TrainLoraNode - ComfyUI Built-in Node Documentation

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The TrainLoraNode creates and trains a LoRA (Low-Rank Adaptation) model on a diffusion model using provided latents and conditioning data. It allows you to fine-tune a model with custom training parameters, optimizers, and loss functions. The node outputs the trained model with LoRA applied, the LoRA weights, training loss metrics, and the total training steps completed.

Inputs

Parameter	Data Type	Required	Range	Description
`model`	MODEL	Yes	-	The model to train the LoRA on.
`latents`	LATENT	Yes	-	The Latents to use for training, serve as dataset/input of the model.
`positive`	CONDITIONING	Yes	-	The positive conditioning to use for training.
`batch_size`	INT	Yes	1-10000	The batch size to use for training (default: 1).
`grad_accumulation_steps`	INT	Yes	1-1024	The number of gradient accumulation steps to use for training (default: 1).
`steps`	INT	Yes	1-100000	The number of steps to train the LoRA for (default: 16).
`learning_rate`	FLOAT	Yes	0.0000001-1.0	The learning rate to use for training (default: 0.0005).
`rank`	INT	Yes	1-128	The rank of the LoRA layers (default: 8).
`optimizer`	COMBO	Yes	”AdamW" "Adam" "SGD" "RMSprop”	The optimizer to use for training (default: “AdamW”).
`loss_function`	COMBO	Yes	”MSE" "L1" "Huber" "SmoothL1”	The loss function to use for training (default: “MSE”).
`seed`	INT	Yes	0-18446744073709551615	The seed to use for training (used in generator for LoRA weight initialization and noise sampling) (default: 0).
`training_dtype`	COMBO	Yes	”bf16" "fp32”	The dtype to use for training (default: “bf16”).
`lora_dtype`	COMBO	Yes	”bf16" "fp32”	The dtype to use for lora (default: “bf16”).
`algorithm`	COMBO	Yes	Multiple options available	The algorithm to use for training.
`gradient_checkpointing`	BOOLEAN	Yes	-	Use gradient checkpointing for training (default: True).
`existing_lora`	COMBO	Yes	Multiple options available	The existing LoRA to append to. Set to None for new LoRA (default: “[None]”).

Note: The number of positive conditioning inputs must match the number of latent images. If only one positive conditioning is provided with multiple images, it will be automatically repeated for all images.

Outputs

Output Name	Data Type	Description
`model_with_lora`	MODEL	The original model with the trained LoRA applied.
`lora`	LORA_MODEL	The trained LoRA weights that can be saved or applied to other models.
`loss`	LOSS_MAP	A dictionary containing the training loss values over time.
`steps`	INT	The total number of training steps completed (including any previous steps from existing LoRA).

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Inputs

Outputs