ComfyUI Node: JR MiniMax H3 Neural Latent Upscaler

JR MiniMax H3 Neural Latent Upscaler:

The JR_MiniMaxH3NeuralLatentUpscaler is a specialized node designed for the spatial upscaling of MiniMax H3 video latents using a 3D neural network. This node leverages a user-supplied H3-specific neural checkpoint to enhance the resolution of a 24-channel MiniMax H3 video latent while maintaining important metadata such as Batch (B), Channels (C), Time (T), and Latent information. The primary goal of this node is to provide a seamless and efficient way to upscale video latents, ensuring that the quality and integrity of the original content are preserved. By utilizing advanced neural network techniques, this node offers a powerful tool for AI artists looking to improve the visual quality of their video projects without compromising on detail or accuracy.

JR MiniMax H3 Neural Latent Upscaler Input Parameters:

video_latent

This parameter represents the input video latent that you wish to upscale. It must be a dictionary containing a samples key, which holds a 5-dimensional tensor with the shape [B, 24, T, H, W]. This structure ensures that the latent data is correctly formatted for processing by the neural upscaler.

resize_mode

The resize_mode parameter determines the method used for upscaling. It can be set to either scale or megapixels. When set to scale, the upscaling is based on a specified scale factor. When set to megapixels, the upscaling is based on achieving a target number of megapixels. The default value is scale.

scale

This parameter is used when resize_mode is set to scale. It specifies the factor by which the video latent should be upscaled. The value must be a floating-point number between 1.0 and 4.0, with a default of 1.5. Adjusting this value allows you to control the degree of upscaling applied to the video latent.

target_megapixels

When resize_mode is set to megapixels, this parameter specifies the target number of megapixels for the upscaled video latent. It must be a floating-point number between 0.01 and 64.0, with a default of 2.0. This setting allows you to achieve a specific resolution in terms of megapixels, providing flexibility in the final output size.

JR MiniMax H3 Neural Latent Upscaler Output Parameters:

video_latent

The video_latent output is the upscaled version of the input video latent. It retains the original metadata and structure while providing enhanced spatial resolution. This output is crucial for further processing or rendering in your video projects, ensuring that the upscaled content meets your quality expectations.

status

The status output provides a string message indicating the success or failure of the upscaling operation. This feedback is essential for understanding the outcome of the process and diagnosing any issues that may arise during execution.

JR MiniMax H3 Neural Latent Upscaler Usage Tips:

• Ensure that your input video latent is correctly formatted as a dictionary with a samples key containing a 5D tensor. This is crucial for the node to function properly.
• Choose the resize_mode that best suits your project needs. Use scale for straightforward upscaling by a factor, and megapixels when you have a specific resolution target in mind.
• Adjust the scale or target_megapixels parameters carefully to avoid exceeding the maximum supported scale of 4.0, which could lead to errors or suboptimal results.

JR MiniMax H3 Neural Latent Upscaler Common Errors and Solutions:

"video_latent must be a LATENT dictionary containing 'samples'."

• Explanation: This error occurs when the input video latent is not formatted correctly as a dictionary with a samples key.
• Solution: Ensure that your input is a dictionary with a samples key containing a 5D tensor.

"Expected MiniMax H3 video latent [B,24,T,H,W]."

• Explanation: The input tensor does not match the expected shape of [B, 24, T, H, W].
• Solution: Verify that your input tensor has the correct dimensions and structure before passing it to the node.

"scale must be finite and between 1.0 and 4.0."

• Explanation: The scale parameter is set to a value outside the allowed range.
• Solution: Adjust the scale parameter to be within the range of 1.0 to 4.0.

"target_megapixels must be finite and between 0.01 and 64.0."

• Explanation: The target_megapixels parameter is set to a value outside the allowed range.
• Solution: Ensure that the target_megapixels value is between 0.01 and 64.0.

"Neural backend did not return a torch.Tensor."

• Explanation: The neural upscaling process did not produce a valid tensor output.
• Solution: Check the compatibility of the neural checkpoint and ensure that the input data is correctly formatted.