78 lines
2.8 KiB
Markdown
78 lines
2.8 KiB
Markdown
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# Documentation
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- Class name: SaltFilmicTransitions
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- Category: SALT/Scheduling/Image
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- Output node: False
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SaltFilmicTransitions节点旨在创建两组图像之间视觉上吸引人的过渡效果，利用各种模式如滑动和圆形扩展等。它通过在指定数量的帧上无缝地混合图像，实现动态的视觉叙事，并可通过遮罩模糊调度来选择性地控制过渡的平滑度。
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# Input types
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## Required
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- images_a
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- 要进行过渡的第一组图像。这些图像作为过渡效果的起点。
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- Comfy dtype: IMAGE
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- Python dtype: List[torch.Tensor]
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- images_b
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- 要过渡到的第二组图像。这些图像作为过渡效果的终点。
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- Comfy dtype: IMAGE
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- Python dtype: List[torch.Tensor]
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- mode
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- 指定过渡的风格，如滑动方向或圆形扩展，定义用于混合图像的视觉效果。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- transition_frames
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- 过渡发生的帧数，决定效果的持续时间和平滑度。
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- Comfy dtype: INT
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- Python dtype: int
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## Optional
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- mask_blur_schedule
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- 一个可选的调度，用于控制过渡过程中的模糊强度，允许对过渡效果进行更精细的视觉调整。
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- Comfy dtype: LIST
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- Python dtype: List[float]
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# Output types
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- images
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- 应用过渡效果后得到的图像集，根据指定的模式和参数进行混合。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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## Usage tips
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- Infra type: `GPU`
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- Common nodes: unknown
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## Source code
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```python
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class SaltFilmicTransitions:
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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_a": ("IMAGE", ),
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"images_b": ("IMAGE", ),
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"mode": (["sipe_left", "swipe_right", "swipe_up", "swipe_down", "diagonal_tl_br", "diagonal_tr_bl", "diagonal_bl_tr", "diagonal_br_tl", "circle_expand", "circle_contract"],),
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"transition_frames": ("INT", {"min": 2, "max": 1024, "default": 10}),
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},
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"optional": {
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"mask_blur_schedule": ("LIST", ),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("images",)
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FUNCTION = "generate_transition"
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CATEGORY = "SALT/Scheduling/Image"
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def generate_transition(self, images_a, images_b, mode, transition_frames, mask_blur_schedule=[0]):
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mask_blur_schedule = [float(val) for val in mask_blur_schedule]
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img_list_a = [tensor2pil(img) for img in images_a]
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img_list_b = [tensor2pil(img) for img in images_b]
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transition = ImageBatchTransition(img_list_a, img_list_b, frames_per_transition=int(transition_frames), blur_radius=mask_blur_schedule, mode=mode)
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result_images = transition.create_transition()
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result_images = [pil2tensor(img) for img in result_images]
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return (torch.cat(result_images, dim=0), )
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```
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