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Enhances video models with linear classifier-free guidance for improved video frame quality and coherence.
The VideoLinearCFGGuidance
node is designed to enhance video models by applying a linear classifier-free guidance (CFG) function. This node modifies the model's sampling process to interpolate between conditional and unconditional outputs, providing a smooth transition that can improve the quality and coherence of generated video frames. By adjusting the guidance scale linearly, it allows for more nuanced control over the influence of conditioning inputs, which can be particularly beneficial in generating videos with consistent and high-quality visual features. This node is essential for AI artists looking to fine-tune the balance between creativity and adherence to specific prompts in their video generation projects.
This parameter represents the video model that you want to apply the linear CFG function to. The model is the core component that generates the video frames based on the provided conditioning inputs.
min_cfg
is a floating-point parameter that sets the minimum value for the classifier-free guidance scale. This value determines the starting point of the linear interpolation between the conditional and unconditional outputs. The default value is 1.0, with a minimum of 0.0 and a maximum of 100.0. Adjusting this value can help control the initial influence of the conditioning inputs on the generated video frames.
The output is the modified video model with the linear CFG function applied. This model will now use the linear interpolation method to balance the conditional and unconditional outputs during the video generation process, resulting in smoother and more coherent video frames.
min_cfg
values to find the optimal balance between creativity and adherence to the conditioning inputs for your specific video generation task.min_cfg
value to ensure consistent results throughout the entire video.set_model_sampler_cfg_function
method is not correctly defined.VideoLinearCFGGuidance
node. Verify that the model has the set_model_sampler_cfg_function
method implemented.min_cfg
value is set outside the allowed range.min_cfg
value to be within the range of 0.0 to 100.0. Double-check the input value to ensure it meets the specified constraints.© Copyright 2024 RunComfy. All Rights Reserved.