ComfyUI Node: Smooth Video

Smooth Video:

SmoothVideo is a powerful node designed to enhance the visual quality of videos by applying a smoothing effect. This node is particularly useful for AI artists who want to create seamless transitions and reduce visual noise between frames in a video. By leveraging advanced algorithms, SmoothVideo ensures that the output video maintains high fidelity and smoothness, making it ideal for artistic projects that require a polished and professional look. The node processes input frames and applies a smoothing technique that blends the frames together, resulting in a cohesive and visually appealing video. This can be especially beneficial for projects involving style transfer, animation, or any scenario where frame consistency is crucial.

Smooth Video Input Parameters:

orginalframe

This parameter expects an image that serves as the original frame of the video. It is used as a reference point for the smoothing process. The quality and content of this frame can significantly impact the final output, as it provides the baseline for the smoothing algorithm.

keyframe

This parameter expects an image that acts as the keyframe for the video. The keyframe is used to guide the style and appearance of the smoothed video. It helps in maintaining the desired visual aesthetics throughout the video.

accuracy

This integer parameter controls the accuracy of the smoothing process. It has a default value of 1, with a minimum value of 1 and a maximum value of 3. Higher values result in more accurate smoothing but may increase processing time. Adjusting this parameter allows you to balance between speed and quality based on your project's needs.

window_size

This integer parameter defines the size of the window used for the smoothing algorithm. It has a default value of 15, with a minimum value of 1 and a maximum value of 100. The window size determines the range of frames considered for smoothing, affecting the smoothness and coherence of the output video.

batch_size

This integer parameter specifies the number of frames processed in each batch. It has a default value of 16, with a minimum value of 1 and a maximum value of 100. Larger batch sizes can speed up the processing but may require more memory. Adjust this parameter based on your system's capabilities and the desired processing speed.

Smooth Video Output Parameters:

torch_images

The output of the SmoothVideo node is a tensor of images in the form of a PyTorch tensor. This tensor contains the smoothed frames of the video, normalized and ready for further processing or rendering. The output tensor is of type torch.float32, ensuring compatibility with various deep learning frameworks and tools.

Smooth Video Usage Tips:

• To achieve the best results, ensure that the original frame and keyframe are of high quality and visually consistent.
• Experiment with the accuracy parameter to find the optimal balance between processing time and output quality for your specific project.
• Adjust the window_size parameter to control the smoothness of transitions between frames. Larger window sizes generally result in smoother videos.
• Use a batch size that matches your system's memory capacity to avoid running into memory issues during processing.

Smooth Video Common Errors and Solutions:

"frames max min: <value> <value> <shape> <type> <length>"

• Explanation: This message indicates the maximum and minimum values, shape, type, and length of the frames being processed.
• Solution: Ensure that the input frames are correctly formatted and within the expected range. Verify that the input images are not corrupted and meet the required specifications.

"numpy_images.shape <shape>"

• Explanation: This message shows the shape of the numpy array containing the stacked frames.
• Solution: Check that the input frames are correctly stacked and that the numpy array has the expected dimensions. If the shape is incorrect, review the input parameters and ensure they are properly configured.

"torch_images.shape <shape>"

• Explanation: This message displays the shape of the PyTorch tensor containing the smoothed frames.
• Solution: Verify that the tensor shape matches the expected output dimensions. If there is a discrepancy, ensure that the input frames and parameters are correctly set up.

"torch_images.dtype <dtype>"

• Explanation: This message indicates the data type of the PyTorch tensor.
• Solution: Ensure that the tensor data type is torch.float32. If the data type is incorrect, check the normalization and conversion steps in the processing pipeline.