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add TransitionImagesInBatch -node
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kijai
parent
3df9f978f7
commit
14f6d5fb51
@ -80,6 +80,7 @@ NODE_CONFIG = {
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"SaveImageKJ": {"class": SaveImageKJ, "name": "Save Image KJ"},
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"SplitImageChannels": {"class": SplitImageChannels, "name": "Split Image Channels"},
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"TransitionImagesMulti": {"class": TransitionImagesMulti, "name": "Transition Images Multi"},
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"TransitionImagesInBatch": {"class": TransitionImagesInBatch, "name": "Transition Images In Batch"},
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#batch cropping
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"BatchCropFromMask": {"class": BatchCropFromMask, "name": "Batch Crop From Mask"},
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"BatchCropFromMaskAdvanced": {"class": BatchCropFromMaskAdvanced, "name": "Batch Crop From Mask Advanced"},
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@ -1307,33 +1307,7 @@ class CrossFadeImagesMulti:
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return image_1,
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class TransitionImagesMulti:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "transition"
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CATEGORY = "KJNodes/image"
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}),
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"image_1": ("IMAGE",),
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"image_2": ("IMAGE",),
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"interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],),
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"transition_type": (["horizontal slide", "vertical slide", "box", "circle", "horizontal bar", "vertical bar", "horizontal door", "vertical door", "fade"],),
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"transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
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"blur_radius": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 100.0, "step": 0.1}),
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"reverse": ("BOOLEAN", {"default": False}),
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"device": (["CPU", "GPU"], {"default": "CPU"}),
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},
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}
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#transitions from matteo's essential nodes
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def transition(self, inputcount, transitioning_frames, transition_type, interpolation, device, blur_radius, reverse, **kwargs):
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gpu = model_management.get_torch_device()
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def wipe(images_1, images_2, alpha, transition_type, blur_radius):
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def wipe(images_1, images_2, alpha, transition_type, blur_radius, reverse):
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width = images_1.shape[1]
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height = images_1.shape[0]
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@ -1366,16 +1340,6 @@ class TransitionImagesMulti:
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y, x = torch.meshgrid((y, x), indexing="ij")
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circle = ((x - c_x) ** 2 + (y - c_y) ** 2) <= (radius ** 2)
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mask[circle] = 1.0
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elif "horizontal bar" in transition_type:
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bar = round(height * alpha)
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y1 = (height - bar) // 2
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y2 = y1 + bar
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mask[y1:y2,:, :] = 1.0
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elif "vertical bar" in transition_type:
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bar = round(width * alpha)
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x1 = (width - bar) // 2
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x2 = x1 + bar
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mask[:, x1:x2, :] = 1.0
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elif "horizontal door" in transition_type:
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bar = math.ceil(height * alpha / 2)
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if bar > 0:
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@ -1393,51 +1357,77 @@ class TransitionImagesMulti:
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return images_1 * (1 - mask) + images_2 * mask
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def ease_in(t):
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return t * t
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def ease_out(t):
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return 1 - (1 - t) * (1 - t)
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def ease_in_out(t):
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return 3 * t * t - 2 * t * t * t
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def bounce(t):
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if t < 0.5:
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return self.ease_out(t * 2) * 0.5
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else:
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return self.ease_in((t - 0.5) * 2) * 0.5 + 0.5
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def elastic(t):
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return math.sin(13 * math.pi / 2 * t) * math.pow(2, 10 * (t - 1))
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def glitchy(t):
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return t + 0.1 * math.sin(40 * t)
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def exponential_ease_out(t):
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return 1 - (1 - t) ** 4
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def gaussian_blur(mask, blur_radius):
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if blur_radius > 0:
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kernel_size = int(blur_radius * 2) + 1
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if kernel_size % 2 == 0:
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kernel_size += 1 # Ensure kernel size is odd
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sigma = blur_radius / 3
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x = torch.arange(-kernel_size // 2 + 1, kernel_size // 2 + 1, dtype=torch.float32)
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x = torch.exp(-0.5 * (x / sigma) ** 2)
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kernel1d = x / x.sum()
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kernel2d = kernel1d[:, None] * kernel1d[None, :]
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kernel2d = kernel2d.to(mask.device)
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kernel2d = kernel2d.expand(mask.shape[2], 1, kernel2d.shape[0], kernel2d.shape[1])
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mask = mask.permute(2, 0, 1).unsqueeze(0) # Change to [C, H, W] and add batch dimension
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mask = F.conv2d(mask, kernel2d, padding=kernel_size // 2, groups=mask.shape[1])
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mask = mask.squeeze(0).permute(1, 2, 0) # Change back to [H, W, C]
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return mask
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def ease_in(t):
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return t * t
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def ease_out(t):
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return 1 - (1 - t) * (1 - t)
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def ease_in_out(t):
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return 3 * t * t - 2 * t * t * t
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def bounce(t):
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if t < 0.5:
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return ease_out(t * 2) * 0.5
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else:
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return ease_in((t - 0.5) * 2) * 0.5 + 0.5
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def elastic(t):
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return math.sin(13 * math.pi / 2 * t) * math.pow(2, 10 * (t - 1))
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def glitchy(t):
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return t + 0.1 * math.sin(40 * t)
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def exponential_ease_out(t):
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return 1 - (1 - t) ** 4
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easing_functions = {
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"linear": lambda t: t,
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"ease_in": ease_in,
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"ease_out": ease_out,
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"ease_in_out": ease_in_out,
