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Facilitates visual feature transfer from image to video using advanced encoding for consistent style and motion.
The ES_VideoTransfer
node is designed to facilitate the transfer of visual features from an initial image to a video sequence, leveraging advanced encoding techniques. This node is particularly useful for AI artists looking to create consistent and visually appealing video content from a single image. By encoding the initial image and applying various transformations, the node ensures that the resulting video frames maintain a coherent style and motion, enhancing the overall visual experience. The primary goal of this node is to streamline the process of video creation by automating the transfer of image features, making it easier for you to produce high-quality video content without extensive manual adjustments.
This parameter represents the vision model used to encode the initial image. It is crucial for extracting visual features that will be transferred to the video frames. The model should be compatible with the CLIP (Contrastive Language-Image Pre-Training) framework.
The initial image from which visual features are extracted. This image serves as the basis for the video frames, ensuring that the style and content are consistently transferred. The image should be in a format supported by the node.
The Variational Autoencoder (VAE) model used to encode the image into a latent space. This encoding is essential for generating the video frames with the desired visual features. The VAE model should be pre-trained and compatible with the node.
The width of the video frames to be generated. This parameter determines the horizontal resolution of the output video. The default value is 1024, with a minimum of 16 and a maximum defined by the system's maximum resolution, adjustable in steps of 8.
The height of the video frames to be generated. This parameter determines the vertical resolution of the output video. The default value is 576, with a minimum of 16 and a maximum defined by the system's maximum resolution, adjustable in steps of 8.
The number of frames to be generated for the video. This parameter controls the length of the video sequence. The default value is 14, with a minimum of 1 and a maximum of 4096 frames.
An identifier for the motion bucket, which helps in organizing and managing different motion patterns in the video. The default value is 127, with a range from 1 to 1023.
Frames per second (FPS) for the output video. This parameter determines the playback speed of the video. The default value is 6, with a minimum of 1 and a maximum of 1024 FPS.
The level of augmentation applied to the encoded pixels. This parameter adds noise to the pixels to enhance the diversity of the generated frames. The default value is 0.0, with a range from 0.0 to 10.0, adjustable in steps of 0.01.
This output contains the positive conditioning data, which includes the encoded image features and additional metadata such as motion bucket ID, FPS, augmentation level, and the concatenated latent image. This data is used to generate the video frames with the desired visual characteristics.
This output contains the negative conditioning data, which serves as a counterbalance to the positive conditioning. It includes zeroed-out versions of the encoded image features and metadata, ensuring that the model can differentiate between the presence and absence of specific visual features.
The latent space representation of the video frames. This output is a tensor that holds the encoded features for each frame, allowing for efficient generation and manipulation of the video content. The latent space is crucial for maintaining the consistency and quality of the video frames.
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