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Optimizes video loop timing using RIFE models for seamless transitions and fluid motion.
The RIFE_SeamTimingAnalyzer is a specialized node designed to optimize the timing of video loops by analyzing the motion within a sequence of frames. It leverages the RIFE (Real-Time Intermediate Flow Estimation) models, specifically versions 4.7 and 4.9, to determine the most natural transition points between the last and first frames of a video clip. This node is particularly beneficial for creating seamless video loops, as it calculates a set of timing values (t-values) that ensure the motion at the loop seam blends smoothly. By measuring the visual step sizes from real frames in the input batch, the node can choose an appropriate timing schedule that minimizes visible artifacts and enhances the overall fluidity of the loop. This capability is crucial for artists and creators who aim to produce high-quality, visually appealing video content with minimal manual intervention.
This parameter requires an input clip consisting of at least two frames. The real motion captured in these frames is used to determine the optimal timing for the loop seam. The input should be in the NHWC format, where N is the number of frames, H is the height, W is the width, and C is the number of color channels.
This integer parameter specifies the number of in-between frames to synthesize for the loop seam. It determines how many new frames will be created between the last and first frames to ensure a smooth transition. The default value is 4, and setting it to 0 will skip the synthesis process.
A boolean parameter that, when enabled, includes the distance between the first two frames of the clip as a target for timing analysis. This helps in ensuring that the initial motion is considered in the timing calculations.
Similar to use_first_two, this boolean parameter includes the distance between the last two frames of the clip as a target. It ensures that the final motion is also taken into account for a seamless loop.
This boolean parameter, when enabled, includes the median of all adjacent frame deltas as a target for timing analysis. It requires at least three frames and helps in ignoring outliers by focusing on the median motion.
This parameter allows you to choose between the RIFE 4.7 or 4.9 model files, which are used to test candidate timings. The default model is rife47.pth.
A boolean parameter that, when enabled, blends forward and backward predictions to reduce artifacts. This option can slow down the process but improves the quality of the output.
This parameter controls the quality versus speed trade-off for the probe renders. It offers options like 0.25, 0.5, 1.0, 2.0, and 4.0, with 1.0 being the recommended default for balanced performance.
This parameter allows you to choose between "MSE" (Mean Squared Error) or "L1" (L1 norm) as the visual distance metric for calibration. MSE penalizes larger errors more, while L1 is generally more forgiving.
This parameter specifies the number of binary-search iterations to perform during calibration. It helps in fine-tuning the timing values for optimal results.
These parameters define the clamp range for the timing search. Keeping t_max below 1.0 helps avoid the timing values from hugging the first frame too closely, ensuring a more natural loop.
This output is a string containing a list of timing values (e.g., "0.183112, 0.408217, 0.653991, 0.913541") that define the optimal transition points for the loop seam. These values are crucial for achieving a smooth and natural video loop.
This output echoes the input multiplier for wiring convenience, allowing you to easily reference the number of in-between frames synthesized during the process.
scale_factor settings to find the right balance between quality and processing speed. A higher scale factor may improve quality but could also increase processing time.ensemble option if you notice artifacts in the output. Although it may slow down the process, it can significantly enhance the quality of the loop.rife47.pth or rife49.pth) is missing or incorrectly specified.ckpt_name parameter is set correctly.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.