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"bounce": bounce,
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"elastic": elastic,
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"glitchy": glitchy,
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"exponential_ease_out": exponential_ease_out,
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}
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def gaussian_blur(mask, blur_radius):
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if blur_radius > 0:
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kernel_size = int(blur_radius * 2) + 1
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if kernel_size % 2 == 0:
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kernel_size += 1 # Ensure kernel size is odd
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sigma = blur_radius / 3
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x = torch.arange(-kernel_size // 2 + 1, kernel_size // 2 + 1, dtype=torch.float32)
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x = torch.exp(-0.5 * (x / sigma) ** 2)
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kernel1d = x / x.sum()
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kernel2d = kernel1d[:, None] * kernel1d[None, :]
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kernel2d = kernel2d.to(mask.device)
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kernel2d = kernel2d.expand(mask.shape[2], 1, kernel2d.shape[0], kernel2d.shape[1])
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mask = mask.permute(2, 0, 1).unsqueeze(0) # Change to [C, H, W] and add batch dimension
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mask = F.conv2d(mask, kernel2d, padding=kernel_size // 2, groups=mask.shape[1])
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mask = mask.squeeze(0).permute(1, 2, 0) # Change back to [H, W, C]
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return mask
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easing_functions = {
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"linear": lambda t: t,
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"ease_in": ease_in,
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"ease_out": ease_out,
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"ease_in_out": ease_in_out,
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"bounce": bounce,
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"elastic": elastic,
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"glitchy": glitchy,
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"exponential_ease_out": exponential_ease_out,
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}
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class TransitionImagesMulti:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "transition"
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CATEGORY = "KJNodes/image"
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}),
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"image_1": ("IMAGE",),
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"image_2": ("IMAGE",),
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"interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],),
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"transition_type": (["horizontal slide", "vertical slide", "box", "circle", "horizontal door", "vertical door", "fade"],),
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"transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
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"blur_radius": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 100.0, "step": 0.1}),
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"reverse": ("BOOLEAN", {"default": False}),
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"device": (["CPU", "GPU"], {"default": "CPU"}),
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},
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}
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#transitions from matteo's essential nodes
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def transition(self, inputcount, transitioning_frames, transition_type, interpolation, device, blur_radius, reverse, **kwargs):
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gpu = model_management.get_torch_device()
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image_1 = kwargs["image_1"]
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height = image_1.shape[1]
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@ -1461,11 +1451,14 @@ class TransitionImagesMulti:
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last_frame_image_1 = last_frame_image_1.to(gpu)
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first_frame_image_2 = first_frame_image_2.to(gpu)
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if reverse:
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last_frame_image_1, first_frame_image_2 = first_frame_image_2, last_frame_image_1
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for frame in range(transitioning_frames):
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t = frame / (transitioning_frames - 1)
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alpha = easing_function(t)
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alpha_tensor = torch.tensor(alpha, dtype=last_frame_image_1.dtype, device=last_frame_image_1.device)
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frame_image = wipe(last_frame_image_1, first_frame_image_2, alpha_tensor, transition_type, blur_radius)
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frame_image = wipe(last_frame_image_1, first_frame_image_2, alpha_tensor, transition_type, blur_radius, reverse)
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frames.append(frame_image)
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frames = torch.stack(frames).cpu()
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@ -1473,6 +1466,58 @@ class TransitionImagesMulti:
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return image_1.cpu(),
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class TransitionImagesInBatch:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "transition"
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CATEGORY = "KJNodes/image"
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"images": ("IMAGE",),
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"interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],),
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"transition_type": (["horizontal slide", "vertical slide", "box", "circle", "horizontal door", "vertical door", "fade"],),
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"transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
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"blur_radius": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 100.0, "step": 0.1}),
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"reverse": ("BOOLEAN", {"default": False}),
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"device": (["CPU", "GPU"], {"default": "CPU"}),
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},
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}
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#transitions from matteo's essential nodes
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def transition(self, images, transitioning_frames, transition_type, interpolation, device, blur_radius, reverse):
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gpu = model_management.get_torch_device()
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easing_function = easing_functions[interpolation]
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images_list = []
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for i in range(images.shape[0] - 1):
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frames = []
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image_1 = images[i]
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image_2 = images[i + 1]
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if device == "GPU":
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image_1 = image_1.to(gpu)
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image_2 = image_2.to(gpu)
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if reverse:
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image_1, image_2 = image_2, image_1
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for frame in range(transitioning_frames):
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t = frame / (transitioning_frames - 1)
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alpha = easing_function(t)
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alpha_tensor = torch.tensor(alpha, dtype=image_1.dtype, device=image_1.device)
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frame_image = wipe(image_1, image_2, alpha_tensor, transition_type, blur_radius, reverse)
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frames.append(frame_image)
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frames = torch.stack(frames).cpu()
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images_list.append(frames)
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images = torch.cat(images_list, dim=0)
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return images.cpu(),
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class GetImageRangeFromBatch:
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RETURN_TYPES = ("IMAGE", "MASK", )
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