ComfyUI Node: STATIC_TRT_MODEL_CONVERSION

STATIC_TRT_MODEL_CONVERSION:

The STATIC_TRT_MODEL_CONVERSION node is designed to convert AI models into TensorRT format with static input dimensions. This conversion optimizes the model for inference on NVIDIA GPUs, significantly enhancing performance by leveraging TensorRT's capabilities. The primary benefit of using this node is the substantial reduction in inference time, making it ideal for applications requiring real-time processing. The node ensures that the model is converted with fixed input sizes, which can lead to more efficient execution compared to dynamic input sizes. This is particularly useful in scenarios where the input dimensions are known and do not change, such as in certain image processing or video analysis tasks.

STATIC_TRT_MODEL_CONVERSION Input Parameters:

model

The model parameter represents the AI model you wish to convert to TensorRT format. This model should be compatible with ONNX format as the conversion process involves exporting the model to ONNX before converting it to TensorRT. The quality and compatibility of the model directly impact the success of the conversion process.

filename_prefix

This parameter specifies the prefix for the output filenames generated during the conversion process. It helps in organizing and identifying the converted model files. Ensure that the prefix is unique and descriptive to avoid confusion with other files.

batch_size_opt

The batch_size_opt parameter defines the optimal batch size for the model during inference. This fixed batch size is used to optimize the model for performance. The value should be chosen based on the typical batch size used in your application to achieve the best performance.

height_opt

This parameter sets the optimal height of the input data for the model. The height should be fixed and match the expected input dimensions of the model. Incorrect values can lead to errors during the conversion process.

width_opt

Similar to height_opt, the width_opt parameter specifies the optimal width of the input data. It should be set to the fixed width that the model expects. Ensuring the correct width is crucial for a successful conversion.

context_opt

The context_opt parameter defines the optimal context size for the model. This is relevant for models that require a specific context size for processing, such as certain types of sequence models. The value should be chosen based on the model's requirements.

num_video_frames

This parameter indicates the number of video frames to be processed by the model. It is particularly useful for video analysis tasks where the model processes a fixed number of frames at a time. The value should match the typical number of frames used in your application.

STATIC_TRT_MODEL_CONVERSION Output Parameters:

output_model

The output_model parameter represents the converted TensorRT model. This model is optimized for inference on NVIDIA GPUs with static input dimensions, providing enhanced performance and reduced latency. The output model can be directly used for inference tasks, leveraging the benefits of TensorRT optimization.

STATIC_TRT_MODEL_CONVERSION Usage Tips:

• Ensure that the input dimensions (height, width, batch size) are fixed and match the expected dimensions of the model to avoid conversion errors.
• Use a descriptive and unique filename prefix to easily identify and manage the converted model files.
• Test the converted model thoroughly to ensure it performs as expected in your specific application scenario.

STATIC_TRT_MODEL_CONVERSION Common Errors and Solutions:

ONNX load ERROR

• Explanation: This error occurs when the ONNX model cannot be loaded successfully during the conversion process.
• Solution: Verify that the model is correctly exported to ONNX format and that the file path is correct. Ensure that the model is compatible with ONNX and does not contain unsupported operations.

Dimension mismatch

• Explanation: This error happens when the input dimensions specified do not match the expected dimensions of the model.
• Solution: Double-check the height_opt, width_opt, and batch_size_opt parameters to ensure they match the model's expected input dimensions. Adjust the values accordingly and retry the conversion.

Unsupported operations

• Explanation: The model contains operations that are not supported by TensorRT, leading to conversion failure.
• Solution: Review the model architecture and replace or modify unsupported operations. Consult TensorRT documentation for a list of supported operations and make necessary adjustments to the model.