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Encode text prompts with optional timestamps for timed conditioning in video processing, enabling dynamic content creation.
The FramePackTimestampedTextEncode node is designed to encode text prompts with optional timestamps for timed conditioning in video processing. This node allows you to specify when certain prompts should be applied during a video, using a format like [Xs: prompt] or [Xs-Ys: prompt], where X and Y are times in seconds. This feature is particularly useful for creating dynamic and contextually relevant content in videos, as it enables the application of different prompts at specific times. The node automatically fills gaps between specified timestamps, typically using the preceding prompt, ensuring a seamless transition throughout the video. It aligns timestamps to internal section boundaries based on a specified latent_window_size, which helps in maintaining synchronization with the video's processing. The main goal of this node is to provide a flexible and efficient way to manage and encode text prompts for video content, enhancing the creative possibilities for AI artists.
The clip parameter represents the model or tool used for tokenizing and encoding the text prompts. It is essential for converting text into a format that can be processed and applied as conditioning in the video. This parameter does not have a specific range or default value, as it depends on the implementation and the model being used.
The text parameter contains the main text prompts that you want to encode and apply to the video. These prompts can include timestamps to specify when they should be active. The text should be formatted correctly to ensure proper encoding and application.
The negative_text parameter allows you to specify text prompts that should be avoided or minimized during the video. This can be useful for creating contrast or emphasizing certain aspects by reducing the influence of specific prompts. If not provided, the node attempts to create a default negative conditioning.
The total_second_length parameter defines the overall duration of the video in seconds. It is crucial for calculating the timing and alignment of the text prompts throughout the video. This parameter ensures that the prompts are applied correctly over the entire video length.
The latent_window_size parameter specifies the processing window size used by the sampler to align timestamps. It helps in determining the internal section boundaries for applying the text prompts, ensuring that they are synchronized with the video's processing.
The prompt_blend_sections parameter determines the number of sections over which to smoothly blend between changing prompts. A higher value results in a longer, more gradual blend, which can be useful for creating smoother visual transitions when timed prompts change.
The timed_data output is a dictionary containing the encoded text prompts and their timing information. It includes the timed conditioning sections, total duration, latent window size, and prompt blend sections. This output is essential for applying the encoded prompts to the video at the specified times, ensuring that the desired effects are achieved.
The negative output provides the encoded negative text prompts, which are used to minimize or avoid certain influences during the video. This output is important for creating contrast and emphasizing specific aspects by reducing the impact of unwanted prompts.
prompt_blend_sections parameter to control the smoothness of transitions between different prompts, adjusting it based on the visual effect you want to achieve.total_second_length accurately reflects the video's duration to ensure proper alignment and application of the text prompts.RunComfy is the premier ComfyUI platform, offering ComfyUI online environment and services, along with ComfyUI workflows featuring stunning visuals. RunComfy also provides AI Models, enabling artists to harness the latest AI tools to create incredible art.