ComfyUI Node: DYNAMIC TRT_MODEL CONVERSION

DYNAMIC_TRT_MODEL_CONVERSION:

The DYNAMIC_TRT_MODEL_CONVERSION node is designed to convert AI models into TensorRT format dynamically, allowing for optimized performance across a range of input sizes and batch configurations. This node is particularly beneficial for AI artists who work with varying model inputs, as it provides the flexibility to handle different dimensions and batch sizes without the need for multiple static models. By leveraging TensorRT's dynamic capabilities, this node ensures that your models run efficiently on NVIDIA GPUs, offering faster inference times and reduced latency. The primary goal of this node is to streamline the model conversion process, making it easier for you to deploy high-performance AI models in your creative projects.

DYNAMIC_TRT_MODEL_CONVERSION Input Parameters:

model

This parameter represents the AI model that you wish to convert to TensorRT format. The model should be compatible with ONNX format as the conversion process involves exporting the model to ONNX before converting it to TensorRT. Ensure that your model is properly trained and validated before using this node for conversion.

filename_prefix

This parameter specifies the prefix for the output filenames generated during the conversion process. It helps in organizing and identifying the converted models, especially when dealing with multiple models or versions. Choose a meaningful prefix that reflects the model's purpose or version.

batch_size_min

This parameter defines the minimum batch size that the converted TensorRT model should support. It allows the model to handle smaller batches efficiently, which is useful for scenarios with limited input data. The value should be a positive integer, with a typical default value of 1.

batch_size_opt

This parameter sets the optimal batch size for the converted TensorRT model. The optimal batch size is the most frequently used batch size during inference, and setting this correctly can significantly enhance performance. The value should be a positive integer, with a typical default value that matches your common use case.

batch_size_max

This parameter indicates the maximum batch size that the converted TensorRT model should support. It ensures that the model can handle larger batches when needed, providing flexibility for different workloads. The value should be a positive integer, with a typical default value that accommodates your largest expected batch size.

height_min

This parameter specifies the minimum height of the input images that the converted TensorRT model should support. It allows the model to process smaller images efficiently. The value should be a positive integer, with a typical default value that matches the smallest expected input height.

height_opt

This parameter sets the optimal height of the input images for the converted TensorRT model. The optimal height is the most frequently used height during inference, and setting this correctly can enhance performance. The value should be a positive integer, with a typical default value that matches your common use case.

height_max

This parameter indicates the maximum height of the input images that the converted TensorRT model should support. It ensures that the model can handle larger images when needed. The value should be a positive integer, with a typical default value that accommodates your largest expected input height.

width_min

This parameter specifies the minimum width of the input images that the converted TensorRT model should support. It allows the model to process smaller images efficiently. The value should be a positive integer, with a typical default value that matches the smallest expected input width.

width_opt

This parameter sets the optimal width of the input images for the converted TensorRT model. The optimal width is the most frequently used width during inference, and setting this correctly can enhance performance. The value should be a positive integer, with a typical default value that matches your common use case.

width_max

This parameter indicates the maximum width of the input images that the converted TensorRT model should support. It ensures that the model can handle larger images when needed. The value should be a positive integer, with a typical default value that accommodates your largest expected input width.

context_min

This parameter defines the minimum context size that the converted TensorRT model should support. It allows the model to handle smaller contexts efficiently. The value should be a positive integer, with a typical default value that matches the smallest expected context size.

context_opt

This parameter sets the optimal context size for the converted TensorRT model. The optimal context size is the most frequently used context size during inference, and setting this correctly can enhance performance. The value should be a positive integer, with a typical default value that matches your common use case.

context_max

This parameter indicates the maximum context size that the converted TensorRT model should support. It ensures that the model can handle larger contexts when needed. The value should be a positive integer, with a typical default value that accommodates your largest expected context size.

num_video_frames

This parameter specifies the number of video frames that the converted TensorRT model should support. It is particularly useful for models that process video data, ensuring that the model can handle the required number of frames efficiently. The value should be a positive integer, with a typical default value that matches your common use case.

DYNAMIC_TRT_MODEL_CONVERSION Output Parameters:

converted_model

This parameter represents the TensorRT model that has been successfully converted from the original AI model. The converted model is optimized for dynamic input sizes and batch configurations, providing enhanced performance and flexibility. You can use this model for inference on NVIDIA GPUs, benefiting from faster processing times and reduced latency.

DYNAMIC_TRT_MODEL_CONVERSION Usage Tips:

• Ensure that your input model is in ONNX format before using this node for conversion.
• Set the optimal batch size, height, and width parameters based on your most common use cases to achieve the best performance.
• Use meaningful filename prefixes to organize and identify your converted models easily.
• Test the converted model with different input sizes and batch configurations to verify its dynamic capabilities.

DYNAMIC_TRT_MODEL_CONVERSION Common Errors and Solutions:

ONNX load ERROR

• Explanation: This error occurs when the ONNX model file cannot be loaded successfully during the conversion process.
• Solution: Ensure that the input model is correctly exported to ONNX format and that the file path is correct. Check for any compatibility issues with the ONNX version.

Model conversion failed

• Explanation: This error indicates that the model conversion process encountered an issue and could not complete successfully.
• Solution: Verify that all input parameters are set correctly and that the input model is compatible with TensorRT. Check the logs for detailed error messages and address any specific issues mentioned.

Unsupported input dimensions

• Explanation: This error occurs when the input dimensions specified are not supported by the conversion process.
• Solution: Ensure that the minimum, optimal, and maximum values for batch size, height, width, and context are within acceptable ranges and compatible with the input model.