CavinHuang-comfyui-nodes-docs/nodes-docs/en-US/SaltAI_AudioViz/docs/SaltFilmicTransitions.md

tags

tags
Image ImageBlend ImageComposite

Image Batch Filmic Transitions

Documentation

• Class name: SaltFilmicTransitions
• Category: SALT/Scheduling/Image
• Output node: False

The SaltFilmicTransitions node is designed to create visually appealing transitions between two sets of images, utilizing a variety of modes such as swipes and circle expansions. It allows for dynamic visual storytelling by seamlessly blending images together over a specified number of frames, with optional control over the transition's smoothness through a mask blur schedule.

Input types

Required

• images_a
  • The first set of images to transition from. These images serve as the starting point for the transition effect.
  • Comfy dtype: IMAGE
  • Python dtype: List[torch.Tensor]
• images_b
  • The second set of images to transition to. These images serve as the destination for the transition effect.
  • Comfy dtype: IMAGE
  • Python dtype: List[torch.Tensor]
• mode
  • Specifies the style of the transition, such as swipe directions or circle expansions, defining the visual effect used to blend the images.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• transition_frames
  • The number of frames over which the transition occurs, determining the duration and smoothness of the effect.
  • Comfy dtype: INT
  • Python dtype: int

Optional

• mask_blur_schedule
  • An optional schedule to control the blur intensity over the transition, allowing for finer visual tuning of the transition effect.
  • Comfy dtype: LIST
  • Python dtype: List[float]

Output types

• images
  • Comfy dtype: IMAGE
  • The resulting set of images after applying the transition effect, blended according to the specified mode and parameters.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class SaltFilmicTransitions:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "images_a": ("IMAGE", ),
                "images_b": ("IMAGE", ),
                "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"],),
                "transition_frames": ("INT", {"min": 2, "max": 1024, "default": 10}),
            },
            "optional": {
                "mask_blur_schedule": ("LIST", ),
            }
        }
    
    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("images",)

    FUNCTION = "generate_transition"
    CATEGORY = "SALT/Scheduling/Image"

    def generate_transition(self, images_a, images_b, mode, transition_frames, mask_blur_schedule=[0]):
        mask_blur_schedule = [float(val) for val in mask_blur_schedule]
        img_list_a = [tensor2pil(img) for img in images_a]
        img_list_b = [tensor2pil(img) for img in images_b]
        transition = ImageBatchTransition(img_list_a, img_list_b, frames_per_transition=int(transition_frames), blur_radius=mask_blur_schedule, mode=mode)
        result_images = transition.create_transition()
        result_images = [pil2tensor(img) for img in result_images]
        return (torch.cat(result_images, dim=0), )