ComfyUI Node: PM Merge LoRA SVD

PM Merge LoRA SVD:

The PM LoRA SVD Merger node is designed to merge multiple LoRA (Low-Rank Adaptation) models using Singular Value Decomposition (SVD). This node allows you to combine the strengths of different LoRA models into a single, more powerful model. By leveraging SVD, the node can efficiently handle the merging process, ensuring that the resulting model maintains high performance while potentially reducing its complexity. This is particularly useful for AI artists who want to enhance their models without manually tweaking each one. The node supports various merging modes and parameters, providing flexibility and control over the merging process.

PM Merge LoRA SVD Input Parameters:

lora1

This parameter represents the first LoRA model to be merged. It is a required input and serves as the base model for the merging process.

mode

The merging mode to use. This parameter determines how the LoRA models will be combined. The available options are defined by SVD_MODES. The mode you choose can significantly impact the final merged model's characteristics.

density

This parameter controls the density of the merged model. It is a floating-point value ranging from 0 to 1, with a default value of 1.0. A higher density value means more information from the original models will be retained, potentially leading to a more complex but detailed merged model.

svd_rank

The rank for the SVD process. This integer parameter determines the number of singular values to retain during the decomposition. It ranges from 1 to 320, with a default value of 16. A higher rank can capture more details from the original models but may increase the complexity of the merged model.

svd_conv_rank

The convolution rank for the SVD process. Similar to svd_rank, this integer parameter is specifically for convolutional layers. It ranges from 0 to 320, with a default value of 1. Adjusting this rank can help balance the model's performance and complexity.

device

Specifies the device to use for the merging process. The options are cuda and cpu. Using cuda can significantly speed up the process if a compatible GPU is available.

dtype

The data type for the output model. The available options are float32, float16, and bfloat16. Choosing a lower precision data type like float16 can reduce the model size and speed up computations but may slightly affect the model's accuracy.

PM Merge LoRA SVD Output Parameters:

LoRA

The output is a single merged LoRA model. This model combines the features and strengths of the input LoRA models based on the specified parameters and merging mode. The resulting model is ready to be used for further tasks or fine-tuning.

PM Merge LoRA SVD Usage Tips:

• Experiment with different svd_rank and svd_conv_rank values to find the optimal balance between model complexity and performance.
• Use the density parameter to control the amount of information retained from the original models. A higher density can lead to a more detailed merged model.
• If you have access to a GPU, set the device parameter to cuda to speed up the merging process.
• Choose the appropriate dtype based on your needs. For most cases, float32 is a safe choice, but float16 can be useful for reducing model size and computation time.

PM Merge LoRA SVD Common Errors and Solutions:

LoRAs with different ranks not allowed in LoraMerger. Use SVD merge.

• Explanation: This error occurs when the input LoRA models have different ranks, which is not supported by the merging process.
• Solution: Ensure that all input LoRA models have the same rank before attempting to merge them.

Invalid / unsupported mode <mode>

• Explanation: This error indicates that the specified merging mode is not recognized or supported.
• Solution: Verify that the mode parameter is set to a valid option defined by SVD_MODES.

Device not specified or unsupported

• Explanation: This error occurs when the device parameter is not set or is set to an unsupported value.
• Solution: Ensure that the device parameter is set to either cuda or cpu.

Data type not specified or unsupported

• Explanation: This error indicates that the dtype parameter is not set or is set to an unsupported value.
• Solution: Ensure that the dtype parameter is set to one of the supported options: float32, float16, or bfloat16.