2 = first_frame_image_2.to(device)
last_frame_image_1 = last_frame_image_1.to(gpu)
first_frame_image_2 = first_frame_image_2.to(gpu)
for frame in range(transitioning_frames):
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)
frame_image = wipe(last_frame_image_1, first_frame_image_2, alpha_tensor, transition_type, blur_radius)
frames.append(frame_image)
frames = torch.stack(frames)
frames = torch.stack(frames).cpu()
image_1 = torch.cat((image_1, frames, new_image), dim=0)
return image_1.cpu(),