lora-training.mdx

title	Native LoRA training
sidebarTitle	LoRA Training
description	Train LoRA models directly in ComfyUI using built-in training nodes

ComfyUI includes native support for training LoRA (Low-Rank Adaptation) models without requiring external tools or custom nodes. This guide covers how to use the built-in training nodes to create your own LoRAs.

The training nodes are marked as **experimental**. Features and behavior may change in future releases.

Overview

The native LoRA training system consists of four nodes:

Node	Category	Purpose
Train LoRA	training	Trains a LoRA model from latents and conditioning
Load LoRA Model	loaders	Applies trained LoRA weights to a model
Save LoRA Weights	loaders	Exports LoRA weights to a safetensors file
Plot Loss Graph	training	Visualizes training loss over time

Requirements

• A GPU with sufficient VRAM (training typically requires more memory than inference)
• Latent images (encoded from your training dataset)
• Text conditioning (captions for your training images)

Basic training workflow

Encode your training images to latents using a VAE Encode node. Create text conditioning for each image using CLIP Text Encode. For best results, use high-quality images that represent the style or subject you want to train. Connect your model, latents, and conditioning to the Train LoRA node. Set the training parameters:

• batch_size: Number of samples per training step (default: 1)
• steps: Total training iterations (default: 16)
• learning_rate: How quickly the model adapts (default: 0.0005)
• rank: LoRA rank - higher values capture more detail but use more memory (default: 8)

Execute the workflow. The node will output: - **lora**: The trained LoRA weights - **loss_map**: Training loss history - **steps**: Total steps completed Connect the output to **Save LoRA Weights** to export your trained LoRA. Use **Load LoRA Model** to apply it during inference.

Train LoRA node

The main training node that creates LoRA weights from your dataset.

Inputs

Parameter	Type	Default	Description
model	MODEL	-	Base model to train the LoRA on
latents	LATENT	-	Encoded training images
positive	CONDITIONING	-	Text conditioning for training
batch_size	INT	1	Samples per step (1-10000)
grad_accumulation_steps	INT	1	Gradient accumulation steps (1-1024)
steps	INT	16	Training iterations (1-100000)
learning_rate	FLOAT	0.0005	Learning rate (0.0000001-1.0)
rank	INT	8	LoRA rank (1-128)
optimizer	COMBO	AdamW	Optimizer: AdamW, Adam, SGD, RMSprop
loss_function	COMBO	MSE	Loss function: MSE, L1, Huber, SmoothL1
seed	INT	0	Random seed for reproducibility
training_dtype	COMBO	bf16	Training precision: bf16, fp32
lora_dtype	COMBO	bf16	LoRA weight precision: bf16, fp32
algorithm	COMBO	lora	Training algorithm (lora, lokr, oft, etc.)
gradient_checkpointing	BOOLEAN	true	Reduces VRAM usage during training
checkpoint_depth	INT	1	Depth level for gradient checkpointing (1-5)
offloading	BOOLEAN	false	Offload model to RAM (requires bypass mode)
existing_lora	COMBO	[None]	Continue training from existing LoRA
bucket_mode	BOOLEAN	false	Enable resolution bucketing for multi-resolution datasets
bypass_mode	BOOLEAN	false	Apply adapters via hooks instead of weight modification

Outputs

Output	Type	Description
lora	LORA_MODEL	Trained LoRA weights
loss_map	LOSS_MAP	Training loss history
steps	INT	Total training steps completed

Load LoRA Model node

Applies trained LoRA weights to a diffusion model. Use this instead of the standard Load LoRA node when working with LoRA weights directly from the Train LoRA node.

Inputs

Parameter	Type	Default	Description
model	MODEL	-	Base diffusion model
lora	LORA_MODEL	-	Trained LoRA weights
strength_model	FLOAT	1.0	LoRA strength (-100 to 100)
bypass	BOOLEAN	false	Apply LoRA without modifying base weights

Output

Output	Type	Description
model	MODEL	Model with LoRA applied

Save LoRA Weights node

Exports trained LoRA weights to a safetensors file in your output folder.

Inputs

Parameter	Type	Default	Description
lora	LORA_MODEL	-	Trained LoRA weights to save
prefix	STRING	loras/ComfyUI_trained_lora	Output filename prefix
steps	INT	(optional)	Training steps for filename

The saved file will be named {prefix}_{steps}_steps_{counter}.safetensors and placed in your ComfyUI/output/loras/ folder.

Plot Loss Graph node

Visualizes training progress by plotting loss values over training steps.

Inputs

Parameter	Type	Default	Description
loss	LOSS_MAP	-	Loss history from Train LoRA
filename_prefix	STRING	loss_graph	Output filename prefix

Training tips

VRAM optimization

• Enable gradient_checkpointing to significantly reduce VRAM usage (enabled by default)
• Use bypass_mode when working with quantized models (FP8)
• Enable offloading to move the model to RAM during training (requires bypass_mode)
• Lower the batch_size if you encounter out-of-memory errors

Dataset preparation

• Use consistent image dimensions when possible, or enable bucket_mode for multi-resolution training
• Match the number of conditioning inputs to the number of latent images
• Quality matters more than quantity—start with 10-20 high-quality images

Training parameters

• rank: Start with 8-16 for most use cases. Higher ranks (32-64) capture more detail but may overfit
• steps: Start with 100-500 steps and monitor the loss graph
• learning_rate: The default 0.0005 works well for most cases. Lower values (0.0001) for more stable training

Continuing training

Select an existing LoRA from the existing_lora dropdown to continue training from a previously saved checkpoint. The total step count will accumulate.

Supported algorithms

The algorithm parameter supports multiple weight adapter types:

• lora: Standard Low-Rank Adaptation (recommended)
• lokr: LoCon with Kronecker product decomposition
• oft: Orthogonal Fine-Tuning

Example: Single-subject LoRA

A minimal workflow for training a LoRA on a specific subject:

1. Load your training images with Load Image
2. Encode images to latents with VAE Encode
3. Create captions with CLIP Text Encode (e.g., "a photo of [subject]")
4. Connect to Train LoRA with:
   • steps: 200
   • rank: 16
   • learning_rate: 0.0001
5. Save with Save LoRA Weights
6. Test with Load LoRA Model connected to your inference workflow

For training on multiple images with different captions, connect multiple conditioning inputs to match your latent batch size.