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Add ImageBatchJoinWithTransition
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kijai
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f20cb4c108
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@ -55,6 +55,7 @@ NODE_CONFIG = {
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"ImageBatchFilter": {"class": ImageBatchFilter, "name": "Image Batch Filter"},
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"ImageAndMaskPreview": {"class": ImageAndMaskPreview},
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"ImageAddMulti": {"class": ImageAddMulti, "name": "Image Add Multi"},
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"ImageBatchJoinWithTransition": {"class": ImageBatchJoinWithTransition, "name": "Image Batch Join With Transition"},
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"ImageBatchMulti": {"class": ImageBatchMulti, "name": "Image Batch Multi"},
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"ImageBatchRepeatInterleaving": {"class": ImageBatchRepeatInterleaving},
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"ImageBatchTestPattern": {"class": ImageBatchTestPattern, "name": "Image Batch Test Pattern"},
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@ -1252,28 +1252,6 @@ nodes for example.
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return (preview, )
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return(self.save_images(preview, filename_prefix, prompt, extra_pnginfo))
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class CrossFadeImages:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "crossfadeimages"
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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_1": ("IMAGE",),
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"images_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_start_index": ("INT", {"default": 1,"min": -4096, "max": 4096, "step": 1}),
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"transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
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"start_level": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 1.0, "step": 0.01}),
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"end_level": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 1.0, "step": 0.01}),
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},
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}
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def crossfadeimages(self, images_1, images_2, transition_start_index, transitioning_frames, interpolation, start_level, end_level):
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def crossfade(images_1, images_2, alpha):
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crossfade = (1 - alpha) * images_1 + alpha * images_2
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return crossfade
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@ -1306,6 +1284,28 @@ class CrossFadeImages:
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"exponential_ease_out": exponential_ease_out,
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}
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class CrossFadeImages:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "crossfadeimages"
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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_1": ("IMAGE",),
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"images_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_start_index": ("INT", {"default": 1,"min": -4096, "max": 4096, "step": 1}),
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"transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
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"start_level": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 1.0, "step": 0.01}),
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"end_level": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 1.0, "step": 0.01}),
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},
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}
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def crossfadeimages(self, images_1, images_2, transition_start_index, transitioning_frames, interpolation, start_level, end_level):
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crossfade_images = []
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if transition_start_index < 0:
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@ -1359,38 +1359,6 @@ class CrossFadeImagesMulti:
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def crossfadeimages(self, inputcount, transitioning_frames, interpolation, **kwargs):
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def crossfade(images_1, images_2, alpha):
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crossfade = (1 - alpha) * images_1 + alpha * images_2
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return crossfade
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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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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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image_1 = kwargs["image_1"]
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height = image_1.shape[1]
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width = image_1.shape[2]
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@ -1473,24 +1441,6 @@ def transition_images(images_1, images_2, alpha, transition_type, blur_radius, r
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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 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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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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@ -1643,6 +1593,74 @@ Creates transitions between images in a batch.
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return images.cpu(),
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class ImageBatchJoinWithTransition:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "transition_batches"
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CATEGORY = "KJNodes/image"
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DESCRIPTION = """
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Transitions between two batches of images, starting at a specified index in the first batch.
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During the transition, frames from both batches are blended frame-by-frame, so the video keeps playing.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images_1": ("IMAGE",),
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"images_2": ("IMAGE",),
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"start_index": ("INT", {"default": 0, "min": -10000, "max": 10000, "step": 1}),
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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": 1, "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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def transition_batches(self, images_1, images_2, start_index, interpolation, transition_type, transitioning_frames, blur_radius, reverse, device):
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if images_1.shape[0] == 0 or images_2.shape[0] == 0:
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raise ValueError("Both input batches must have at least one image.")
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if start_index < 0:
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start_index = images_1.shape[0] + start_index
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if start_index < 0 or start_index > images_1.shape[0]:
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raise ValueError("start_index is out of range.")
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gpu = model_management.get_torch_device()
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easing_function = easing_functions[interpolation]
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out_frames = []
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# Add images from images_1 up to start_index
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if start_index > 0:
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out_frames.append(images_1[:start_index])
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# Determine how many frames we can blend
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max_transition = min(transitioning_frames, images_1.shape[0] - start_index, images_2.shape[0])
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# Blend corresponding frames from both batches
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for i in range(max_transition):
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img1 = images_1[start_index + i]
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img2 = images_2[i]
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if device == "GPU":
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img1 = img1.to(gpu)
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img2 = img2.to(gpu)
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if reverse:
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img1, img2 = img2, img1
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t = i / (max_transition - 1) if max_transition > 1 else 1.0
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alpha = easing_function(t)
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alpha_tensor = torch.tensor(alpha, dtype=img1.dtype, device=img1.device)
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frame_image = transition_images(img1, img2, alpha_tensor, transition_type, blur_radius, reverse)
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out_frames.append(frame_image.cpu().unsqueeze(0))
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# Add remaining images from images_2 after transition
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if images_2.shape[0] > max_transition:
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out_frames.append(images_2[max_transition:])
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# Concatenate all frames
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out = torch.cat(out_frames, dim=0)
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return (out.cpu(),)
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class ShuffleImageBatch:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "shuffle"
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