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Update CrossFadeImages to allow joining different batch sizes
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@ -1265,7 +1265,7 @@ class CrossFadeImages:
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"images_1": ("IMAGE",),
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"images_1": ("IMAGE",),
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"images_2": ("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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"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": 0, "max": 4096, "step": 1}),
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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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"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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"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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"end_level": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 1.0, "step": 0.01}),
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@ -1308,11 +1308,18 @@ class CrossFadeImages:
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crossfade_images = []
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crossfade_images = []
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if transition_start_index < 0:
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transition_start_index = len(images_1) + transition_start_index
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if transition_start_index < 0:
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raise ValueError("Transition start index is out of range for images_1.")
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transitioning_frames = min(transitioning_frames, len(images_1) - transition_start_index, len(images_2))
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alphas = torch.linspace(start_level, end_level, transitioning_frames)
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alphas = torch.linspace(start_level, end_level, transitioning_frames)
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for i in range(transitioning_frames):
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for i in range(transitioning_frames):
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alpha = alphas[i]
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alpha = alphas[i]
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image1 = images_1[i + transition_start_index]
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image1 = images_1[transition_start_index + i]
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image2 = images_2[i + transition_start_index]
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image2 = images_2[i]
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easing_function = easing_functions.get(interpolation)
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easing_function = easing_functions.get(interpolation)
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alpha = easing_function(alpha) # Apply the easing function to the alpha value
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alpha = easing_function(alpha) # Apply the easing function to the alpha value
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@ -1321,23 +1328,16 @@ class CrossFadeImages:
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# Convert crossfade_images to tensor
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# Convert crossfade_images to tensor
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crossfade_images = torch.stack(crossfade_images, dim=0)
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crossfade_images = torch.stack(crossfade_images, dim=0)
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# Get the last frame result of the interpolation
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last_frame = crossfade_images[-1]
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# Calculate the number of remaining frames from images_2
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remaining_frames = len(images_2) - (transition_start_index + transitioning_frames)
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# Crossfade the remaining frames with the last used alpha value
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for i in range(remaining_frames):
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alpha = alphas[-1]
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image1 = images_1[i + transition_start_index + transitioning_frames]
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image2 = images_2[i + transition_start_index + transitioning_frames]
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easing_function = easing_functions.get(interpolation)
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alpha = easing_function(alpha) # Apply the easing function to the alpha value
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crossfade_image = crossfade(image1, image2, alpha)
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# Append the beginning of images_1 (before the transition)
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crossfade_images = torch.cat([crossfade_images, crossfade_image.unsqueeze(0)], dim=0)
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# Append the beginning of images_1
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beginning_images_1 = images_1[:transition_start_index]
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beginning_images_1 = images_1[:transition_start_index]
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crossfade_images = torch.cat([beginning_images_1, crossfade_images], dim=0)
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crossfade_images = torch.cat([beginning_images_1, crossfade_images], dim=0)
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# Append the remaining frames of images_2 (after the transition)
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remaining_images_2 = images_2[transitioning_frames:]
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if len(remaining_images_2) > 0:
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crossfade_images = torch.cat([crossfade_images, remaining_images_2], dim=0)
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return (crossfade_images, )
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return (crossfade_images, )
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class CrossFadeImagesMulti:
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class CrossFadeImagesMulti:
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